---
_id: '12390'
abstract:
- lang: eng
text: "The scope of this thesis is to study quantum systems exhibiting a continuous
symmetry that\r\nis broken on the level of the corresponding effective theory.
In particular we are going to\r\ninvestigate translation-invariant Bose gases
in the mean field limit, effectively described by\r\nthe Hartree functional, and
the Fröhlich Polaron in the regime of strong coupling, effectively\r\ndescribed
by the Pekar functional. The latter is a model describing the interaction between
a\r\ncharged particle and the optical modes of a polar crystal. Regarding the
former, we assume in\r\naddition that the particles in the gas are unconfined,
and typically we will consider particles\r\nthat are subject to an attractive
interaction. In both cases the ground state energy of the\r\nHamiltonian is not
a proper eigenvalue due to the underlying translation-invariance, while on\r\nthe
contrary there exists a whole invariant orbit of minimizers for the corresponding
effective\r\nfunctionals. Both, the absence of proper eigenstates and the broken
symmetry of the effective\r\ntheory, make the study significantly more involved
and it is the content of this thesis to\r\ndevelop a frameworks which allows for
a systematic way to circumvent these issues.\r\nIt is a well-established result
that the ground state energy of Bose gases in the mean field limit,\r\nas well
as the ground state energy of the Fröhlich Polaron in the regime of strong coupling,
is\r\nto leading order given by the minimal energy of the corresponding effective
theory. As part\r\nof this thesis we identify the sub-leading term in the expansion
of the ground state energy,\r\nwhich can be interpreted as the quantum correction
to the classical energy, since the effective\r\ntheories under consideration can
be seen as classical counterparts.\r\nWe are further going to establish an asymptotic
expression for the energy-momentum relation\r\nof the Fröhlich Polaron in the
strong coupling limit. In the regime of suitably small momenta,\r\nthis asymptotic
expression agrees with the energy-momentum relation of a free particle having\r\nan
effectively increased mass, and we find that this effectively increased mass agrees
with the\r\nconjectured value in the physics literature.\r\nIn addition we will
discuss two unrelated papers written by the author during his stay at ISTA\r\nin
the appendix. The first one concerns the realization of anyons, which are quasi-particles\r\nacquiring
a non-trivial phase under the exchange of two particles, as molecular impurities.\r\nThe
second one provides a classification of those vector fields defined on a given
manifold\r\nthat can be written as the gradient of a given functional with respect
to a suitable metric,\r\nprovided that some mild smoothness assumptions hold.
This classification is subsequently\r\nused to identify those quantum Markov semigroups
that can be written as a gradient flow of\r\nthe relative entropy.\r\n"
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Morris
full_name: Brooks, Morris
id: B7ECF9FC-AA38-11E9-AC9A-0930E6697425
last_name: Brooks
orcid: 0000-0002-6249-0928
citation:
ama: Brooks M. Translation-invariant quantum systems with effectively broken symmetry.
2022. doi:10.15479/at:ista:12390
apa: Brooks, M. (2022). Translation-invariant quantum systems with effectively
broken symmetry. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:12390
chicago: Brooks, Morris. “Translation-Invariant Quantum Systems with Effectively
Broken Symmetry.” Institute of Science and Technology Austria, 2022. https://doi.org/10.15479/at:ista:12390.
ieee: M. Brooks, “Translation-invariant quantum systems with effectively broken
symmetry,” Institute of Science and Technology Austria, 2022.
ista: Brooks M. 2022. Translation-invariant quantum systems with effectively broken
symmetry. Institute of Science and Technology Austria.
mla: Brooks, Morris. Translation-Invariant Quantum Systems with Effectively Broken
Symmetry. Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:12390.
short: M. Brooks, Translation-Invariant Quantum Systems with Effectively Broken
Symmetry, Institute of Science and Technology Austria, 2022.
date_created: 2023-01-26T10:00:42Z
date_published: 2022-12-15T00:00:00Z
date_updated: 2023-08-07T13:32:09Z
day: '15'
ddc:
- '500'
degree_awarded: PhD
department:
- _id: GradSch
- _id: RoSe
doi: 10.15479/at:ista:12390
ec_funded: 1
file:
- access_level: open_access
checksum: b31460e937f33b557abb40ebef02b567
content_type: application/pdf
creator: cchlebak
date_created: 2023-01-26T10:02:34Z
date_updated: 2023-01-26T10:02:34Z
file_id: '12391'
file_name: Brooks_Thesis.pdf
file_size: 3095225
relation: main_file
success: 1
- access_level: closed
checksum: 9751869fa5e7981588ad4228f4fd4bd6
content_type: application/octet-stream
creator: cchlebak
date_created: 2023-01-26T10:02:42Z
date_updated: 2023-01-26T10:02:42Z
file_id: '12392'
file_name: Brooks_Thesis.tex
file_size: 809842
relation: source_file
file_date_updated: 2023-01-26T10:02:42Z
has_accepted_license: '1'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-sa/4.0/
month: '12'
oa: 1
oa_version: Published Version
page: '196'
project:
- _id: 25C6DC12-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '694227'
name: Analysis of quantum many-body systems
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '9005'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Robert
full_name: Seiringer, Robert
id: 4AFD0470-F248-11E8-B48F-1D18A9856A87
last_name: Seiringer
orcid: 0000-0002-6781-0521
title: Translation-invariant quantum systems with effectively broken symmetry
tmp:
image: /images/cc_by_nc_sa.png
legal_code_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC
BY-NC-SA 4.0)
short: CC BY-NC-SA (4.0)
type: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2022'
...
