---
_id: '12159'
abstract:
- lang: eng
text: The term “haplotype block” is commonly used in the developing field of haplotype-based
inference methods. We argue that the term should be defined based on the structure
of the Ancestral Recombination Graph (ARG), which contains complete information
on the ancestry of a sample. We use simulated examples to demonstrate key features
of the relationship between haplotype blocks and ancestral structure, emphasizing
the stochasticity of the processes that generate them. Even the simplest cases
of neutrality or of a “hard” selective sweep produce a rich structure, often missed
by commonly used statistics. We highlight a number of novel methods for inferring
haplotype structure, based on the full ARG, or on a sequence of trees, and illustrate
how they can be used to define haplotype blocks using an empirical data set. While
the advent of new, computationally efficient methods makes it possible to apply
these concepts broadly, they (and additional new methods) could benefit from adding
features to explore haplotype blocks, as we define them. Understanding and applying
the concept of the haplotype block will be essential to fully exploit long and
linked-read sequencing technologies.
acknowledgement: 'We thank the Barton group for useful discussion and feedback during
the writing of this article. Comments from Roger Butlin, Molly Schumer''s Group,
the tskit development team, editors and three reviewers greatly improved the manuscript.
Funding was provided by SCAS (Natural Sciences Programme, Knut and Alice Wallenberg
Foundation), an FWF Wittgenstein grant (PT1001Z211), an FWF standalone grant (grant
P 32166), and an ERC Advanced Grant. YFC was supported by the Max Planck Society
and an ERC Proof of Concept Grant #101069216 (HAPLOTAGGING).'
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Daria
full_name: Shipilina, Daria
id: 428A94B0-F248-11E8-B48F-1D18A9856A87
last_name: Shipilina
orcid: 0000-0002-1145-9226
- first_name: Arka
full_name: Pal, Arka
id: 6AAB2240-CA9A-11E9-9C1A-D9D1E5697425
last_name: Pal
orcid: 0000-0002-4530-8469
- first_name: Sean
full_name: Stankowski, Sean
id: 43161670-5719-11EA-8025-FABC3DDC885E
last_name: Stankowski
- first_name: Yingguang Frank
full_name: Chan, Yingguang Frank
last_name: Chan
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
citation:
ama: Shipilina D, Pal A, Stankowski S, Chan YF, Barton NH. On the origin and structure
of haplotype blocks. Molecular Ecology. 2023;32(6):1441-1457. doi:10.1111/mec.16793
apa: Shipilina, D., Pal, A., Stankowski, S., Chan, Y. F., & Barton, N. H. (2023).
On the origin and structure of haplotype blocks. Molecular Ecology. Wiley.
https://doi.org/10.1111/mec.16793
chicago: Shipilina, Daria, Arka Pal, Sean Stankowski, Yingguang Frank Chan, and
Nicholas H Barton. “On the Origin and Structure of Haplotype Blocks.” Molecular
Ecology. Wiley, 2023. https://doi.org/10.1111/mec.16793.
ieee: D. Shipilina, A. Pal, S. Stankowski, Y. F. Chan, and N. H. Barton, “On the
origin and structure of haplotype blocks,” Molecular Ecology, vol. 32,
no. 6. Wiley, pp. 1441–1457, 2023.
ista: Shipilina D, Pal A, Stankowski S, Chan YF, Barton NH. 2023. On the origin
and structure of haplotype blocks. Molecular Ecology. 32(6), 1441–1457.
mla: Shipilina, Daria, et al. “On the Origin and Structure of Haplotype Blocks.”
Molecular Ecology, vol. 32, no. 6, Wiley, 2023, pp. 1441–57, doi:10.1111/mec.16793.
short: D. Shipilina, A. Pal, S. Stankowski, Y.F. Chan, N.H. Barton, Molecular Ecology
32 (2023) 1441–1457.
date_created: 2023-01-12T12:09:17Z
date_published: 2023-03-01T00:00:00Z
date_updated: 2023-08-16T08:18:47Z
day: '01'
ddc:
- '570'
department:
- _id: NiBa
doi: 10.1111/mec.16793
external_id:
isi:
- '000900762000001'
pmid:
- '36433653'
file:
- access_level: open_access
checksum: b10e0f8fa3dc4d72aaf77a557200978a
content_type: application/pdf
creator: dernst
date_created: 2023-08-16T08:15:41Z
date_updated: 2023-08-16T08:15:41Z
file_id: '14062'
file_name: 2023_MolecularEcology_Shipilina.pdf
file_size: 7144607
relation: main_file
success: 1
file_date_updated: 2023-08-16T08:15:41Z
has_accepted_license: '1'
intvolume: ' 32'
isi: 1
issue: '6'
keyword:
- Genetics
- Ecology
- Evolution
- Behavior and Systematics
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
page: 1441-1457
pmid: 1
project:
- _id: 05959E1C-7A3F-11EA-A408-12923DDC885E
grant_number: P32166
name: The maintenance of alternative adaptive peaks in snapdragons
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
- _id: bd6958e0-d553-11ed-ba76-86eba6a76c00
grant_number: '101055327'
name: Understanding the evolution of continuous genomes
publication: Molecular Ecology
publication_identifier:
eissn:
- 1365-294X
issn:
- 0962-1083
publication_status: published
publisher: Wiley
quality_controlled: '1'
scopus_import: '1'
status: public
title: On the origin and structure of haplotype blocks
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 32
year: '2023'
...
---
_id: '12861'
abstract:
- lang: eng
text: The field of indirect reciprocity investigates how social norms can foster
cooperation when individuals continuously monitor and assess each other’s social
interactions. By adhering to certain social norms, cooperating individuals can
improve their reputation and, in turn, receive benefits from others. Eight social
norms, known as the “leading eight," have been shown to effectively promote the
evolution of cooperation as long as information is public and reliable. These
norms categorize group members as either ’good’ or ’bad’. In this study, we examine
a scenario where individuals instead assign nuanced reputation scores to each
other, and only cooperate with those whose reputation exceeds a certain threshold.
We find both analytically and through simulations that such quantitative assessments
are error-correcting, thus facilitating cooperation in situations where information
is private and unreliable. Moreover, our results identify four specific norms
that are robust to such conditions, and may be relevant for helping to sustain
cooperation in natural populations.
acknowledgement: 'This work was supported by the European Research Council CoG 863818
(ForM-SMArt) (to K.C.) and the European Research Council Starting Grant 850529:
E-DIRECT (to C.H.). L.S. received additional partial support by the Austrian Science
Fund (FWF) under grant Z211-N23 (Wittgenstein Award), and also thanks the support
by the Stochastic Analysis and Application Research Center (SAARC) under National
Research Foundation of Korea grant NRF-2019R1A5A1028324. The authors additionally
thank Stefan Schmid for providing access to his lab infrastructure at the University
of Vienna for the purpose of collecting simulation data.'
article_number: '2086'
article_processing_charge: No
article_type: original
author:
- first_name: Laura
full_name: Schmid, Laura
id: 38B437DE-F248-11E8-B48F-1D18A9856A87
last_name: Schmid
orcid: 0000-0002-6978-7329
- first_name: Farbod
full_name: Ekbatani, Farbod
last_name: Ekbatani
- first_name: Christian
full_name: Hilbe, Christian
id: 2FDF8F3C-F248-11E8-B48F-1D18A9856A87
last_name: Hilbe
orcid: 0000-0001-5116-955X
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
citation:
ama: Schmid L, Ekbatani F, Hilbe C, Chatterjee K. Quantitative assessment can stabilize
indirect reciprocity under imperfect information. Nature Communications.
2023;14. doi:10.1038/s41467-023-37817-x
apa: Schmid, L., Ekbatani, F., Hilbe, C., & Chatterjee, K. (2023). Quantitative
assessment can stabilize indirect reciprocity under imperfect information. Nature
Communications. Springer Nature. https://doi.org/10.1038/s41467-023-37817-x
chicago: Schmid, Laura, Farbod Ekbatani, Christian Hilbe, and Krishnendu Chatterjee.
“Quantitative Assessment Can Stabilize Indirect Reciprocity under Imperfect Information.”
Nature Communications. Springer Nature, 2023. https://doi.org/10.1038/s41467-023-37817-x.
ieee: L. Schmid, F. Ekbatani, C. Hilbe, and K. Chatterjee, “Quantitative assessment
can stabilize indirect reciprocity under imperfect information,” Nature Communications,
vol. 14. Springer Nature, 2023.
ista: Schmid L, Ekbatani F, Hilbe C, Chatterjee K. 2023. Quantitative assessment
can stabilize indirect reciprocity under imperfect information. Nature Communications.
14, 2086.
mla: Schmid, Laura, et al. “Quantitative Assessment Can Stabilize Indirect Reciprocity
under Imperfect Information.” Nature Communications, vol. 14, 2086, Springer
Nature, 2023, doi:10.1038/s41467-023-37817-x.
short: L. Schmid, F. Ekbatani, C. Hilbe, K. Chatterjee, Nature Communications 14
(2023).
date_created: 2023-04-23T22:01:03Z
date_published: 2023-04-12T00:00:00Z
date_updated: 2025-07-14T09:09:52Z
day: '12'
ddc:
- '000'
department:
- _id: KrCh
doi: 10.1038/s41467-023-37817-x
ec_funded: 1
external_id:
isi:
- '001003644100020'
pmid:
- '37045828'
file:
- access_level: open_access
checksum: a4b3b7b36fbef068cabf4fb99501fef6
content_type: application/pdf
creator: dernst
date_created: 2023-04-25T09:13:53Z
date_updated: 2023-04-25T09:13:53Z
file_id: '12868'
file_name: 2023_NatureComm_Schmid.pdf
file_size: 1786475
relation: main_file
success: 1
file_date_updated: 2023-04-25T09:13:53Z
has_accepted_license: '1'
intvolume: ' 14'
isi: 1
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
publication: Nature Communications
publication_identifier:
eissn:
- 2041-1723
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Quantitative assessment can stabilize indirect reciprocity under imperfect
information
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 14
year: '2023'
...
---
_id: '10774'
abstract:
- lang: eng
text: We study the problem of specifying sequential information-flow properties
of systems. Information-flow properties are hyperproperties, as they compare different
traces of a system. Sequential information-flow properties can express changes,
over time, in the information-flow constraints. For example, information-flow
constraints during an initialization phase of a system may be different from information-flow
constraints that are required during the operation phase. We formalize several
variants of interpreting sequential information-flow constraints, which arise
from different assumptions about what can be observed of the system. For this
purpose, we introduce a first-order logic, called Hypertrace Logic, with both
trace and time quantifiers for specifying linear-time hyperproperties. We prove
that HyperLTL, which corresponds to a fragment of Hypertrace Logic with restricted
quantifier prefixes, cannot specify the majority of the studied variants of sequential
information flow, including all variants in which the transition between sequential
phases (such as initialization and operation) happens asynchronously. Our results
rely on new equivalences between sets of traces that cannot be distinguished by
certain classes of formulas from Hypertrace Logic. This presents a new approach
to proving inexpressiveness results for HyperLTL.
acknowledgement: This work was funded in part by the Wittgenstein Award Z211-N23 of
the Austrian Science Fund (FWF) and by the FWF project W1255-N23.
alternative_title:
- LNCS
article_processing_charge: No
arxiv: 1
author:
- first_name: Ezio
full_name: Bartocci, Ezio
last_name: Bartocci
- first_name: Thomas
full_name: Ferrere, Thomas
id: 40960E6E-F248-11E8-B48F-1D18A9856A87
last_name: Ferrere
orcid: 0000-0001-5199-3143
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
- first_name: Dejan
full_name: Nickovic, Dejan
id: 41BCEE5C-F248-11E8-B48F-1D18A9856A87
last_name: Nickovic
- first_name: Ana Oliveira
full_name: Da Costa, Ana Oliveira
last_name: Da Costa
citation:
ama: 'Bartocci E, Ferrere T, Henzinger TA, Nickovic D, Da Costa AO. Flavors of sequential
information flow. In: Lecture Notes in Computer Science (Including Subseries
Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
Vol 13182. Springer Nature; 2022:1-19. doi:10.1007/978-3-030-94583-1_1'
apa: 'Bartocci, E., Ferrere, T., Henzinger, T. A., Nickovic, D., & Da Costa,
A. O. (2022). Flavors of sequential information flow. In Lecture Notes in Computer
Science (including subseries Lecture Notes in Artificial Intelligence and Lecture
Notes in Bioinformatics) (Vol. 13182, pp. 1–19). Philadelphia, PA, United
States: Springer Nature. https://doi.org/10.1007/978-3-030-94583-1_1'
chicago: Bartocci, Ezio, Thomas Ferrere, Thomas A Henzinger, Dejan Nickovic, and
Ana Oliveira Da Costa. “Flavors of Sequential Information Flow.” In Lecture
Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence
and Lecture Notes in Bioinformatics), 13182:1–19. Springer Nature, 2022. https://doi.org/10.1007/978-3-030-94583-1_1.
ieee: E. Bartocci, T. Ferrere, T. A. Henzinger, D. Nickovic, and A. O. Da Costa,
“Flavors of sequential information flow,” in Lecture Notes in Computer Science
(including subseries Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics), Philadelphia, PA, United States, 2022, vol. 13182, pp.
1–19.
ista: 'Bartocci E, Ferrere T, Henzinger TA, Nickovic D, Da Costa AO. 2022. Flavors
of sequential information flow. Lecture Notes in Computer Science (including subseries
Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
VMCAI: Verifcation, Model Checking, and Abstract Interpretation, LNCS, vol. 13182,
1–19.'
mla: Bartocci, Ezio, et al. “Flavors of Sequential Information Flow.” Lecture
Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence
and Lecture Notes in Bioinformatics), vol. 13182, Springer Nature, 2022, pp.
1–19, doi:10.1007/978-3-030-94583-1_1.
short: E. Bartocci, T. Ferrere, T.A. Henzinger, D. Nickovic, A.O. Da Costa, in:,
Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial
Intelligence and Lecture Notes in Bioinformatics), Springer Nature, 2022, pp.
1–19.
conference:
end_date: 2022-01-18
location: Philadelphia, PA, United States
name: 'VMCAI: Verifcation, Model Checking, and Abstract Interpretation'
start_date: 2022-01-16
date_created: 2022-02-20T23:01:34Z
date_published: 2022-01-14T00:00:00Z
date_updated: 2022-08-05T09:02:56Z
day: '14'
department:
- _id: ToHe
doi: 10.1007/978-3-030-94583-1_1
external_id:
arxiv:
- '2105.02013'
intvolume: ' 13182'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2105.02013'
month: '01'
oa: 1
oa_version: Preprint
page: 1-19
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
publication: Lecture Notes in Computer Science (including subseries Lecture Notes
in Artificial Intelligence and Lecture Notes in Bioinformatics)
publication_identifier:
eissn:
- '16113349'
isbn:
- '9783030945824'
issn:
- '03029743'
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Flavors of sequential information flow
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13182
year: '2022'
...
---
_id: '10891'
abstract:
- lang: eng
text: We present a formal framework for the online black-box monitoring of software
using monitors with quantitative verdict functions. Quantitative verdict functions
have several advantages. First, quantitative monitors can be approximate, i.e.,
the value of the verdict function does not need to correspond exactly to the value
of the property under observation. Second, quantitative monitors can be quantified
universally, i.e., for every possible observed behavior, the monitor tries to
make the best effort to estimate the value of the property under observation.
Third, quantitative monitors can watch boolean as well as quantitative properties,
such as average response time. Fourth, quantitative monitors can use non-finite-state
resources, such as counters. As a consequence, quantitative monitors can be compared
according to how many resources they use (e.g., the number of counters) and how
precisely they approximate the property under observation. This allows for a rich
spectrum of cost-precision trade-offs in monitoring software.
acknowledgement: The formal framework for quantitative monitoring which is presented
in this invited talk was defined jointly with N. Ege Saraç at LICS 2021. This work
was supported in part by the Wittgenstein Award Z211-N23 of the Austrian Science
Fund.
article_processing_charge: No
author:
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
citation:
ama: 'Henzinger TA. Quantitative monitoring of software. In: Software Verification.
Vol 13124. LNCS. Springer Nature; 2022:3-6. doi:10.1007/978-3-030-95561-8_1'
apa: 'Henzinger, T. A. (2022). Quantitative monitoring of software. In Software
Verification (Vol. 13124, pp. 3–6). New Haven, CT, United States: Springer
Nature. https://doi.org/10.1007/978-3-030-95561-8_1'
chicago: Henzinger, Thomas A. “Quantitative Monitoring of Software.” In Software
Verification, 13124:3–6. LNCS. Springer Nature, 2022. https://doi.org/10.1007/978-3-030-95561-8_1.
ieee: T. A. Henzinger, “Quantitative monitoring of software,” in Software Verification,
New Haven, CT, United States, 2022, vol. 13124, pp. 3–6.
ista: 'Henzinger TA. 2022. Quantitative monitoring of software. Software Verification.
NSV: Numerical Software VerificationLNCS vol. 13124, 3–6.'
mla: Henzinger, Thomas A. “Quantitative Monitoring of Software.” Software Verification,
vol. 13124, Springer Nature, 2022, pp. 3–6, doi:10.1007/978-3-030-95561-8_1.
short: T.A. Henzinger, in:, Software Verification, Springer Nature, 2022, pp. 3–6.
conference:
end_date: 2021-10-19
location: New Haven, CT, United States
name: 'NSV: Numerical Software Verification'
start_date: 2021-10-18
date_created: 2022-03-20T23:01:40Z
date_published: 2022-02-22T00:00:00Z
date_updated: 2023-08-03T06:11:55Z
day: '22'
department:
- _id: ToHe
doi: 10.1007/978-3-030-95561-8_1
external_id:
isi:
- '000771713200001'
intvolume: ' 13124'
isi: 1
language:
- iso: eng
month: '02'
oa_version: None
page: 3-6
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
publication: Software Verification
publication_identifier:
eissn:
- 1611-3349
isbn:
- '9783030955601'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
series_title: LNCS
status: public
title: Quantitative monitoring of software
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 13124
year: '2022'
...
---
_id: '11362'
abstract:
- lang: eng
text: "Deep learning has enabled breakthroughs in challenging computing problems
and has emerged as the standard problem-solving tool for computer vision and natural
language processing tasks.\r\nOne exception to this trend is safety-critical tasks
where robustness and resilience requirements contradict the black-box nature of
neural networks. \r\nTo deploy deep learning methods for these tasks, it is vital
to provide guarantees on neural network agents' safety and robustness criteria.
\r\nThis can be achieved by developing formal verification methods to verify the
safety and robustness properties of neural networks.\r\n\r\nOur goal is to design,
develop and assess safety verification methods for neural networks to improve
their reliability and trustworthiness in real-world applications.\r\nThis thesis
establishes techniques for the verification of compressed and adversarially trained
models as well as the design of novel neural networks for verifiably safe decision-making.\r\n\r\nFirst,
we establish the problem of verifying quantized neural networks. Quantization
is a technique that trades numerical precision for the computational efficiency
of running a neural network and is widely adopted in industry.\r\nWe show that
neglecting the reduced precision when verifying a neural network can lead to wrong
conclusions about the robustness and safety of the network, highlighting that
novel techniques for quantized network verification are necessary. We introduce
several bit-exact verification methods explicitly designed for quantized neural
networks and experimentally confirm on realistic networks that the network's robustness
and other formal properties are affected by the quantization.\r\n\r\nFurthermore,
we perform a case study providing evidence that adversarial training, a standard
technique for making neural networks more robust, has detrimental effects on the
network's performance. This robustness-accuracy tradeoff has been studied before
regarding the accuracy obtained on classification datasets where each data point
is independent of all other data points. On the other hand, we investigate the
tradeoff empirically in robot learning settings where a both, a high accuracy
and a high robustness, are desirable.\r\nOur results suggest that the negative
side-effects of adversarial training outweigh its robustness benefits in practice.\r\n\r\nFinally,
we consider the problem of verifying safety when running a Bayesian neural network
policy in a feedback loop with systems over the infinite time horizon. Bayesian
neural networks are probabilistic models for learning uncertainties in the data
and are therefore often used on robotic and healthcare applications where data
is inherently stochastic.\r\nWe introduce a method for recalibrating Bayesian
neural networks so that they yield probability distributions over safe decisions
only.\r\nOur method learns a safety certificate that guarantees safety over the
infinite time horizon to determine which decisions are safe in every possible
state of the system.\r\nWe demonstrate the effectiveness of our approach on a
series of reinforcement learning benchmarks."
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
citation:
ama: Lechner M. Learning verifiable representations. 2022. doi:10.15479/at:ista:11362
apa: Lechner, M. (2022). Learning verifiable representations. Institute of
Science and Technology Austria. https://doi.org/10.15479/at:ista:11362
chicago: Lechner, Mathias. “Learning Verifiable Representations.” Institute of Science
and Technology Austria, 2022. https://doi.org/10.15479/at:ista:11362.
ieee: M. Lechner, “Learning verifiable representations,” Institute of Science and
Technology Austria, 2022.
ista: Lechner M. 2022. Learning verifiable representations. Institute of Science
and Technology Austria.
mla: Lechner, Mathias. Learning Verifiable Representations. Institute of
Science and Technology Austria, 2022, doi:10.15479/at:ista:11362.
short: M. Lechner, Learning Verifiable Representations, Institute of Science and
Technology Austria, 2022.
date_created: 2022-05-12T07:14:01Z
date_published: 2022-05-12T00:00:00Z
date_updated: 2025-07-14T09:10:11Z
day: '12'
ddc:
- '004'
degree_awarded: PhD
department:
- _id: GradSch
- _id: ToHe
doi: 10.15479/at:ista:11362
ec_funded: 1
file:
- access_level: closed
checksum: 8eefa9c7c10ca7e1a2ccdd731962a645
content_type: application/zip
creator: mlechner
date_created: 2022-05-13T12:33:26Z
date_updated: 2022-05-13T12:49:00Z
file_id: '11378'
file_name: src.zip
file_size: 13210143
relation: source_file
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file_size: 2732536
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file_date_updated: 2022-05-17T15:19:39Z
has_accepted_license: '1'
keyword:
- neural networks
- verification
- machine learning
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nd/4.0/
month: '05'
oa: 1
oa_version: Published Version
page: '124'
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
publication_identifier:
isbn:
- 978-3-99078-017-6
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '11366'
relation: part_of_dissertation
status: public
- id: '7808'
relation: part_of_dissertation
status: public
- id: '10666'
relation: part_of_dissertation
status: public
- id: '10665'
relation: part_of_dissertation
status: public
- id: '10667'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
title: Learning verifiable representations
tmp:
image: /image/cc_by_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nd/4.0/legalcode
name: Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0)
short: CC BY-ND (4.0)
type: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2022'
...
---
_id: '12010'
abstract:
- lang: eng
text: World models learn behaviors in a latent imagination space to enhance the
sample-efficiency of deep reinforcement learning (RL) algorithms. While learning
world models for high-dimensional observations (e.g., pixel inputs) has become
practicable on standard RL benchmarks and some games, their effectiveness in real-world
robotics applications has not been explored. In this paper, we investigate how
such agents generalize to real-world autonomous vehicle control tasks, where advanced
model-free deep RL algorithms fail. In particular, we set up a series of time-lap
tasks for an F1TENTH racing robot, equipped with a high-dimensional LiDAR sensor,
on a set of test tracks with a gradual increase in their complexity. In this continuous-control
setting, we show that model-based agents capable of learning in imagination substantially
outperform model-free agents with respect to performance, sample efficiency, successful
task completion, and generalization. Moreover, we show that the generalization
ability of model-based agents strongly depends on the choice of their observation
model. We provide extensive empirical evidence for the effectiveness of world
models provided with long enough memory horizons in sim2real tasks.
acknowledgement: L.B. was supported by the Doctoral College Resilient Embedded Systems.