---
_id: '12401'
abstract:
- lang: eng
text: "Detachment of the cancer cells from the bulk of the tumor is the first step
of metastasis, which\r\nis the primary cause of cancer related deaths. It is unclear,
which factors contribute to this step.\r\nRecent studies indicate a crucial role
of the tumor microenvironment in malignant\r\ntransformation and metastasis. Studying
cancer cell invasion and detachments quantitatively in\r\nthe context of its physiological
microenvironment is technically challenging. Especially, precise\r\ncontrol of
microenvironmental properties in vivo is currently not possible. Here, I studied
the\r\nrole of microenvironment geometry in the invasion and detachment of cancer
cells from the\r\nbulk with a simplistic and reductionist approach. In this approach,
I engineered microfluidic\r\ndevices to mimic a pseudo 3D extracellular matrix
environment, where I was able to\r\nquantitatively tune the geometrical configuration
of the microenvironment and follow tumor\r\ncells with fluorescence live imaging.
To aid quantitative analysis I developed a widely applicable\r\nsoftware application
to automatically analyze and visualize particle tracking data.\r\nQuantitative
analysis of tumor cell invasion in isotropic and anisotropic microenvironments\r\nshowed
that heterogeneity in the microenvironment promotes faster invasion and more\r\nfrequent
detachment of cells. These observations correlated with overall higher speed of
cells at\r\nthe edge of the bulk of the cells. In heterogeneous microenvironments
cells preferentially\r\npassed through larger pores, thus invading areas of least
resistance and generating finger-like\r\ninvasive structures. The detachments
occurred mostly at the tips of these structures.\r\nTo investigate the potential
mechanism, we established a two dimensional model to simulate\r\nactive Brownian
particles representing the cell nuclei dynamics. These simulations backed our
in\r\nvitro observations without the need of precise fitting the simulation parameters.
Our model\r\nsuggests the importance of the pore heterogeneity in the direction
perpendicular to the\r\norientation of bias field (lateral heterogeneity), which
causes the interface roughening."
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Saren
full_name: Tasciyan, Saren
id: 4323B49C-F248-11E8-B48F-1D18A9856A87
last_name: Tasciyan
orcid: 0000-0003-1671-393X
citation:
ama: Tasciyan S. Role of microenvironment heterogeneity in cancer cell invasion.
2022. doi:10.15479/at:ista:12401
apa: Tasciyan, S. (2022). Role of microenvironment heterogeneity in cancer cell
invasion. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:12401
chicago: Tasciyan, Saren. “Role of Microenvironment Heterogeneity in Cancer Cell
Invasion.” Institute of Science and Technology Austria, 2022. https://doi.org/10.15479/at:ista:12401.
ieee: S. Tasciyan, “Role of microenvironment heterogeneity in cancer cell invasion,”
Institute of Science and Technology Austria, 2022.
ista: Tasciyan S. 2022. Role of microenvironment heterogeneity in cancer cell invasion.
Institute of Science and Technology Austria.
mla: Tasciyan, Saren. Role of Microenvironment Heterogeneity in Cancer Cell Invasion.
Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:12401.
short: S. Tasciyan, Role of Microenvironment Heterogeneity in Cancer Cell Invasion,
Institute of Science and Technology Austria, 2022.
date_created: 2023-01-26T11:55:16Z
date_published: 2022-12-22T00:00:00Z
date_updated: 2024-09-10T12:04:26Z
day: '22'
ddc:
- '610'
degree_awarded: PhD
department:
- _id: GradSch
- _id: MiSi
doi: 10.15479/at:ista:12401
file:
- access_level: open_access
checksum: cc4a2b4a7e3c4ee8ef7f2dbf909b12bd
content_type: application/pdf
creator: cchlebak
date_created: 2023-01-26T11:58:14Z
date_updated: 2023-12-21T23:30:03Z
embargo: 2023-12-20
file_id: '12402'
file_name: PhD-Thesis_Saren Tasciyan_formatted_aftercrash_fixed_600dpi_95pc_final_PDFA3b.pdf
file_size: 42059787
relation: main_file
- access_level: closed
checksum: f1b4ca98b8ab0cb043b1830971e9bd9c
content_type: application/x-zip-compressed
creator: cchlebak
date_created: 2023-01-26T12:00:10Z
date_updated: 2023-12-21T23:30:03Z
embargo_to: open_access
file_id: '12403'
file_name: Source Files - Saren Tasciyan - PhD Thesis.zip
file_size: 261256696
relation: source_file
file_date_updated: 2023-12-21T23:30:03Z
has_accepted_license: '1'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
page: '105'
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '679'
relation: part_of_dissertation
status: public
- id: '10703'
relation: part_of_dissertation
status: public
- id: '7885'
relation: part_of_dissertation
status: public
- id: '9429'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Michael K
full_name: Sixt, Michael K
id: 41E9FBEA-F248-11E8-B48F-1D18A9856A87
last_name: Sixt
orcid: 0000-0002-6620-9179
title: Role of microenvironment heterogeneity in cancer cell invasion
type: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2022'
...
---
_id: '12431'
abstract:
- lang: eng
text: This paper presents a new representation of curve dynamics, with applications
to vortex filaments in fluid dynamics. Instead of representing these filaments
with explicit curve geometry and Lagrangian equations of motion, we represent
curves implicitly with a new co-dimensional 2 level set description. Our implicit
representation admits several redundant mathematical degrees of freedom in both
the configuration and the dynamics of the curves, which can be tailored specifically
to improve numerical robustness, in contrast to naive approaches for implicit
curve dynamics that suffer from overwhelming numerical stability problems. Furthermore,
we note how these hidden degrees of freedom perfectly map to a Clebsch representation
in fluid dynamics. Motivated by these observations, we introduce untwisted level
set functions and non-swirling dynamics which successfully regularize sources
of numerical instability, particularly in the twisting modes around curve filaments.
A consequence is a novel simulation method which produces stable dynamics for
large numbers of interacting vortex filaments and effortlessly handles topological
changes and re-connection events.
acknowledgement: We thank the visual computing group at IST Austria for their valuable
discussions and feedback. Houdini Education licenses were provided by SideFX software.
This project was funded in part by the European Research Council (ERC Consolidator
Grant 101045083 CoDiNA).
article_number: '241'
article_processing_charge: No
article_type: original
author:
- first_name: Sadashige
full_name: Ishida, Sadashige
id: 6F7C4B96-A8E9-11E9-A7CA-09ECE5697425
last_name: Ishida
- first_name: Christopher J
full_name: Wojtan, Christopher J
id: 3C61F1D2-F248-11E8-B48F-1D18A9856A87
last_name: Wojtan
orcid: 0000-0001-6646-5546
- first_name: Albert
full_name: Chern, Albert
last_name: Chern
citation:
ama: Ishida S, Wojtan C, Chern A. Hidden degrees of freedom in implicit vortex filaments.
ACM Transactions on Graphics. 2022;41(6). doi:10.1145/3550454.3555459
apa: Ishida, S., Wojtan, C., & Chern, A. (2022). Hidden degrees of freedom in
implicit vortex filaments. ACM Transactions on Graphics. Association for
Computing Machinery. https://doi.org/10.1145/3550454.3555459
chicago: Ishida, Sadashige, Chris Wojtan, and Albert Chern. “Hidden Degrees of Freedom
in Implicit Vortex Filaments.” ACM Transactions on Graphics. Association
for Computing Machinery, 2022. https://doi.org/10.1145/3550454.3555459.
ieee: S. Ishida, C. Wojtan, and A. Chern, “Hidden degrees of freedom in implicit
vortex filaments,” ACM Transactions on Graphics, vol. 41, no. 6. Association
for Computing Machinery, 2022.
ista: Ishida S, Wojtan C, Chern A. 2022. Hidden degrees of freedom in implicit vortex
filaments. ACM Transactions on Graphics. 41(6), 241.
mla: Ishida, Sadashige, et al. “Hidden Degrees of Freedom in Implicit Vortex Filaments.”
ACM Transactions on Graphics, vol. 41, no. 6, 241, Association for Computing
Machinery, 2022, doi:10.1145/3550454.3555459.
short: S. Ishida, C. Wojtan, A. Chern, ACM Transactions on Graphics 41 (2022).
date_created: 2023-01-29T23:00:59Z
date_published: 2022-12-01T00:00:00Z
date_updated: 2023-08-04T09:37:23Z
day: '01'
ddc:
- '000'
department:
- _id: ChWo
doi: 10.1145/3550454.3555459
external_id:
isi:
- '000891651900061'
file:
- access_level: open_access
checksum: a2fba257fdefe0e747182be6c0f7c70c
content_type: application/pdf
creator: dernst
date_created: 2023-01-30T07:15:48Z
date_updated: 2023-01-30T07:15:48Z
file_id: '12433'
file_name: 2022_ACM_Ishida.pdf
file_size: 15551202
relation: main_file
success: 1
file_date_updated: 2023-01-30T07:15:48Z
has_accepted_license: '1'
intvolume: ' 41'
isi: 1
issue: '6'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
project:
- _id: 34bc2376-11ca-11ed-8bc3-9a3b3961a088
grant_number: '101045083'
name: Computational Discovery of Numerical Algorithms for Animation and Simulation
of Natural Phenomena
publication: ACM Transactions on Graphics
publication_identifier:
eissn:
- 1557-7368
issn:
- 0730-0301
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
scopus_import: '1'
status: public
title: Hidden degrees of freedom in implicit vortex filaments
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: 41
year: '2022'
...