M.L. was supported in part by the ERC2020-AdG 101020093 and the Austrian Science
Fund (FWF) under grant Z211-N23 (Wittgenstein Award). R.H. and D.R. were supported
by The Boeing Company and the Office of Naval Research (ONR) Grant N00014-18-1-2830.
R.G. was partially supported by the Horizon-2020 ECSEL Project grant No. 783163
(iDev40) and A.B. by FFG Project ADEX.
article_processing_charge: No
arxiv: 1
author:
- first_name: Axel
full_name: Brunnbauer, Axel
last_name: Brunnbauer
- first_name: Luigi
full_name: Berducci, Luigi
last_name: Berducci
- first_name: Andreas
full_name: Brandstatter, Andreas
last_name: Brandstatter
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Ramin
full_name: Hasani, Ramin
last_name: Hasani
- first_name: Daniela
full_name: Rus, Daniela
last_name: Rus
- first_name: Radu
full_name: Grosu, Radu
last_name: Grosu
citation:
ama: 'Brunnbauer A, Berducci L, Brandstatter A, et al. Latent imagination facilitates
zero-shot transfer in autonomous racing. In: 2022 International Conference
on Robotics and Automation. IEEE; 2022:7513-7520. doi:10.1109/ICRA46639.2022.9811650'
apa: 'Brunnbauer, A., Berducci, L., Brandstatter, A., Lechner, M., Hasani, R., Rus,
D., & Grosu, R. (2022). Latent imagination facilitates zero-shot transfer
in autonomous racing. In 2022 International Conference on Robotics and Automation
(pp. 7513–7520). Philadelphia, PA, United States: IEEE. https://doi.org/10.1109/ICRA46639.2022.9811650'
chicago: Brunnbauer, Axel, Luigi Berducci, Andreas Brandstatter, Mathias Lechner,
Ramin Hasani, Daniela Rus, and Radu Grosu. “Latent Imagination Facilitates Zero-Shot
Transfer in Autonomous Racing.” In 2022 International Conference on Robotics
and Automation, 7513–20. IEEE, 2022. https://doi.org/10.1109/ICRA46639.2022.9811650.
ieee: A. Brunnbauer et al., “Latent imagination facilitates zero-shot transfer
in autonomous racing,” in 2022 International Conference on Robotics and Automation,
Philadelphia, PA, United States, 2022, pp. 7513–7520.
ista: 'Brunnbauer A, Berducci L, Brandstatter A, Lechner M, Hasani R, Rus D, Grosu
R. 2022. Latent imagination facilitates zero-shot transfer in autonomous racing.
2022 International Conference on Robotics and Automation. ICRA: International
Conference on Robotics and Automation, 7513–7520.'
mla: Brunnbauer, Axel, et al. “Latent Imagination Facilitates Zero-Shot Transfer
in Autonomous Racing.” 2022 International Conference on Robotics and Automation,
IEEE, 2022, pp. 7513–20, doi:10.1109/ICRA46639.2022.9811650.
short: A. Brunnbauer, L. Berducci, A. Brandstatter, M. Lechner, R. Hasani, D. Rus,
R. Grosu, in:, 2022 International Conference on Robotics and Automation, IEEE,
2022, pp. 7513–7520.
conference:
end_date: 2022-05-27
location: Philadelphia, PA, United States
name: 'ICRA: International Conference on Robotics and Automation'
start_date: 2022-05-23
date_created: 2022-09-04T22:02:02Z
date_published: 2022-07-12T00:00:00Z
date_updated: 2022-09-05T08:46:12Z
day: '12'
department:
- _id: ToHe
doi: 10.1109/ICRA46639.2022.9811650
ec_funded: 1
external_id:
arxiv:
- '2103.04909'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2103.04909
month: '07'
oa: 1
oa_version: Preprint
page: 7513-7520
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
publication: 2022 International Conference on Robotics and Automation
publication_identifier:
isbn:
- '9781728196817'
issn:
- 1050-4729
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Latent imagination facilitates zero-shot transfer in autonomous racing
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '12147'
abstract:
- lang: eng
text: Continuous-time neural networks are a class of machine learning systems that
can tackle representation learning on spatiotemporal decision-making tasks. These
models are typically represented by continuous differential equations. However,
their expressive power when they are deployed on computers is bottlenecked by
numerical differential equation solvers. This limitation has notably slowed down
the scaling and understanding of numerous natural physical phenomena such as the
dynamics of nervous systems. Ideally, we would circumvent this bottleneck by solving
the given dynamical system in closed form. This is known to be intractable in
general. Here, we show that it is possible to closely approximate the interaction
between neurons and synapses—the building blocks of natural and artificial neural
networks—constructed by liquid time-constant networks efficiently in closed form.
To this end, we compute a tightly bounded approximation of the solution of an
integral appearing in liquid time-constant dynamics that has had no known closed-form
solution so far. This closed-form solution impacts the design of continuous-time
and continuous-depth neural models. For instance, since time appears explicitly
in closed form, the formulation relaxes the need for complex numerical solvers.
Consequently, we obtain models that are between one and five orders of magnitude
faster in training and inference compared with differential equation-based counterparts.
More importantly, in contrast to ordinary differential equation-based continuous
networks, closed-form networks can scale remarkably well compared with other deep
learning instances. Lastly, as these models are derived from liquid networks,
they show good performance in time-series modelling compared with advanced recurrent
neural network models.
acknowledgement: This research was supported in part by the AI2050 program at Schmidt
Futures (grant G-22-63172), the Boeing Company, and the United States Air Force
Research Laboratory and the United States Air Force Artificial Intelligence Accelerator
and was accomplished under cooperative agreement number FA8750-19-2-1000. The views
and conclusions contained in this document are those of the authors and should not
be interpreted as representing the official policies, either expressed or implied,
of the United States Air Force or the U.S. Government. The U.S. Government is authorized
to reproduce and distribute reprints for Government purposes, notwithstanding any
copyright notation herein. This work was further supported by The Boeing Company
and Office of Naval Research grant N00014-18-1-2830. M.T. is supported by the Poul
Due Jensen Foundation, grant 883901. M.L. was supported in part by the Austrian
Science Fund under grant Z211-N23 (Wittgenstein Award). A.A. was supported by the
National Science Foundation Graduate Research Fellowship Program. We thank T.-H.
Wang, P. Kao, M. Chahine, W. Xiao, X. Li, L. Yin and Y. Ben for useful suggestions
and for testing of CfC models to confirm the results across other domains.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Ramin
full_name: Hasani, Ramin
last_name: Hasani
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Alexander
full_name: Amini, Alexander
last_name: Amini
- first_name: Lucas
full_name: Liebenwein, Lucas
last_name: Liebenwein
- first_name: Aaron
full_name: Ray, Aaron
last_name: Ray
- first_name: Max
full_name: Tschaikowski, Max
last_name: Tschaikowski
- first_name: Gerald
full_name: Teschl, Gerald
last_name: Teschl
- first_name: Daniela
full_name: Rus, Daniela
last_name: Rus
citation:
ama: Hasani R, Lechner M, Amini A, et al. Closed-form continuous-time neural networks.
Nature Machine Intelligence. 2022;4(11):992-1003. doi:10.1038/s42256-022-00556-7
apa: Hasani, R., Lechner, M., Amini, A., Liebenwein, L., Ray, A., Tschaikowski,
M., … Rus, D. (2022). Closed-form continuous-time neural networks. Nature Machine
Intelligence. Springer Nature. https://doi.org/10.1038/s42256-022-00556-7
chicago: Hasani, Ramin, Mathias Lechner, Alexander Amini, Lucas Liebenwein, Aaron
Ray, Max Tschaikowski, Gerald Teschl, and Daniela Rus. “Closed-Form Continuous-Time
Neural Networks.” Nature Machine Intelligence. Springer Nature, 2022. https://doi.org/10.1038/s42256-022-00556-7.
ieee: R. Hasani et al., “Closed-form continuous-time neural networks,” Nature
Machine Intelligence, vol. 4, no. 11. Springer Nature, pp. 992–1003, 2022.
ista: Hasani R, Lechner M, Amini A, Liebenwein L, Ray A, Tschaikowski M, Teschl
G, Rus D. 2022. Closed-form continuous-time neural networks. Nature Machine Intelligence.
4(11), 992–1003.
mla: Hasani, Ramin, et al. “Closed-Form Continuous-Time Neural Networks.” Nature
Machine Intelligence, vol. 4, no. 11, Springer Nature, 2022, pp. 992–1003,
doi:10.1038/s42256-022-00556-7.
short: R. Hasani, M. Lechner, A. Amini, L. Liebenwein, A. Ray, M. Tschaikowski,
G. Teschl, D. Rus, Nature Machine Intelligence 4 (2022) 992–1003.
date_created: 2023-01-12T12:07:21Z
date_published: 2022-11-15T00:00:00Z
date_updated: 2023-08-04T09:00:10Z
day: '15'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.1038/s42256-022-00556-7
external_id:
arxiv:
- '2106.13898'
isi:
- '000884215600003'
file:
- access_level: open_access
checksum: b4789122ce04bfb4ac042390f59aaa8b
content_type: application/pdf
creator: dernst
date_created: 2023-01-24T09:49:44Z
date_updated: 2023-01-24T09:49:44Z
file_id: '12355'
file_name: 2022_NatureMachineIntelligence_Hasani.pdf
file_size: 3259553
relation: main_file
success: 1
file_date_updated: 2023-01-24T09:49:44Z
has_accepted_license: '1'
intvolume: ' 4'
isi: 1
issue: '11'
keyword:
- Artificial Intelligence
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Human-Computer Interaction
- Software
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
page: 992-1003
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
publication: Nature Machine Intelligence
publication_identifier:
issn:
- 2522-5839
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- relation: erratum
url: https://doi.org/10.1038/s42256-022-00597-y
scopus_import: '1'
status: public
title: Closed-form continuous-time neural networks
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 4
year: '2022'
...
---
_id: '12510'
abstract:
- lang: eng
text: "We introduce a new statistical verification algorithm that formally quantifies
the behavioral robustness of any time-continuous process formulated as a continuous-depth
model. Our algorithm solves a set of global optimization (Go) problems over a
given time horizon to construct a tight enclosure (Tube) of the set of all process
executions starting from a ball of initial states. We call our algorithm GoTube.
Through its construction, GoTube ensures that the bounding tube is conservative
up to a desired probability and up to a desired tightness.\r\n GoTube is implemented
in JAX and optimized to scale to complex continuous-depth neural network models.
Compared to advanced reachability analysis tools for time-continuous neural networks,
GoTube does not accumulate overapproximation errors between time steps and avoids
the infamous wrapping effect inherent in symbolic techniques. We show that GoTube
substantially outperforms state-of-the-art verification tools in terms of the
size of the initial ball, speed, time-horizon, task completion, and scalability
on a large set of experiments.\r\n GoTube is stable and sets the state-of-the-art
in terms of its ability to scale to time horizons well beyond what has been previously
possible."
acknowledgement: SG is funded by the Austrian Science Fund (FWF) project number W1255-N23.
ML and TH are supported in part by FWF under grant Z211-N23 (Wittgenstein Award)
and the ERC-2020-AdG 101020093. SS is supported by NSF awards DCL-2040599, CCF-1918225,
and CPS-1446832. RH and DR are partially supported by Boeing. RG is partially supported
by Horizon-2020 ECSEL Project grant No. 783163 (iDev40).
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Sophie A.
full_name: Gruenbacher, Sophie A.
last_name: Gruenbacher
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Ramin
full_name: Hasani, Ramin
last_name: Hasani
- first_name: Daniela
full_name: Rus, Daniela
last_name: Rus
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
- first_name: Scott A.
full_name: Smolka, Scott A.
last_name: Smolka
- first_name: Radu
full_name: Grosu, Radu
last_name: Grosu
citation:
ama: 'Gruenbacher SA, Lechner M, Hasani R, et al. GoTube: Scalable statistical verification
of continuous-depth models. Proceedings of the AAAI Conference on Artificial
Intelligence. 2022;36(6):6755-6764. doi:10.1609/aaai.v36i6.20631'
apa: 'Gruenbacher, S. A., Lechner, M., Hasani, R., Rus, D., Henzinger, T. A., Smolka,
S. A., & Grosu, R. (2022). GoTube: Scalable statistical verification of continuous-depth
models. Proceedings of the AAAI Conference on Artificial Intelligence.
Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v36i6.20631'
chicago: 'Gruenbacher, Sophie A., Mathias Lechner, Ramin Hasani, Daniela Rus, Thomas
A Henzinger, Scott A. Smolka, and Radu Grosu. “GoTube: Scalable Statistical Verification
of Continuous-Depth Models.” Proceedings of the AAAI Conference on Artificial
Intelligence. Association for the Advancement of Artificial Intelligence,
2022. https://doi.org/10.1609/aaai.v36i6.20631.'
ieee: 'S. A. Gruenbacher et al., “GoTube: Scalable statistical verification
of continuous-depth models,” Proceedings of the AAAI Conference on Artificial
Intelligence, vol. 36, no. 6. Association for the Advancement of Artificial
Intelligence, pp. 6755–6764, 2022.'
ista: 'Gruenbacher SA, Lechner M, Hasani R, Rus D, Henzinger TA, Smolka SA, Grosu
R. 2022. GoTube: Scalable statistical verification of continuous-depth models.
Proceedings of the AAAI Conference on Artificial Intelligence. 36(6), 6755–6764.'
mla: 'Gruenbacher, Sophie A., et al. “GoTube: Scalable Statistical Verification
of Continuous-Depth Models.” Proceedings of the AAAI Conference on Artificial
Intelligence, vol. 36, no. 6, Association for the Advancement of Artificial
Intelligence, 2022, pp. 6755–64, doi:10.1609/aaai.v36i6.20631.'
short: S.A. Gruenbacher, M. Lechner, R. Hasani, D. Rus, T.A. Henzinger, S.A. Smolka,
R. Grosu, Proceedings of the AAAI Conference on Artificial Intelligence 36 (2022)
6755–6764.
date_created: 2023-02-05T17:27:42Z
date_published: 2022-06-28T00:00:00Z
date_updated: 2023-09-26T10:46:59Z
day: '28'
department:
- _id: ToHe
doi: 10.1609/aaai.v36i6.20631
ec_funded: 1
external_id:
arxiv:
- '2107.08467'
intvolume: ' 36'
issue: '6'
keyword:
- General Medicine
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2107.08467
month: '06'
oa: 1
oa_version: Preprint
page: 6755-6764
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
publication: Proceedings of the AAAI Conference on Artificial Intelligence
publication_identifier:
eissn:
- 2374-3468
isbn:
- '978577358350'
issn:
- 2159-5399
publication_status: published
publisher: Association for the Advancement of Artificial Intelligence
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'GoTube: Scalable statistical verification of continuous-depth models'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 36
year: '2022'
...
---
_id: '10665'
abstract:
- lang: eng
text: "Formal verification of neural networks is an active topic of research, and
recent advances have significantly increased the size of the networks that verification
tools can handle. However, most methods are designed for verification of an idealized
model of the actual network which works over real arithmetic and ignores rounding
imprecisions. This idealization is in stark contrast to network quantization,
which is a technique that trades numerical precision for computational efficiency
and is, therefore, often applied in practice. Neglecting rounding errors of such
low-bit quantized neural networks has been shown to lead to wrong conclusions
about the network’s correctness. Thus, the desired approach for verifying quantized
neural networks would be one that takes these rounding errors\r\ninto account.
In this paper, we show that verifying the bitexact implementation of quantized
neural networks with bitvector specifications is PSPACE-hard, even though verifying
idealized real-valued networks and satisfiability of bit-vector specifications
alone are each in NP. Furthermore, we explore several practical heuristics toward
closing the complexity gap between idealized and bit-exact verification. In particular,
we propose three techniques for making SMT-based verification of quantized neural
networks more scalable. Our experiments demonstrate that our proposed methods
allow a speedup of up to three orders of magnitude over existing approaches."
acknowledgement: "This research was supported in part by the Austrian Science Fund
(FWF) under grant Z211-N23 (Wittgenstein\r\nAward), ERC CoG 863818 (FoRM-SMArt),
and the European Union’s Horizon 2020 research and innovation programme under the
Marie Skłodowska-Curie Grant Agreement No. 665385.\r\n"
alternative_title:
- Technical Tracks
article_processing_charge: No
arxiv: 1
author:
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Dorde
full_name: Zikelic, Dorde
id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
last_name: Zikelic
orcid: 0000-0002-4681-1699
citation:
ama: 'Henzinger TA, Lechner M, Zikelic D. Scalable verification of quantized neural
networks. In: Proceedings of the AAAI Conference on Artificial Intelligence.
Vol 35. AAAI Press; 2021:3787-3795.'
apa: 'Henzinger, T. A., Lechner, M., & Zikelic, D. (2021). Scalable verification
of quantized neural networks. In Proceedings of the AAAI Conference on Artificial
Intelligence (Vol. 35, pp. 3787–3795). Virtual: AAAI Press.'
chicago: Henzinger, Thomas A, Mathias Lechner, and Dorde Zikelic. “Scalable Verification
of Quantized Neural Networks.” In Proceedings of the AAAI Conference on Artificial
Intelligence, 35:3787–95. AAAI Press, 2021.
ieee: T. A. Henzinger, M. Lechner, and D. Zikelic, “Scalable verification of quantized
neural networks,” in Proceedings of the AAAI Conference on Artificial Intelligence,
Virtual, 2021, vol. 35, no. 5A, pp. 3787–3795.
ista: 'Henzinger TA, Lechner M, Zikelic D. 2021. Scalable verification of quantized
neural networks. Proceedings of the AAAI Conference on Artificial Intelligence.
AAAI: Association for the Advancement of Artificial Intelligence, Technical Tracks,
vol. 35, 3787–3795.'
mla: Henzinger, Thomas A., et al. “Scalable Verification of Quantized Neural Networks.”
Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35,
no. 5A, AAAI Press, 2021, pp. 3787–95.
short: T.A. Henzinger, M. Lechner, D. Zikelic, in:, Proceedings of the AAAI Conference
on Artificial Intelligence, AAAI Press, 2021, pp. 3787–3795.
conference:
end_date: 2021-02-09
location: Virtual
name: 'AAAI: Association for the Advancement of Artificial Intelligence'
start_date: 2021-02-02
date_created: 2022-01-25T15:15:02Z
date_published: 2021-05-28T00:00:00Z
date_updated: 2025-07-14T09:10:11Z
day: '28'
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
ec_funded: 1
external_id:
arxiv:
- '2012.08185'
file:
- access_level: open_access
checksum: 2bc8155b2526a70fba5b7301bc89dbd1
content_type: application/pdf
creator: mlechner
date_created: 2022-01-26T07:41:16Z
date_updated: 2022-01-26T07:41:16Z
file_id: '10684'
file_name: 16496-Article Text-19990-1-2-20210518 (1).pdf
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url: https://ojs.aaai.org/index.php/AAAI/article/view/16496
month: '05'
oa: 1
oa_version: Published Version
page: 3787-3795
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
publication: Proceedings of the AAAI Conference on Artificial Intelligence
publication_identifier:
eissn:
- 2374-3468
isbn:
- 978-1-57735-866-4
issn:
- 2159-5399
publication_status: published
publisher: AAAI Press
quality_controlled: '1'
related_material:
record:
- id: '11362'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Scalable verification of quantized neural networks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 35
year: '2021'
...
---
_id: '10666'
abstract:
- lang: eng
text: Adversarial training is an effective method to train deep learning models
that are resilient to norm-bounded perturbations, with the cost of nominal performance
drop. While adversarial training appears to enhance the robustness and safety
of a deep model deployed in open-world decision-critical applications, counterintuitively,
it induces undesired behaviors in robot learning settings. In this paper, we show
theoretically and experimentally that neural controllers obtained via adversarial
training are subjected to three types of defects, namely transient, systematic,
and conditional errors. We first generalize adversarial training to a safety-domain
optimization scheme allowing for more generic specifications. We then prove that
such a learning process tends to cause certain error profiles. We support our
theoretical results by a thorough experimental safety analysis in a robot-learning
task. Our results suggest that adversarial training is not yet ready for robot
learning.
acknowledgement: M.L. and T.A.H. are supported in part by the Austrian Science Fund
(FWF) under grant Z211-N23 (Wittgenstein Award). R.H. and D.R. are supported by
Boeing and R.G. by Horizon-2020 ECSEL Project grant no. 783163 (iDev40).
article_processing_charge: No
arxiv: 1
author:
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Ramin
full_name: Hasani, Ramin
last_name: Hasani
- first_name: Radu
full_name: Grosu, Radu
last_name: Grosu
- first_name: Daniela
full_name: Rus, Daniela
last_name: Rus
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
citation:
ama: 'Lechner M, Hasani R, Grosu R, Rus D, Henzinger TA. Adversarial training is
not ready for robot learning. In: 2021 IEEE International Conference on Robotics
and Automation. ICRA. ; 2021:4140-4147. doi:10.1109/ICRA48506.2021.9561036'
apa: Lechner, M., Hasani, R., Grosu, R., Rus, D., & Henzinger, T. A. (2021).
Adversarial training is not ready for robot learning. In 2021 IEEE International
Conference on Robotics and Automation (pp. 4140–4147). Xi’an, China. https://doi.org/10.1109/ICRA48506.2021.9561036
chicago: Lechner, Mathias, Ramin Hasani, Radu Grosu, Daniela Rus, and Thomas A Henzinger.
“Adversarial Training Is Not Ready for Robot Learning.” In 2021 IEEE International
Conference on Robotics and Automation, 4140–47. ICRA, 2021. https://doi.org/10.1109/ICRA48506.2021.9561036.
ieee: M. Lechner, R. Hasani, R. Grosu, D. Rus, and T. A. Henzinger, “Adversarial
training is not ready for robot learning,” in 2021 IEEE International Conference
on Robotics and Automation, Xi’an, China, 2021, pp. 4140–4147.
ista: 'Lechner M, Hasani R, Grosu R, Rus D, Henzinger TA. 2021. Adversarial training
is not ready for robot learning. 2021 IEEE International Conference on Robotics
and Automation. ICRA: International Conference on Robotics and AutomationICRA,
4140–4147.'
mla: Lechner, Mathias, et al. “Adversarial Training Is Not Ready for Robot Learning.”
2021 IEEE International Conference on Robotics and Automation, 2021, pp.
4140–47, doi:10.1109/ICRA48506.2021.9561036.
short: M. Lechner, R. Hasani, R. Grosu, D. Rus, T.A. Henzinger, in:, 2021 IEEE International
Conference on Robotics and Automation, 2021, pp. 4140–4147.
conference:
end_date: 2021-06-05
location: Xi'an, China
name: 'ICRA: International Conference on Robotics and Automation'
start_date: 2021-05-30
date_created: 2022-01-25T15:44:54Z
date_published: 2021-01-01T00:00:00Z
date_updated: 2023-08-17T06:58:38Z
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
doi: 10.1109/ICRA48506.2021.9561036
external_id:
arxiv:
- '2103.08187'
isi:
- '000765738803040'
has_accepted_license: '1'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2103.08187
oa: 1
oa_version: None
page: 4140-4147
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
publication: 2021 IEEE International Conference on Robotics and Automation
publication_identifier:
eisbn:
- 978-1-7281-9077-8
eissn:
- 2577-087X
isbn:
- 978-1-7281-9078-5
issn:
- 1050-4729
publication_status: published
quality_controlled: '1'
related_material:
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relation: dissertation_contains
status: public
series_title: ICRA
status: public
title: Adversarial training is not ready for robot learning
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/3.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND
3.0)
short: CC BY-NC-ND (3.0)
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
year: '2021'
...
---
_id: '10667'
abstract:
- lang: eng
text: Bayesian neural networks (BNNs) place distributions over the weights of a
neural network to model uncertainty in the data and the network's prediction.