---
_id: '12432'
abstract:
- lang: eng
text: "We present CertifyHAM, a deterministic algorithm that takes a graph G as
input and either finds a Hamilton cycle of G or outputs that such a cycle does
not exist. If G ∼ G(n, p) and p ≥\r\n100 log n/n then the expected running time
of CertifyHAM is O(n/p) which is best possible. This improves upon previous results
due to Gurevich and Shelah, Thomason and Alon, and\r\nKrivelevich, who proved
analogous results for p being constant, p ≥ 12n −1/3 and p ≥ 70n\r\n−1/2 respectively."
acknowledgement: "This project has received funding from the European Union’s Horizon
2020\r\nresearch and innovation programme under the Marie Skłodowska-Curie grant\r\nagreement
No 101034413"
article_processing_charge: No
author:
- first_name: Michael
full_name: Anastos, Michael
id: 0b2a4358-bb35-11ec-b7b9-e3279b593dbb
last_name: Anastos
citation:
ama: 'Anastos M. Solving the Hamilton cycle problem fast on average. In: 63rd
Annual IEEE Symposium on Foundations of Computer Science. Vol 2022-October.
Institute of Electrical and Electronics Engineers; 2022:919-930. doi:10.1109/FOCS54457.2022.00091'
apa: 'Anastos, M. (2022). Solving the Hamilton cycle problem fast on average. In
63rd Annual IEEE Symposium on Foundations of Computer Science (Vol. 2022–October,
pp. 919–930). Denver, CO, United States: Institute of Electrical and Electronics
Engineers. https://doi.org/10.1109/FOCS54457.2022.00091'
chicago: Anastos, Michael. “Solving the Hamilton Cycle Problem Fast on Average.”
In 63rd Annual IEEE Symposium on Foundations of Computer Science, 2022–October:919–30.
Institute of Electrical and Electronics Engineers, 2022. https://doi.org/10.1109/FOCS54457.2022.00091.
ieee: M. Anastos, “Solving the Hamilton cycle problem fast on average,” in 63rd
Annual IEEE Symposium on Foundations of Computer Science, Denver, CO, United
States, 2022, vol. 2022–October, pp. 919–930.
ista: 'Anastos M. 2022. Solving the Hamilton cycle problem fast on average. 63rd
Annual IEEE Symposium on Foundations of Computer Science. FOCS: Symposium on Foundations
of Computer Science vol. 2022–October, 919–930.'
mla: Anastos, Michael. “Solving the Hamilton Cycle Problem Fast on Average.” 63rd
Annual IEEE Symposium on Foundations of Computer Science, vol. 2022–October,
Institute of Electrical and Electronics Engineers, 2022, pp. 919–30, doi:10.1109/FOCS54457.2022.00091.
short: M. Anastos, in:, 63rd Annual IEEE Symposium on Foundations of Computer Science,
Institute of Electrical and Electronics Engineers, 2022, pp. 919–930.
conference:
end_date: 2022-11-03
location: Denver, CO, United States
name: 'FOCS: Symposium on Foundations of Computer Science'
start_date: 2022-10-31
date_created: 2023-01-29T23:00:59Z
date_published: 2022-12-01T00:00:00Z
date_updated: 2023-08-04T09:37:56Z
day: '01'
department:
- _id: MaKw
doi: 10.1109/FOCS54457.2022.00091
ec_funded: 1
external_id:
isi:
- '000909382900084'
isi: 1
language:
- iso: eng
month: '12'
oa_version: None
page: 919-930
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: 63rd Annual IEEE Symposium on Foundations of Computer Science
publication_identifier:
isbn:
- '9781665455190'
issn:
- 0272-5428
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Solving the Hamilton cycle problem fast on average
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 2022-October
year: '2022'
...
---
_id: '12452'
abstract:
- lang: eng
text: Portrait viewpoint and illumination editing is an important problem with several
applications in VR/AR, movies, and photography. Comprehensive knowledge of geometry
and illumination is critical for obtaining photorealistic results. Current methods
are unable to explicitly model in 3D while handing both viewpoint and illumination
editing from a single image. In this paper, we propose VoRF, a novel approach
that can take even a single portrait image as input and relight human heads under
novel illuminations that can be viewed from arbitrary viewpoints. VoRF represents
a human head as a continuous volumetric field and learns a prior model of human
heads using a coordinate-based MLP with separate latent spaces for identity and
illumination. The prior model is learnt in an auto-decoder manner over a diverse
class of head shapes and appearances, allowing VoRF to generalize to novel test
identities from a single input image. Additionally, VoRF has a reflectance MLP
that uses the intermediate features of the prior model for rendering One-Light-at-A-Time
(OLAT) images under novel views. We synthesize novel illuminations by combining
these OLAT images with target environment maps. Qualitative and quantitative evaluations
demonstrate the effectiveness of VoRF for relighting and novel view synthesis
even when applied to unseen subjects under uncontrolled illuminations.
acknowledgement: This work was supported by the ERC Consolidator Grant 4DReply (770784).
article_number: '708'
article_processing_charge: No
author:
- first_name: Pramod
full_name: Rao, Pramod
last_name: Rao
- first_name: Mallikarjun
full_name: B R, Mallikarjun
last_name: B R
- first_name: Gereon
full_name: Fox, Gereon
last_name: Fox
- first_name: Tim
full_name: Weyrich, Tim
last_name: Weyrich
- first_name: Bernd
full_name: Bickel, Bernd
id: 49876194-F248-11E8-B48F-1D18A9856A87
last_name: Bickel
orcid: 0000-0001-6511-9385
- first_name: Hans-Peter
full_name: Seidel, Hans-Peter
last_name: Seidel
- first_name: Hanspeter
full_name: Pfister, Hanspeter
last_name: Pfister
- first_name: Wojciech
full_name: Matusik, Wojciech
last_name: Matusik
- first_name: Ayush
full_name: Tewari, Ayush
last_name: Tewari
- first_name: Christian
full_name: Theobalt, Christian
last_name: Theobalt
- first_name: Mohamed
full_name: Elgharib, Mohamed
last_name: Elgharib
citation:
ama: 'Rao P, B R M, Fox G, et al. VoRF: Volumetric Relightable Faces. In: 33rd
British Machine Vision Conference. British Machine Vision Association and
Society for Pattern Recognition; 2022.'
apa: 'Rao, P., B R, M., Fox, G., Weyrich, T., Bickel, B., Seidel, H.-P., … Elgharib,
M. (2022). VoRF: Volumetric Relightable Faces. In 33rd British Machine Vision
Conference. London, United Kingdom: British Machine Vision Association and
Society for Pattern Recognition.'
chicago: 'Rao, Pramod, Mallikarjun B R, Gereon Fox, Tim Weyrich, Bernd Bickel, Hans-Peter
Seidel, Hanspeter Pfister, et al. “VoRF: Volumetric Relightable Faces.” In 33rd
British Machine Vision Conference. British Machine Vision Association and
Society for Pattern Recognition, 2022.'
ieee: 'P. Rao et al., “VoRF: Volumetric Relightable Faces,” in 33rd British
Machine Vision Conference, London, United Kingdom, 2022.'
ista: 'Rao P, B R M, Fox G, Weyrich T, Bickel B, Seidel H-P, Pfister H, Matusik
W, Tewari A, Theobalt C, Elgharib M. 2022. VoRF: Volumetric Relightable Faces.
33rd British Machine Vision Conference. BMVC: British Machine Vision Conference,
708.'
mla: 'Rao, Pramod, et al. “VoRF: Volumetric Relightable Faces.” 33rd British
Machine Vision Conference, 708, British Machine Vision Association and Society
for Pattern Recognition, 2022.'
short: P. Rao, M. B R, G. Fox, T. Weyrich, B. Bickel, H.-P. Seidel, H. Pfister,
W. Matusik, A. Tewari, C. Theobalt, M. Elgharib, in:, 33rd British Machine Vision
Conference, British Machine Vision Association and Society for Pattern Recognition,
2022.
conference:
end_date: 2022-11-24
location: London, United Kingdom
name: 'BMVC: British Machine Vision Conference'
start_date: 2022-11-21
date_created: 2023-01-30T10:47:06Z
date_published: 2022-12-01T00:00:00Z
date_updated: 2023-10-31T08:40:55Z
day: '01'
ddc:
- '000'
department:
- _id: BeBi
file:
- access_level: open_access
checksum: b60b70bb48700aee709c85a69231821d
content_type: application/pdf
creator: bbickel
date_created: 2023-01-30T10:48:18Z
date_updated: 2023-01-30T10:48:18Z
file_id: '12453'
file_name: vorf_main.pdf
file_size: 5202710
relation: main_file
title: 'VoRF: Volumetric Relightable Faces'
- access_level: open_access
checksum: ce5f4ce66eaaa1590ee5df989fca6f61
content_type: application/pdf
creator: bbickel
date_created: 2023-01-30T10:48:29Z
date_updated: 2023-01-30T10:48:29Z
file_id: '12454'
file_name: vorf_supp.pdf
file_size: 37953188
relation: supplementary_material
title: 'VoRF: Volumetric Relightable Faces – SUPPLEMENTAL MATERIAL –'
- access_level: open_access
checksum: 08aecca434b08fee75ee1efe87943718
content_type: video/mp4
creator: bbickel
date_created: 2023-01-30T10:48:37Z
date_updated: 2023-01-30T10:48:37Z
file_id: '12455'
file_name: video.mp4
file_size: 57855492
relation: supplementary_material
file_date_updated: 2023-01-30T10:48:37Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://bmvc2022.mpi-inf.mpg.de/708/
month: '12'
oa: 1
oa_version: Published Version
publication: 33rd British Machine Vision Conference
publication_status: published
publisher: British Machine Vision Association and Society for Pattern Recognition
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'VoRF: Volumetric Relightable Faces'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '12480'
abstract:
- lang: eng
text: 'We consider the problem of estimating a signal from measurements obtained
via a generalized linear model. We focus on estimators based on approximate message
passing (AMP), a family of iterative algorithms with many appealing features:
the performance of AMP in the high-dimensional limit can be succinctly characterized
under suitable model assumptions; AMP can also be tailored to the empirical distribution
of the signal entries, and for a wide class of estimation problems, AMP is conjectured
to be optimal among all polynomial-time algorithms. However, a major issue of
AMP is that in many models (such as phase retrieval), it requires an initialization
correlated with the ground-truth signal and independent from the measurement matrix.