We consider the problem of verifying safety when running a Bayesian neural network
policy in a feedback loop with infinite time horizon systems. Compared to the
existing sampling-based approaches, which are inapplicable to the infinite time
horizon setting, we train a separate deterministic neural network that serves
as an infinite time horizon safety certificate. In particular, we show that the
certificate network guarantees the safety of the system over a subset of the BNN
weight posterior's support. Our method first computes a safe weight set and then
alters the BNN's weight posterior to reject samples outside this set. Moreover,
we show how to extend our approach to a safe-exploration reinforcement learning
setting, in order to avoid unsafe trajectories during the training of the policy.
We evaluate our approach on a series of reinforcement learning benchmarks, including
non-Lyapunovian safety specifications.
acknowledgement: This research was supported in part by the Austrian Science Fund
(FWF) under grant Z211-N23 (Wittgenstein Award), ERC CoG 863818 (FoRM-SMArt), and
the European Union’s Horizon 2020 research and innovation programme under the Marie
Skłodowska-Curie Grant Agreement No. 665385.
alternative_title:
- ' Advances in Neural Information Processing Systems'
article_processing_charge: No
arxiv: 1
author:
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Ðorđe
full_name: Žikelić, Ðorđe
last_name: Žikelić
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
citation:
ama: 'Lechner M, Žikelić Ð, Chatterjee K, Henzinger TA. Infinite time horizon safety
of Bayesian neural networks. In: 35th Conference on Neural Information Processing
Systems. ; 2021. doi:10.48550/arXiv.2111.03165'
apa: Lechner, M., Žikelić, Ð., Chatterjee, K., & Henzinger, T. A. (2021). Infinite
time horizon safety of Bayesian neural networks. In 35th Conference on Neural
Information Processing Systems. Virtual. https://doi.org/10.48550/arXiv.2111.03165
chicago: Lechner, Mathias, Ðorđe Žikelić, Krishnendu Chatterjee, and Thomas A Henzinger.
“Infinite Time Horizon Safety of Bayesian Neural Networks.” In 35th Conference
on Neural Information Processing Systems, 2021. https://doi.org/10.48550/arXiv.2111.03165.
ieee: M. Lechner, Ð. Žikelić, K. Chatterjee, and T. A. Henzinger, “Infinite time
horizon safety of Bayesian neural networks,” in 35th Conference on Neural Information
Processing Systems, Virtual, 2021.
ista: 'Lechner M, Žikelić Ð, Chatterjee K, Henzinger TA. 2021. Infinite time horizon
safety of Bayesian neural networks. 35th Conference on Neural Information Processing
Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information
Processing Systems, .'
mla: Lechner, Mathias, et al. “Infinite Time Horizon Safety of Bayesian Neural Networks.”
35th Conference on Neural Information Processing Systems, 2021, doi:10.48550/arXiv.2111.03165.
short: M. Lechner, Ð. Žikelić, K. Chatterjee, T.A. Henzinger, in:, 35th Conference
on Neural Information Processing Systems, 2021.
conference:
end_date: 2021-12-10
location: Virtual
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2021-12-06
date_created: 2022-01-25T15:45:58Z
date_published: 2021-12-01T00:00:00Z
date_updated: 2025-07-14T09:10:12Z
day: '01'
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
- _id: KrCh
doi: 10.48550/arXiv.2111.03165
ec_funded: 1
external_id:
arxiv:
- '2111.03165'
file:
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checksum: 0fc0f852525c10dda9cc9ffea07fb4e4
content_type: application/pdf
creator: mlechner
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date_updated: 2022-01-26T07:39:59Z
file_id: '10682'
file_name: infinite_time_horizon_safety_o.pdf
file_size: 452492
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success: 1
file_date_updated: 2022-01-26T07:39:59Z
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language:
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main_file_link:
- open_access: '1'
url: https://proceedings.neurips.cc/paper/2021/hash/544defa9fddff50c53b71c43e0da72be-Abstract.html
month: '12'
oa: 1
oa_version: Published Version
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
publication: 35th Conference on Neural Information Processing Systems
publication_status: published
quality_controlled: '1'
related_material:
record:
- id: '11362'
relation: dissertation_contains
status: public
status: public
title: Infinite time horizon safety of Bayesian neural networks
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/3.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND
3.0)
short: CC BY-NC-ND (3.0)
type: conference
user_id: 2EBD1598-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '10668'
abstract:
- lang: eng
text: 'Robustness to variations in lighting conditions is a key objective for any
deep vision system. To this end, our paper extends the receptive field of convolutional
neural networks with two residual components, ubiquitous in the visual processing
system of vertebrates: On-center and off-center pathways, with an excitatory center
and inhibitory surround; OOCS for short. The On-center pathway is excited by the
presence of a light stimulus in its center, but not in its surround, whereas the
Off-center pathway is excited by the absence of a light stimulus in its center,
but not in its surround. We design OOCS pathways via a difference of Gaussians,
with their variance computed analytically from the size of the receptive fields.
OOCS pathways complement each other in their response to light stimuli, ensuring
this way a strong edge-detection capability, and as a result an accurate and robust
inference under challenging lighting conditions. We provide extensive empirical
evidence showing that networks supplied with OOCS pathways gain accuracy and illumination-robustness
from the novel edge representation, compared to other baselines.'
acknowledgement: Z.B. is supported by the Doctoral College Resilient Embedded Systems,
which is run jointly by the TU Wien’s Faculty of Informatics and the UAS Technikum
Wien. R.G. is partially supported by the Horizon 2020 Era-Permed project Persorad,
and ECSEL Project grant no. 783163 (iDev40). R.H and D.R were partially supported
by Boeing and MIT. M.L. is supported in part by the Austrian Science Fund (FWF)
under grant Z211-N23 (Wittgenstein Award).
alternative_title:
- PMLR
article_processing_charge: No
author:
- first_name: Zahra
full_name: Babaiee, Zahra
last_name: Babaiee
- first_name: Ramin
full_name: Hasani, Ramin
last_name: Hasani
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Daniela
full_name: Rus, Daniela
last_name: Rus
- first_name: Radu
full_name: Grosu, Radu
last_name: Grosu
citation:
ama: 'Babaiee Z, Hasani R, Lechner M, Rus D, Grosu R. On-off center-surround receptive
fields for accurate and robust image classification. In: Proceedings of the
38th International Conference on Machine Learning. Vol 139. ML Research Press;
2021:478-489.'
apa: 'Babaiee, Z., Hasani, R., Lechner, M., Rus, D., & Grosu, R. (2021). On-off
center-surround receptive fields for accurate and robust image classification.
In Proceedings of the 38th International Conference on Machine Learning
(Vol. 139, pp. 478–489). Virtual: ML Research Press.'
chicago: Babaiee, Zahra, Ramin Hasani, Mathias Lechner, Daniela Rus, and Radu Grosu.
“On-off Center-Surround Receptive Fields for Accurate and Robust Image Classification.”
In Proceedings of the 38th International Conference on Machine Learning,
139:478–89. ML Research Press, 2021.
ieee: Z. Babaiee, R. Hasani, M. Lechner, D. Rus, and R. Grosu, “On-off center-surround
receptive fields for accurate and robust image classification,” in Proceedings
of the 38th International Conference on Machine Learning, Virtual, 2021, vol.
139, pp. 478–489.
ista: 'Babaiee Z, Hasani R, Lechner M, Rus D, Grosu R. 2021. On-off center-surround
receptive fields for accurate and robust image classification. Proceedings of
the 38th International Conference on Machine Learning. ML: Machine Learning, PMLR,
vol. 139, 478–489.'
mla: Babaiee, Zahra, et al. “On-off Center-Surround Receptive Fields for Accurate
and Robust Image Classification.” Proceedings of the 38th International Conference
on Machine Learning, vol. 139, ML Research Press, 2021, pp. 478–89.
short: Z. Babaiee, R. Hasani, M. Lechner, D. Rus, R. Grosu, in:, Proceedings of
the 38th International Conference on Machine Learning, ML Research Press, 2021,
pp. 478–489.
conference:
end_date: 2021-07-24
location: Virtual
name: 'ML: Machine Learning'
start_date: 2021-07-18
date_created: 2022-01-25T15:46:33Z
date_published: 2021-07-01T00:00:00Z
date_updated: 2022-05-04T15:02:27Z
day: '01'
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
file:
- access_level: open_access
checksum: d30eae62561bb517d9f978437d7677db
content_type: application/pdf
creator: mlechner
date_created: 2022-01-26T07:38:32Z
date_updated: 2022-01-26T07:38:32Z
file_id: '10681'
file_name: babaiee21a.pdf
file_size: 4246561
relation: main_file
success: 1
file_date_updated: 2022-01-26T07:38:32Z
has_accepted_license: '1'
intvolume: ' 139'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://proceedings.mlr.press/v139/babaiee21a
month: '07'
oa: 1
oa_version: Published Version
page: 478-489
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
publication: Proceedings of the 38th International Conference on Machine Learning
publication_identifier:
issn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
status: public
title: On-off center-surround receptive fields for accurate and robust image classification
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/3.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND
3.0)
short: CC BY-NC-ND (3.0)
type: conference
user_id: 2EBD1598-F248-11E8-B48F-1D18A9856A87
volume: 139
year: '2021'
...
---
_id: '10669'
abstract:
- lang: eng
text: "We show that Neural ODEs, an emerging class of timecontinuous neural networks,
can be verified by solving a set of global-optimization problems. For this purpose,
we introduce Stochastic Lagrangian Reachability (SLR), an\r\nabstraction-based
technique for constructing a tight Reachtube (an over-approximation of the set
of reachable states\r\nover a given time-horizon), and provide stochastic guarantees
in the form of confidence intervals for the Reachtube bounds. SLR inherently avoids
the infamous wrapping effect (accumulation of over-approximation errors) by performing
local optimization steps to expand safe regions instead of repeatedly forward-propagating
them as is done by deterministic reachability methods. To enable fast local optimizations,
we introduce a novel forward-mode adjoint sensitivity method to compute gradients
without the need for backpropagation. Finally, we establish asymptotic and non-asymptotic
convergence rates for SLR."
acknowledgement: "The authors would like to thank the reviewers for their insightful
comments. RH and RG were partially supported by\r\nHorizon-2020 ECSEL Project grant
No. 783163 (iDev40). RH was partially supported by Boeing. ML was supported\r\nin
part by the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein Award).
SG was funded by FWF\r\nproject W1255-N23. JC was partially supported by NAWA Polish
Returns grant PPN/PPO/2018/1/00029. SS was supported by NSF awards DCL-2040599,
CCF-1918225, and CPS-1446832.\r\n"
alternative_title:
- Technical Tracks
article_processing_charge: No
arxiv: 1
author:
- first_name: Sophie
full_name: Grunbacher, Sophie
last_name: Grunbacher
- first_name: Ramin
full_name: Hasani, Ramin
last_name: Hasani
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Jacek
full_name: Cyranka, Jacek
last_name: Cyranka
- first_name: Scott A
full_name: Smolka, Scott A
last_name: Smolka
- first_name: Radu
full_name: Grosu, Radu
last_name: Grosu
citation:
ama: 'Grunbacher S, Hasani R, Lechner M, Cyranka J, Smolka SA, Grosu R. On the verification
of neural ODEs with stochastic guarantees. In: Proceedings of the AAAI Conference
on Artificial Intelligence. Vol 35. AAAI Press; 2021:11525-11535.'
apa: 'Grunbacher, S., Hasani, R., Lechner, M., Cyranka, J., Smolka, S. A., &
Grosu, R. (2021). On the verification of neural ODEs with stochastic guarantees.
In Proceedings of the AAAI Conference on Artificial Intelligence (Vol.