Assuming that such an initialization is available is typically not realistic.
In this paper, we solve this problem by proposing an AMP algorithm initialized
with a spectral estimator. With such an initialization, the standard AMP analysis
fails since the spectral estimator depends in a complicated way on the design
matrix. Our main contribution is a rigorous characterization of the performance
of AMP with spectral initialization in the high-dimensional limit. The key technical
idea is to define and analyze a two-phase artificial AMP algorithm that first
produces the spectral estimator, and then closely approximates the iterates of
the true AMP. We also provide numerical results that demonstrate the validity
of the proposed approach.'
acknowledgement: "The authors would like to thank Andrea Montanari for helpful discussions.\r\nM
Mondelli was partially supported by the 2019 Lopez-Loreta Prize. R Venkataramanan
was partially supported by the Alan Turing Institute under the EPSRC Grant\r\nEP/N510129/1."
article_number: '114003'
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
- first_name: Ramji
full_name: Venkataramanan, Ramji
last_name: Venkataramanan
citation:
ama: 'Mondelli M, Venkataramanan R. Approximate message passing with spectral initialization
for generalized linear models. Journal of Statistical Mechanics: Theory and
Experiment. 2022;2022(11). doi:10.1088/1742-5468/ac9828'
apa: 'Mondelli, M., & Venkataramanan, R. (2022). Approximate message passing
with spectral initialization for generalized linear models. Journal of Statistical
Mechanics: Theory and Experiment. IOP Publishing. https://doi.org/10.1088/1742-5468/ac9828'
chicago: 'Mondelli, Marco, and Ramji Venkataramanan. “Approximate Message Passing
with Spectral Initialization for Generalized Linear Models.” Journal of Statistical
Mechanics: Theory and Experiment. IOP Publishing, 2022. https://doi.org/10.1088/1742-5468/ac9828.'
ieee: 'M. Mondelli and R. Venkataramanan, “Approximate message passing with spectral
initialization for generalized linear models,” Journal of Statistical Mechanics:
Theory and Experiment, vol. 2022, no. 11. IOP Publishing, 2022.'
ista: 'Mondelli M, Venkataramanan R. 2022. Approximate message passing with spectral
initialization for generalized linear models. Journal of Statistical Mechanics:
Theory and Experiment. 2022(11), 114003.'
mla: 'Mondelli, Marco, and Ramji Venkataramanan. “Approximate Message Passing with
Spectral Initialization for Generalized Linear Models.” Journal of Statistical
Mechanics: Theory and Experiment, vol. 2022, no. 11, 114003, IOP Publishing,
2022, doi:10.1088/1742-5468/ac9828.'
short: 'M. Mondelli, R. Venkataramanan, Journal of Statistical Mechanics: Theory
and Experiment 2022 (2022).'
date_created: 2023-02-02T08:31:57Z
date_published: 2022-11-24T00:00:00Z
date_updated: 2024-03-07T10:36:52Z
day: '24'
ddc:
- '510'
- '530'
department:
- _id: MaMo
doi: 10.1088/1742-5468/ac9828
external_id:
isi:
- '000889589900001'
file:
- access_level: open_access
checksum: 01411ffa76d3e380a0446baeb89b1ef7
content_type: application/pdf
creator: dernst
date_created: 2023-02-02T08:35:52Z
date_updated: 2023-02-02T08:35:52Z
file_id: '12481'
file_name: 2022_JourStatisticalMechanics_Mondelli.pdf
file_size: 1729997
relation: main_file
success: 1
file_date_updated: 2023-02-02T08:35:52Z
has_accepted_license: '1'
intvolume: ' 2022'
isi: 1
issue: '11'
keyword:
- Statistics
- Probability and Uncertainty
- Statistics and Probability
- Statistical and Nonlinear Physics
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
project:
- _id: 059876FA-7A3F-11EA-A408-12923DDC885E
name: Prix Lopez-Loretta 2019 - Marco Mondelli
publication: 'Journal of Statistical Mechanics: Theory and Experiment'
publication_identifier:
issn:
- 1742-5468
publication_status: published
publisher: IOP Publishing
quality_controlled: '1'
related_material:
record:
- id: '10598'
relation: earlier_version
status: public
scopus_import: '1'
status: public
title: Approximate message passing with spectral initialization for generalized linear
models
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: 2022
year: '2022'
...
---
_id: '12495'
abstract:
- lang: eng
text: "Fairness-aware learning aims at constructing classifiers that not only make
accurate predictions, but also do not discriminate against specific groups. It
is a fast-growing area of\r\nmachine learning with far-reaching societal impact.