35, pp. 11525–11535). Virtual: AAAI Press.'
chicago: Grunbacher, Sophie, Ramin Hasani, Mathias Lechner, Jacek Cyranka, Scott
A Smolka, and Radu Grosu. “On the Verification of Neural ODEs with Stochastic
Guarantees.” In Proceedings of the AAAI Conference on Artificial Intelligence,
35:11525–35. AAAI Press, 2021.
ieee: S. Grunbacher, R. Hasani, M. Lechner, J. Cyranka, S. A. Smolka, and R. Grosu,
“On the verification of neural ODEs with stochastic guarantees,” in Proceedings
of the AAAI Conference on Artificial Intelligence, Virtual, 2021, vol. 35,
no. 13, pp. 11525–11535.
ista: 'Grunbacher S, Hasani R, Lechner M, Cyranka J, Smolka SA, Grosu R. 2021. On
the verification of neural ODEs with stochastic guarantees. Proceedings of the
AAAI Conference on Artificial Intelligence. AAAI: Association for the Advancement
of Artificial Intelligence, Technical Tracks, vol. 35, 11525–11535.'
mla: Grunbacher, Sophie, et al. “On the Verification of Neural ODEs with Stochastic
Guarantees.” Proceedings of the AAAI Conference on Artificial Intelligence,
vol. 35, no. 13, AAAI Press, 2021, pp. 11525–35.
short: S. Grunbacher, R. Hasani, M. Lechner, J. Cyranka, S.A. Smolka, R. Grosu,
in:, Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Press,
2021, pp. 11525–11535.
conference:
end_date: 2021-02-09
location: Virtual
name: 'AAAI: Association for the Advancement of Artificial Intelligence'
start_date: 2021-02-02
date_created: 2022-01-25T15:47:20Z
date_published: 2021-05-28T00:00:00Z
date_updated: 2022-05-24T06:33:14Z
day: '28'
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
external_id:
arxiv:
- '2012.08863'
file:
- access_level: open_access
checksum: 468d07041e282a1d46ffdae92f709630
content_type: application/pdf
creator: mlechner
date_created: 2022-01-26T07:38:08Z
date_updated: 2022-01-26T07:38:08Z
file_id: '10680'
file_name: 17372-Article Text-20866-1-2-20210518.pdf
file_size: 286906
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success: 1
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has_accepted_license: '1'
intvolume: ' 35'
issue: '13'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://ojs.aaai.org/index.php/AAAI/article/view/17372
month: '05'
oa: 1
oa_version: Published Version
page: 11525-11535
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
publication: Proceedings of the AAAI Conference on Artificial Intelligence
publication_identifier:
eissn:
- 2374-3468
isbn:
- 978-1-57735-866-4
issn:
- 2159-5399
publication_status: published
publisher: AAAI Press
quality_controlled: '1'
status: public
title: On the verification of neural ODEs with stochastic guarantees
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 35
year: '2021'
...
---
_id: '10670'
abstract:
- lang: eng
text: "Imitation learning enables high-fidelity, vision-based learning of policies
within rich, photorealistic environments. However, such techniques often rely
on traditional discrete-time neural models and face difficulties in generalizing
to domain shifts by failing to account for the causal relationships between the
agent and the environment. In this paper, we propose a theoretical and experimental
framework for learning causal representations using continuous-time neural networks,
specifically over their discrete-time counterparts. We evaluate our method in
the context of visual-control learning of drones over a series of complex tasks,
ranging from short- and long-term navigation, to chasing static and dynamic objects
through photorealistic environments. Our results demonstrate that causal continuous-time\r\ndeep
models can perform robust navigation tasks, where advanced recurrent models fail.
These models learn complex causal control representations directly from raw visual
inputs and scale to solve a variety of tasks using imitation learning."
acknowledgement: "C.V., R.H. A.A. and D.R. are partially supported by Boeing and MIT.
A.A. is supported by the National Science Foundation (NSF) Graduate Research Fellowship
Program. M.L. is supported in part by the Austrian Science Fund (FWF) under grant
Z211-N23 (Wittgenstein Award). Research was sponsored by the United States Air Force
Research Laboratory and the United States Air Force Artificial Intelligence Accelerator
and was accomplished under Cooperative Agreement Number FA8750-19-2-1000. The views
and conclusions contained in this document are those of the authors\r\nand should
not be interpreted as representing the official policies, either expressed or implied,
of the United States Air Force or the U.S. Government. The U.S. Government is authorized
to reproduce and distribute reprints for Government purposes notwithstanding any
copyright notation herein.\r\n"
alternative_title:
- ' Advances in Neural Information Processing Systems'
article_processing_charge: No
arxiv: 1
author:
- first_name: Charles J
full_name: Vorbach, Charles J
last_name: Vorbach
- first_name: Ramin
full_name: Hasani, Ramin
last_name: Hasani
- first_name: Alexander
full_name: Amini, Alexander
last_name: Amini
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Daniela
full_name: Rus, Daniela
last_name: Rus
citation:
ama: 'Vorbach CJ, Hasani R, Amini A, Lechner M, Rus D. Causal navigation by continuous-time
neural networks. In: 35th Conference on Neural Information Processing Systems.
; 2021.'
apa: Vorbach, C. J., Hasani, R., Amini, A., Lechner, M., & Rus, D. (2021). Causal
navigation by continuous-time neural networks. In 35th Conference on Neural
Information Processing Systems. Virtual.
chicago: Vorbach, Charles J, Ramin Hasani, Alexander Amini, Mathias Lechner, and
Daniela Rus. “Causal Navigation by Continuous-Time Neural Networks.” In 35th
Conference on Neural Information Processing Systems, 2021.
ieee: C. J. Vorbach, R. Hasani, A. Amini, M. Lechner, and D. Rus, “Causal navigation
by continuous-time neural networks,” in 35th Conference on Neural Information
Processing Systems, Virtual, 2021.
ista: 'Vorbach CJ, Hasani R, Amini A, Lechner M, Rus D. 2021. Causal navigation
by continuous-time neural networks. 35th Conference on Neural Information Processing
Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information
Processing Systems, .'
mla: Vorbach, Charles J., et al. “Causal Navigation by Continuous-Time Neural Networks.”
35th Conference on Neural Information Processing Systems, 2021.
short: C.J. Vorbach, R. Hasani, A. Amini, M. Lechner, D. Rus, in:, 35th Conference
on Neural Information Processing Systems, 2021.
conference:
end_date: 2021-12-10
location: Virtual
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2021-12-06
date_created: 2022-01-25T15:47:50Z
date_published: 2021-12-01T00:00:00Z
date_updated: 2022-01-26T14:33:31Z
day: '01'
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
external_id:
arxiv:
- '2106.08314'
file:
- access_level: open_access
checksum: be81f0ade174a8c9b2d4fe09590b2021
content_type: application/pdf
creator: mlechner
date_created: 2022-01-26T07:37:24Z
date_updated: 2022-01-26T07:37:24Z
file_id: '10679'
file_name: NeurIPS-2021-causal-navigation-by-continuous-time-neural-networks-Paper.pdf
file_size: 6841228
relation: main_file
success: 1
file_date_updated: 2022-01-26T07:37:24Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://proceedings.neurips.cc/paper/2021/hash/67ba02d73c54f0b83c05507b7fb7267f-Abstract.html
month: '12'
oa: 1
oa_version: Published Version
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
publication: 35th Conference on Neural Information Processing Systems
publication_status: published
quality_controlled: '1'
status: public
title: Causal navigation by continuous-time neural networks
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/3.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND
3.0)
short: CC BY-NC-ND (3.0)
type: conference
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2021'
...
---
_id: '10671'
abstract:
- lang: eng
text: We introduce a new class of time-continuous recurrent neural network models.
Instead of declaring a learning system’s dynamics by implicit nonlinearities,
we construct networks of linear first-order dynamical systems modulated via nonlinear
interlinked gates. The resulting models represent dynamical systems with varying
(i.e., liquid) time-constants coupled to their hidden state, with outputs being
computed by numerical differential equation solvers. These neural networks exhibit
stable and bounded behavior, yield superior expressivity within the family of
neural ordinary differential equations, and give rise to improved performance
on time-series prediction tasks. To demonstrate these properties, we first take
a theoretical approach to find bounds over their dynamics, and compute their expressive
power by the trajectory length measure in a latent trajectory space. We then conduct
a series of time-series prediction experiments to manifest the approximation capability
of Liquid Time-Constant Networks (LTCs) compared to classical and modern RNNs.
acknowledgement: "R.H. and D.R. are partially supported by Boeing. R.H. and R.G. were
partially supported by the Horizon-2020 ECSEL\r\nProject grant No. 783163 (iDev40).
M.L. was supported in part by the Austrian Science Fund (FWF) under grant Z211-N23
(Wittgenstein Award). A.A. is supported by the National Science Foundation (NSF)
Graduate Research Fellowship Program. This research work is partially drawn from
the PhD dissertation of R.H."
alternative_title:
- Technical Tracks
article_processing_charge: No
arxiv: 1
author:
- first_name: Ramin
full_name: Hasani, Ramin
last_name: Hasani
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Alexander
full_name: Amini, Alexander
last_name: Amini
- first_name: Daniela
full_name: Rus, Daniela
last_name: Rus
- first_name: Radu
full_name: Grosu, Radu
last_name: Grosu
citation:
ama: 'Hasani R, Lechner M, Amini A, Rus D, Grosu R. Liquid time-constant networks.
In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol
35. AAAI Press; 2021:7657-7666.'
apa: 'Hasani, R., Lechner, M., Amini, A., Rus, D., & Grosu, R. (2021). Liquid
time-constant networks. In Proceedings of the AAAI Conference on Artificial
Intelligence (Vol. 35, pp. 7657–7666). Virtual: AAAI Press.'
chicago: Hasani, Ramin, Mathias Lechner, Alexander Amini, Daniela Rus, and Radu
Grosu. “Liquid Time-Constant Networks.” In Proceedings of the AAAI Conference
on Artificial Intelligence, 35:7657–66. AAAI Press, 2021.
ieee: R. Hasani, M. Lechner, A. Amini, D. Rus, and R. Grosu, “Liquid time-constant
networks,” in Proceedings of the AAAI Conference on Artificial Intelligence,
Virtual, 2021, vol. 35, no. 9, pp. 7657–7666.
ista: 'Hasani R, Lechner M, Amini A, Rus D, Grosu R. 2021. Liquid time-constant
networks. Proceedings of the AAAI Conference on Artificial Intelligence. AAAI:
Association for the Advancement of Artificial Intelligence, Technical Tracks,
vol. 35, 7657–7666.'
mla: Hasani, Ramin, et al. “Liquid Time-Constant Networks.” Proceedings of the
AAAI Conference on Artificial Intelligence, vol. 35, no. 9, AAAI Press, 2021,
pp. 7657–66.
short: R. Hasani, M. Lechner, A. Amini, D. Rus, R. Grosu, in:, Proceedings of the
AAAI Conference on Artificial Intelligence, AAAI Press, 2021, pp. 7657–7666.
conference:
end_date: 2021-02-09
location: Virtual
name: 'AAAI: Association for the Advancement of Artificial Intelligence'
start_date: 2021-02-02
date_created: 2022-01-25T15:48:36Z
date_published: 2021-05-28T00:00:00Z
date_updated: 2022-05-24T06:36:54Z
day: '28'
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
external_id:
arxiv:
- '2006.04439'
file:
- access_level: open_access
checksum: 0f06995fba06dbcfa7ed965fc66027ff
content_type: application/pdf
creator: mlechner
date_created: 2022-01-26T07:36:03Z
date_updated: 2022-01-26T07:36:03Z
file_id: '10678'
file_name: 16936-Article Text-20430-1-2-20210518 (1).pdf
file_size: 4302669
relation: main_file
success: 1
file_date_updated: 2022-01-26T07:36:03Z
has_accepted_license: '1'
intvolume: ' 35'
issue: '9'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://ojs.aaai.org/index.php/AAAI/article/view/16936
month: '05'
oa: 1
oa_version: Published Version
page: 7657-7666
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
publication: Proceedings of the AAAI Conference on Artificial Intelligence
publication_identifier:
eissn:
- 2374-3468
isbn:
- 978-1-57735-866-4
issn:
- 2159-5399
publication_status: published
publisher: AAAI Press
quality_controlled: '1'
status: public
title: Liquid time-constant networks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 35
year: '2021'
...