However, existing fair learning methods\r\nare vulnerable to accidental or malicious
artifacts in the training data, which can cause\r\nthem to unknowingly produce
unfair classifiers. In this work we address the problem of\r\nfair learning from
unreliable training data in the robust multisource setting, where the\r\navailable
training data comes from multiple sources, a fraction of which might not be representative
of the true data distribution. We introduce FLEA, a filtering-based algorithm\r\nthat
identifies and suppresses those data sources that would have a negative impact
on\r\nfairness or accuracy if they were used for training. As such, FLEA is not
a replacement of\r\nprior fairness-aware learning methods but rather an augmentation
that makes any of them\r\nrobust against unreliable training data. We show the
effectiveness of our approach by a\r\ndiverse range of experiments on multiple
datasets. Additionally, we prove formally that\r\n–given enough data– FLEA protects
the learner against corruptions as long as the fraction of\r\naffected data sources
is less than half. Our source code and documentation are available at\r\nhttps://github.com/ISTAustria-CVML/FLEA."
acknowledged_ssus:
- _id: ScienComp
acknowledgement: 'The authors would like to thank Bernd Prach, Elias Frantar, Alexandra
Peste, Mahdi Nikdan, and Peter Súkeník for their helpful feedback. This research
was supported by the Scientific Service Units (SSU) of IST Austria through resources
provided by Scientific Computing (SciComp). This publication was made possible by
an ETH AI Center postdoctoral fellowship granted to Nikola Konstantinov. Eugenia
Iofinova was supported in part by the FWF DK VGSCO, grant agreement number W1260-N35. '
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Eugenia B
full_name: Iofinova, Eugenia B
id: f9a17499-f6e0-11ea-865d-fdf9a3f77117
last_name: Iofinova
orcid: 0000-0002-7778-3221
- first_name: Nikola H
full_name: Konstantinov, Nikola H
id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87
last_name: Konstantinov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Iofinova EB, Konstantinov NH, Lampert C. FLEA: Provably robust fair multisource
learning from unreliable training data. Transactions on Machine Learning Research.
2022.'
apa: 'Iofinova, E. B., Konstantinov, N. H., & Lampert, C. (2022). FLEA: Provably
robust fair multisource learning from unreliable training data. Transactions
on Machine Learning Research. ML Research Press.'
chicago: 'Iofinova, Eugenia B, Nikola H Konstantinov, and Christoph Lampert. “FLEA:
Provably Robust Fair Multisource Learning from Unreliable Training Data.” Transactions
on Machine Learning Research. ML Research Press, 2022.'
ieee: 'E. B. Iofinova, N. H. Konstantinov, and C. Lampert, “FLEA: Provably robust
fair multisource learning from unreliable training data,” Transactions on Machine
Learning Research. ML Research Press, 2022.'
ista: 'Iofinova EB, Konstantinov NH, Lampert C. 2022. FLEA: Provably robust fair
multisource learning from unreliable training data. Transactions on Machine Learning
Research.'
mla: 'Iofinova, Eugenia B., et al. “FLEA: Provably Robust Fair Multisource Learning
from Unreliable Training Data.” Transactions on Machine Learning Research,
ML Research Press, 2022.'
short: E.B. Iofinova, N.H. Konstantinov, C. Lampert, Transactions on Machine Learning
Research (2022).
date_created: 2023-02-02T20:29:57Z
date_published: 2022-12-22T00:00:00Z
date_updated: 2023-02-23T10:30:54Z
day: '22'
ddc:
- '000'
department:
- _id: ChLa
external_id:
arxiv:
- '2106.11732'
file:
- access_level: open_access
checksum: 97c8a8470759cab597abb973ca137a3b
content_type: application/pdf
creator: dernst
date_created: 2023-02-23T10:30:04Z
date_updated: 2023-02-23T10:30:04Z
file_id: '12673'
file_name: 2022_TMLR_Iofinova.pdf
file_size: 1948063
relation: main_file
success: 1
file_date_updated: 2023-02-23T10:30:04Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://openreview.net/forum?id=XsPopigZXV
month: '12'
oa: 1
oa_version: Published Version
project:
- _id: 9B9290DE-BA93-11EA-9121-9846C619BF3A
grant_number: ' W1260-N35'
name: Vienna Graduate School on Computational Optimization
publication: Transactions on Machine Learning Research
publication_identifier:
issn:
- 2835-8856
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
link:
- description: source code
relation: software
url: https://github.com/ISTAustria-CVML/FLEA
status: public
title: 'FLEA: Provably robust fair multisource learning from unreliable training 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: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '12508'
abstract:
- lang: eng
text: "We explore the notion of history-determinism in the context of timed automata
(TA). History-deterministic automata are those in which nondeterminism can be
resolved on the fly, based on the run constructed thus far. History-determinism
is a robust property that admits different game-based characterisations, and history-deterministic
specifications allow for game-based verification without an expensive determinization
step.\r\nWe show yet another characterisation of history-determinism in terms
of fair simulation, at the general level of labelled transition systems: a system
is history-deterministic precisely if and only if it fairly simulates all language
smaller systems.\r\nFor timed automata over infinite timed words it is known that
universality is undecidable for Büchi TA. We show that for history-deterministic
TA with arbitrary parity acceptance, timed universality, inclusion, and synthesis
all remain decidable and are ExpTime-complete.\r\nFor the subclass of TA with
safety or reachability acceptance, we show that checking whether such an automaton
is history-deterministic is decidable (in ExpTime), and history-deterministic
TA with safety acceptance are effectively determinizable without introducing new
automata states."
acknowledgement: "Thomas A. Henzinger: This work was supported in part by the ERC-2020-AdG
101020093.\r\nPatrick Totzke: acknowledges support from the EPSRC, project no. EP/V025848/1.\r\n"
alternative_title:
- LIPIcs
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
- first_name: Karoliina
full_name: Lehtinen, Karoliina
last_name: Lehtinen
- first_name: Patrick
full_name: Totzke, Patrick
last_name: Totzke
citation:
ama: 'Henzinger TA, Lehtinen K, Totzke P. History-deterministic timed automata.