---
_id: '10674'
abstract:
- lang: eng
text: 'In two-player games on graphs, the players move a token through a graph to
produce an infinite path, which determines the winner of the game. Such games
are central in formal methods since they model the interaction between a non-terminating
system and its environment. In bidding games the players bid for the right to
move the token: in each round, the players simultaneously submit bids, and the
higher bidder moves the token and pays the other player. Bidding games are known
to have a clean and elegant mathematical structure that relies on the ability
of the players to submit arbitrarily small bids. Many applications, however, require
a fixed granularity for the bids, which can represent, for example, the monetary
value expressed in cents. We study, for the first time, the combination of discrete-bidding
and infinite-duration games. Our most important result proves that these games
form a large determined subclass of concurrent games, where determinacy is the
strong property that there always exists exactly one player who can guarantee
winning the game. In particular, we show that, in contrast to non-discrete bidding
games, the mechanism with which tied bids are resolved plays an important role
in discrete-bidding games. We study several natural tie-breaking mechanisms and
show that, while some do not admit determinacy, most natural mechanisms imply
determinacy for every pair of initial budgets.'
acknowledgement: "This research was supported in part by the Austrian Science Fund
(FWF) under grants S11402-N23 (RiSE/SHiNE), Z211-N23 (Wittgenstein Award), and M
2369-N33 (Meitner fellowship).\r\n"
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Milad
full_name: Aghajohari, Milad
last_name: Aghajohari
- first_name: Guy
full_name: Avni, Guy
id: 463C8BC2-F248-11E8-B48F-1D18A9856A87
last_name: Avni
orcid: 0000-0001-5588-8287
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
citation:
ama: Aghajohari M, Avni G, Henzinger TA. Determinacy in discrete-bidding infinite-duration
games. Logical Methods in Computer Science. 2021;17(1):10:1-10:23. doi:10.23638/LMCS-17(1:10)2021
apa: Aghajohari, M., Avni, G., & Henzinger, T. A. (2021). Determinacy in discrete-bidding
infinite-duration games. Logical Methods in Computer Science. International
Federation for Computational Logic. https://doi.org/10.23638/LMCS-17(1:10)2021
chicago: Aghajohari, Milad, Guy Avni, and Thomas A Henzinger. “Determinacy in Discrete-Bidding
Infinite-Duration Games.” Logical Methods in Computer Science. International
Federation for Computational Logic, 2021. https://doi.org/10.23638/LMCS-17(1:10)2021.
ieee: M. Aghajohari, G. Avni, and T. A. Henzinger, “Determinacy in discrete-bidding
infinite-duration games,” Logical Methods in Computer Science, vol. 17,
no. 1. International Federation for Computational Logic, p. 10:1-10:23, 2021.
ista: Aghajohari M, Avni G, Henzinger TA. 2021. Determinacy in discrete-bidding
infinite-duration games. Logical Methods in Computer Science. 17(1), 10:1-10:23.
mla: Aghajohari, Milad, et al. “Determinacy in Discrete-Bidding Infinite-Duration
Games.” Logical Methods in Computer Science, vol. 17, no. 1, International
Federation for Computational Logic, 2021, p. 10:1-10:23, doi:10.23638/LMCS-17(1:10)2021.
short: M. Aghajohari, G. Avni, T.A. Henzinger, Logical Methods in Computer Science
17 (2021) 10:1-10:23.
date_created: 2022-01-25T16:32:13Z
date_published: 2021-02-03T00:00:00Z
date_updated: 2023-08-17T06:56:42Z
day: '03'
ddc:
- '510'
department:
- _id: ToHe
doi: 10.23638/LMCS-17(1:10)2021
external_id:
arxiv:
- '1905.03588'
isi:
- '000658724600010'
file:
- access_level: open_access
checksum: b35586a50ed1ca8f44767de116d18d81
content_type: application/pdf
creator: alisjak
date_created: 2022-01-26T08:04:50Z
date_updated: 2022-01-26T08:04:50Z
file_id: '10690'
file_name: 2021_LMCS_AGHAJOHAR.pdf
file_size: 819878
relation: main_file
success: 1
file_date_updated: 2022-01-26T08:04:50Z
has_accepted_license: '1'
intvolume: ' 17'
isi: 1
issue: '1'
keyword:
- computer science
- computer science and game theory
- logic in computer science
language:
- iso: eng
month: '02'
oa: 1
oa_version: Published Version
page: 10:1-10:23
project:
- _id: 264B3912-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: M02369
name: Formal Methods meets Algorithmic Game Theory
- _id: 25F2ACDE-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: S11402-N23
name: Rigorous Systems Engineering
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
publication: Logical Methods in Computer Science
publication_identifier:
eissn:
- 1860-5974
publication_status: published
publisher: International Federation for Computational Logic
quality_controlled: '1'
scopus_import: '1'
status: public
title: Determinacy in discrete-bidding infinite-duration games
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 17
year: '2021'
...
---
_id: '10688'
abstract:
- lang: eng
text: "Civl is a static verifier for concurrent programs designed around the conceptual
framework of layered refinement,\r\nwhich views the task of verifying a program
as a sequence of program simplification steps each justified by its own invariant.
Civl verifies a layered concurrent program that compactly expresses all the programs
in this sequence and the supporting invariants. This paper presents the design
and implementation of the Civl verifier."
acknowledgement: This research was performed while Bernhard Kragl was at IST Austria,
supported in part by the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein
Award).
alternative_title:
- Conference Series
article_processing_charge: No
author:
- first_name: Bernhard
full_name: Kragl, Bernhard
id: 320FC952-F248-11E8-B48F-1D18A9856A87
last_name: Kragl
orcid: 0000-0001-7745-9117
- first_name: Shaz
full_name: Qadeer, Shaz
last_name: Qadeer
citation:
ama: 'Kragl B, Qadeer S. The Civl verifier. In: Ruzica P, Whalen MW, eds. Proceedings
of the 21st Conference on Formal Methods in Computer-Aided Design. Vol 2.
TU Wien Academic Press; 2021:143–152. doi:10.34727/2021/isbn.978-3-85448-046-4_23'
apa: 'Kragl, B., & Qadeer, S. (2021). The Civl verifier. In P. Ruzica &
M. W. Whalen (Eds.), Proceedings of the 21st Conference on Formal Methods in
Computer-Aided Design (Vol. 2, pp. 143–152). Virtual: TU Wien Academic Press.
https://doi.org/10.34727/2021/isbn.978-3-85448-046-4_23'
chicago: Kragl, Bernhard, and Shaz Qadeer. “The Civl Verifier.” In Proceedings
of the 21st Conference on Formal Methods in Computer-Aided Design, edited
by Piskac Ruzica and Michael W. Whalen, 2:143–152. TU Wien Academic Press, 2021.
https://doi.org/10.34727/2021/isbn.978-3-85448-046-4_23.
ieee: B. Kragl and S. Qadeer, “The Civl verifier,” in Proceedings of the 21st
Conference on Formal Methods in Computer-Aided Design, Virtual, 2021, vol.
2, pp. 143–152.
ista: 'Kragl B, Qadeer S. 2021. The Civl verifier. Proceedings of the 21st Conference
on Formal Methods in Computer-Aided Design. FMCAD: Formal Methods in Computer-Aided
Design, Conference Series, vol. 2, 143–152.'
mla: Kragl, Bernhard, and Shaz Qadeer. “The Civl Verifier.” Proceedings of the
21st Conference on Formal Methods in Computer-Aided Design, edited by Piskac
Ruzica and Michael W. Whalen, vol. 2, TU Wien Academic Press, 2021, pp. 143–152,
doi:10.34727/2021/isbn.978-3-85448-046-4_23.
short: B. Kragl, S. Qadeer, in:, P. Ruzica, M.W. Whalen (Eds.), Proceedings of the
21st Conference on Formal Methods in Computer-Aided Design, TU Wien Academic Press,
2021, pp. 143–152.
conference:
end_date: 2021-10-22
location: Virtual
name: 'FMCAD: Formal Methods in Computer-Aided Design'
start_date: 2021-10-20
date_created: 2022-01-26T08:01:30Z
date_published: 2021-10-01T00:00:00Z
date_updated: 2022-01-26T08:20:41Z
day: '01'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.34727/2021/isbn.978-3-85448-046-4_23
editor:
- first_name: Piskac
full_name: Ruzica, Piskac
last_name: Ruzica
- first_name: Michael W.
full_name: Whalen, Michael W.
last_name: Whalen
file:
- access_level: open_access
checksum: 35438ac9f9750340b7f8ae4ae3220d9f
content_type: application/pdf
creator: cchlebak
date_created: 2022-01-26T08:04:29Z
date_updated: 2022-01-26T08:04:29Z
file_id: '10689'
file_name: 2021_FCAD2021_Kragl.pdf
file_size: 390555
relation: main_file
success: 1
file_date_updated: 2022-01-26T08:04:29Z
has_accepted_license: '1'
intvolume: ' 2'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
page: 143–152
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
publication: Proceedings of the 21st Conference on Formal Methods in Computer-Aided
Design
publication_identifier:
isbn:
- 978-3-85448-046-4
publication_status: published
publisher: TU Wien Academic Press
quality_controlled: '1'
status: public
title: The Civl verifier
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
volume: 2
year: '2021'
...
---
_id: '10694'
abstract:
- lang: eng
text: 'In a two-player zero-sum graph game the players move a token throughout a
graph to produce an infinite path, which determines the winner or payoff of the
game. Traditionally, the players alternate turns in moving the token. In bidding
games, however, the players have budgets, and in each turn, we hold an “auction”
(bidding) to determine which player moves the token: both players simultaneously
submit bids and the higher bidder moves the token. The bidding mechanisms differ
in their payment schemes. Bidding games were largely studied with variants of
first-price bidding in which only the higher bidder pays his bid. We focus on
all-pay bidding, where both players pay their bids. Finite-duration all-pay bidding
games were studied and shown to be technically more challenging than their first-price
counterparts. We study for the first time, infinite-duration all-pay bidding games.
Our most interesting results are for mean-payoff objectives: we portray a complete
picture for games played on strongly-connected graphs. We study both pure (deterministic)
and mixed (probabilistic) strategies and completely characterize the optimal and
almost-sure (with probability 1) payoffs the players can respectively guarantee.
We show that mean-payoff games under all-pay bidding exhibit the intriguing mathematical
properties of their first-price counterparts; namely, an equivalence with random-turn
games in which in each turn, the player who moves is selected according to a (biased)
coin toss. The equivalences for all-pay bidding are more intricate and unexpected
than for first-price bidding.'
acknowledgement: This research was supported in part by the Austrian Science Fund
(FWF) under grant Z211-N23 (Wittgenstein Award), ERC CoG 863818 (FoRM-SMArt), and
by the European Union's Horizon 2020 research and innovation programme under the
Marie Skłodowska-Curie Grant Agreement No. 665385.
article_processing_charge: No
arxiv: 1
author:
- first_name: Guy
full_name: Avni, Guy
id: 463C8BC2-F248-11E8-B48F-1D18A9856A87
last_name: Avni
orcid: 0000-0001-5588-8287
- first_name: Ismael R
full_name: Jecker, Ismael R
id: 85D7C63E-7D5D-11E9-9C0F-98C4E5697425
last_name: Jecker
- first_name: Dorde
full_name: Zikelic, Dorde
id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
last_name: Zikelic
orcid: 0000-0002-4681-1699
citation:
ama: 'Avni G, Jecker IR, Zikelic D. Infinite-duration all-pay bidding games. In:
Marx D, ed. Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms.
Society for Industrial and Applied Mathematics; 2021:617-636. doi:10.1137/1.9781611976465.38'
apa: 'Avni, G., Jecker, I. R., & Zikelic, D. (2021). Infinite-duration all-pay
bidding games. In D. Marx (Ed.), Proceedings of the 2021 ACM-SIAM Symposium
on Discrete Algorithms (pp. 617–636). Virtual: Society for Industrial and
Applied Mathematics. https://doi.org/10.1137/1.9781611976465.38'
chicago: Avni, Guy, Ismael R Jecker, and Dorde Zikelic. “Infinite-Duration All-Pay
Bidding Games.” In Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms,
edited by Dániel Marx, 617–36. Society for Industrial and Applied Mathematics,
2021. https://doi.org/10.1137/1.9781611976465.38.
ieee: G. Avni, I. R. Jecker, and D. Zikelic, “Infinite-duration all-pay bidding
games,” in Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms,
Virtual, 2021, pp. 617–636.
ista: 'Avni G, Jecker IR, Zikelic D. 2021. Infinite-duration all-pay bidding games.
Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms. SODA: Symposium
on Discrete Algorithms, 617–636.'
mla: Avni, Guy, et al. “Infinite-Duration All-Pay Bidding Games.” Proceedings
of the 2021 ACM-SIAM Symposium on Discrete Algorithms, edited by Dániel Marx,
Society for Industrial and Applied Mathematics, 2021, pp. 617–36, doi:10.1137/1.9781611976465.38.
short: G. Avni, I.R. Jecker, D. Zikelic, in:, D. Marx (Ed.), Proceedings of the
2021 ACM-SIAM Symposium on Discrete Algorithms, Society for Industrial and Applied
Mathematics, 2021, pp. 617–636.
conference:
end_date: 2021-01-13
location: Virtual
name: 'SODA: Symposium on Discrete Algorithms'
start_date: 2021-01-10
date_created: 2022-01-27T12:11:23Z
date_published: 2021-01-01T00:00:00Z
date_updated: 2025-07-14T09:10:12Z
day: '01'
department:
- _id: GradSch
- _id: KrCh
doi: 10.1137/1.9781611976465.38
ec_funded: 1
editor:
- first_name: Dániel
full_name: Marx, Dániel
last_name: Marx
external_id:
arxiv:
- '2005.06636'
language:
- iso: eng
main_file_link:
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url: https://arxiv.org/abs/2005.06636
month: '01'
oa: 1
oa_version: Preprint
page: 617-636
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication: Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms
publication_identifier:
isbn:
- 978-1-61197-646-5
publication_status: published
publisher: Society for Industrial and Applied Mathematics
quality_controlled: '1'
scopus_import: '1'
status: public
title: Infinite-duration all-pay bidding games
type: conference
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2021'
...
---
_id: '8912'
abstract:
- lang: eng
text: "For automata, synchronization, the problem of bringing an automaton to a
particular state regardless of its initial state, is important. It has several
applications in practice and is related to a fifty-year-old conjecture on the
length of the shortest synchronizing word. Although using shorter words increases
the effectiveness in practice, finding a shortest one (which is not necessarily
unique) is NP-hard. For this reason, there exist various heuristics in the literature.
However, high-quality heuristics such as SynchroP producing relatively shorter
sequences are very expensive and can take hours when the automaton has tens of
thousands of states. The SynchroP heuristic has been frequently used as a benchmark
to evaluate the performance of the new heuristics. In this work, we first improve
the runtime of SynchroP and its variants by using algorithmic techniques. We then
focus on adapting SynchroP for many-core architectures,\r\nand overall, we obtain
more than 1000× speedup on GPUs compared to naive sequential implementation that
has been frequently used as a benchmark to evaluate new heuristics in the literature.
We also propose two SynchroP variants and evaluate their performance."
acknowledgement: This work was supported by The Scientific and Technological Research
Council of Turkey (TUBITAK) [grant number 114E569]. This research was supported
in part by the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein Award).
We would like to thank the authors of (Roman & Szykula, 2015) for providing their
heuristics implementations, which we used to compare our SynchroP implementation
as given in Table 11.
article_number: '114203'
article_processing_charge: No
article_type: original
author:
- first_name: Naci E
full_name: Sarac, Naci E
id: 8C6B42F8-C8E6-11E9-A03A-F2DCE5697425
last_name: Sarac
- first_name: Ömer Faruk
full_name: Altun, Ömer Faruk
last_name: Altun
- first_name: Kamil Tolga
full_name: Atam, Kamil Tolga
last_name: Atam
- first_name: Sertac
full_name: Karahoda, Sertac
last_name: Karahoda
- first_name: Kamer
full_name: Kaya, Kamer
last_name: Kaya
- first_name: Hüsnü
full_name: Yenigün, Hüsnü
last_name: Yenigün
citation:
ama: Sarac NE, Altun ÖF, Atam KT, Karahoda S, Kaya K, Yenigün H. Boosting expensive
synchronizing heuristics. Expert Systems with Applications. 2021;167(4).
doi:10.1016/j.eswa.2020.114203
apa: Sarac, N. E., Altun, Ö. F., Atam, K. T., Karahoda, S., Kaya, K., & Yenigün,
H. (2021). Boosting expensive synchronizing heuristics. Expert Systems with
Applications. Elsevier. https://doi.org/10.1016/j.eswa.2020.114203
chicago: Sarac, Naci E, Ömer Faruk Altun, Kamil Tolga Atam, Sertac Karahoda, Kamer
Kaya, and Hüsnü Yenigün. “Boosting Expensive Synchronizing Heuristics.” Expert
Systems with Applications. Elsevier, 2021. https://doi.org/10.1016/j.eswa.2020.114203.
ieee: N. E. Sarac, Ö. F. Altun, K. T. Atam, S. Karahoda, K. Kaya, and H. Yenigün,
“Boosting expensive synchronizing heuristics,” Expert Systems with Applications,
vol. 167, no. 4. Elsevier, 2021.
ista: Sarac NE, Altun ÖF, Atam KT, Karahoda S, Kaya K, Yenigün H. 2021. Boosting
expensive synchronizing heuristics. Expert Systems with Applications. 167(4),
114203.
mla: Sarac, Naci E., et al. “Boosting Expensive Synchronizing Heuristics.” Expert
Systems with Applications, vol. 167, no. 4, 114203, Elsevier, 2021, doi:10.1016/j.eswa.2020.114203.
short: N.E. Sarac, Ö.F. Altun, K.T. Atam, S. Karahoda, K. Kaya, H. Yenigün, Expert
Systems with Applications 167 (2021).
date_created: 2020-12-02T13:34:25Z
date_published: 2021-04-01T00:00:00Z
date_updated: 2023-08-04T11:19:00Z
day: '01'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.1016/j.eswa.2020.114203
external_id:
isi:
- '000640531100038'
file:
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checksum: 600c2f81bc898a725bcfa7cf26ff4fed
content_type: application/pdf
creator: esarac
date_created: 2020-12-02T13:33:51Z
date_updated: 2020-12-02T13:33:51Z
file_id: '8913'
file_name: synchroPaperRevised.pdf
file_size: 634967
relation: main_file
file_date_updated: 2020-12-02T13:33:51Z
has_accepted_license: '1'
intvolume: ' 167'
isi: 1
issue: '4'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Submitted Version
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
publication: Expert Systems with Applications
publication_identifier:
issn:
- '09574174'
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Boosting expensive synchronizing heuristics
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 167
year: '2021'
...
---
_id: '9200'
abstract:
- lang: eng
text: Formal design of embedded and cyber-physical systems relies on mathematical
modeling. In this paper, we consider the model class of hybrid automata whose
dynamics are defined by affine differential equations. Given a set of time-series
data, we present an algorithmic approach to synthesize a hybrid automaton exhibiting
behavior that is close to the data, up to a specified precision, and changes in
synchrony with the data. A fundamental problem in our synthesis algorithm is to
check membership of a time series in a hybrid automaton. Our solution integrates
reachability and optimization techniques for affine dynamical systems to obtain
both a sufficient and a necessary condition for membership, combined in a refinement
framework. The algorithm processes one time series at a time and hence can be
interrupted, provide an intermediate result, and be resumed. We report experimental
results demonstrating the applicability of our synthesis approach.
acknowledgement: This research was supported in part by the Austrian Science Fund
(FWF) under grant Z211-N23 (Wittgenstein Award) and the European Union’s Horizon
2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement
No. 754411.
article_processing_charge: No
arxiv: 1
author:
- first_name: Miriam
full_name: Garcia Soto, Miriam
id: 4B3207F6-F248-11E8-B48F-1D18A9856A87
last_name: Garcia Soto
orcid: 0000-0003-2936-5719
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
- first_name: Christian
full_name: Schilling, Christian
id: 3A2F4DCE-F248-11E8-B48F-1D18A9856A87
last_name: Schilling
orcid: 0000-0003-3658-1065
citation:
ama: 'Garcia Soto M, Henzinger TA, Schilling C. Synthesis of hybrid automata with
affine dynamics from time-series data. In: HSCC ’21: Proceedings of the 24th
International Conference on Hybrid Systems: Computation and Control. Association
for Computing Machinery; 2021:2102.12734. doi:10.1145/3447928.3456704'
apa: 'Garcia Soto, M., Henzinger, T. A., & Schilling, C. (2021). Synthesis of
hybrid automata with affine dynamics from time-series data. In HSCC ’21: Proceedings
of the 24th International Conference on Hybrid Systems: Computation and Control
(p. 2102.12734). Nashville, TN, United States: Association for Computing Machinery.
https://doi.org/10.1145/3447928.3456704'
chicago: 'Garcia Soto, Miriam, Thomas A Henzinger, and Christian Schilling. “Synthesis
of Hybrid Automata with Affine Dynamics from Time-Series Data.” In HSCC ’21:
Proceedings of the 24th International Conference on Hybrid Systems: Computation
and Control, 2102.12734. Association for Computing Machinery, 2021. https://doi.org/10.1145/3447928.3456704.'
ieee: 'M. Garcia Soto, T. A. Henzinger, and C. Schilling, “Synthesis of hybrid automata
with affine dynamics from time-series data,” in HSCC ’21: Proceedings of the
24th International Conference on Hybrid Systems: Computation and Control,
Nashville, TN, United States, 2021, p. 2102.12734.'
ista: 'Garcia Soto M, Henzinger TA, Schilling C. 2021. Synthesis of hybrid automata
with affine dynamics from time-series data. HSCC ’21: Proceedings of the 24th
International Conference on Hybrid Systems: Computation and Control. HSCC: International
Conference on Hybrid Systems Computation and Control, 2102.12734.'
mla: 'Garcia Soto, Miriam, et al. “Synthesis of Hybrid Automata with Affine Dynamics
from Time-Series Data.” HSCC ’21: Proceedings of the 24th International Conference
on Hybrid Systems: Computation and Control, Association for Computing Machinery,
2021, p. 2102.12734, doi:10.1145/3447928.3456704.'
short: 'M. Garcia Soto, T.A. Henzinger, C. Schilling, in:, HSCC ’21: Proceedings
of the 24th International Conference on Hybrid Systems: Computation and Control,
Association for Computing Machinery, 2021, p. 2102.12734.'
conference:
end_date: 2021-05-21
location: Nashville, TN, United States
name: 'HSCC: International Conference on Hybrid Systems Computation and Control'
start_date: 2021-05-19
date_created: 2021-02-26T16:30:39Z
date_published: 2021-05-01T00:00:00Z
date_updated: 2023-08-07T13:49:33Z
day: '01'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.1145/3447928.3456704
ec_funded: 1
external_id:
arxiv:
- '2102.12734'
isi:
- '000932821700028'
file:
- access_level: open_access
checksum: 4c1202c1abf71384c3ee6fea88c2f80e
content_type: application/pdf
creator: kschuh
date_created: 2021-05-25T13:53:22Z
date_updated: 2021-05-25T13:53:22Z
file_id: '9424'
file_name: 2021_HSCC_Soto.pdf
file_size: 1474786
relation: main_file
success: 1
file_date_updated: 2021-05-25T13:53:22Z
has_accepted_license: '1'
isi: 1
keyword:
- hybrid automaton
- membership
- system identification
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
page: '2102.12734'
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
publication: 'HSCC ''21: Proceedings of the 24th International Conference on Hybrid
Systems: Computation and Control'
publication_identifier:
isbn:
- '9781450383394'
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
scopus_import: '1'
status: public
title: Synthesis of hybrid automata with affine dynamics from time-series data
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
year: '2021'
...