In: 33rd International Conference on Concurrency Theory. Vol 243. Schloss
Dagstuhl - Leibniz-Zentrum für Informatik; 2022:14:1-14:21. doi:10.4230/LIPIcs.CONCUR.2022.14'
apa: 'Henzinger, T. A., Lehtinen, K., & Totzke, P. (2022). History-deterministic
timed automata. In 33rd International Conference on Concurrency Theory
(Vol. 243, p. 14:1-14:21). Warsaw, Poland: Schloss Dagstuhl - Leibniz-Zentrum
für Informatik. https://doi.org/10.4230/LIPIcs.CONCUR.2022.14'
chicago: Henzinger, Thomas A, Karoliina Lehtinen, and Patrick Totzke. “History-Deterministic
Timed Automata.” In 33rd International Conference on Concurrency Theory,
243:14:1-14:21. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022. https://doi.org/10.4230/LIPIcs.CONCUR.2022.14.
ieee: T. A. Henzinger, K. Lehtinen, and P. Totzke, “History-deterministic timed
automata,” in 33rd International Conference on Concurrency Theory, Warsaw,
Poland, 2022, vol. 243, p. 14:1-14:21.
ista: 'Henzinger TA, Lehtinen K, Totzke P. 2022. History-deterministic timed automata.
33rd International Conference on Concurrency Theory. CONCUR: Conference on Concurrency
Theory, LIPIcs, vol. 243, 14:1-14:21.'
mla: Henzinger, Thomas A., et al. “History-Deterministic Timed Automata.” 33rd
International Conference on Concurrency Theory, vol. 243, Schloss Dagstuhl
- Leibniz-Zentrum für Informatik, 2022, p. 14:1-14:21, doi:10.4230/LIPIcs.CONCUR.2022.14.
short: T.A. Henzinger, K. Lehtinen, P. Totzke, in:, 33rd International Conference
on Concurrency Theory, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022,
p. 14:1-14:21.
conference:
end_date: 2022-09-16
location: Warsaw, Poland
name: 'CONCUR: Conference on Concurrency Theory'
start_date: 2022-09-13
date_created: 2023-02-05T17:24:23Z
date_published: 2022-09-06T00:00:00Z
date_updated: 2023-02-06T09:23:31Z
day: '06'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.4230/LIPIcs.CONCUR.2022.14
ec_funded: 1
file:
- access_level: open_access
checksum: 9e97e15628f66b2ad77f535bb0327dee
content_type: application/pdf
creator: dernst
date_created: 2023-02-06T09:21:09Z
date_updated: 2023-02-06T09:21:09Z
file_id: '12520'
file_name: 2022_LIPICs_Henzinger2.pdf
file_size: 717940
relation: main_file
success: 1
file_date_updated: 2023-02-06T09:21:09Z
has_accepted_license: '1'
intvolume: ' 243'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: 14:1-14:21
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
publication: 33rd International Conference on Concurrency Theory
publication_identifier:
isbn:
- '9783959772464'
issn:
- 1868-8969
publication_status: published
publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik
quality_controlled: '1'
scopus_import: '1'
status: public
title: History-deterministic timed automata
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: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 243
year: '2022'
...
---
_id: '12509'
abstract:
- lang: eng
text: A graph game is a two-player zero-sum game in which the players move a token
throughout a graph to produce an infinite path, which determines the winner or
payoff of the game. In bidding games, both players have budgets, and in each turn,
we hold an "auction" (bidding) to determine which player moves the token. In this
survey, we consider several bidding mechanisms and their effect on the properties
of the game. Specifically, bidding games, and in particular bidding games of infinite
duration, have an intriguing equivalence with random-turn games in which in each
turn, the player who moves is chosen randomly. We summarize how minor changes
in the bidding mechanism lead to unexpected differences in the equivalence with
random-turn games.
acknowledgement: "Guy Avni: Work partially supported by the Israel Science Foundation,
ISF grant agreement\r\nno 1679/21.\r\nThomas A. Henzinger: This work was supported
in part by the ERC-2020-AdG 101020093.\r\nWe would like to thank all our collaborators
Milad Aghajohari, Ventsislav Chonev, Rasmus Ibsen-Jensen, Ismäel Jecker, Petr Novotný,
Josef Tkadlec, and Ðorđe Žikelić; we hope the collaboration was as fun and meaningful
for you as it was for us."
article_processing_charge: No
author:
- 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: 'Avni G, Henzinger TA. An updated survey of bidding games on graphs. In: 47th
International Symposium on Mathematical Foundations of Computer Science. Vol
241. Leibniz International Proceedings in Informatics (LIPIcs). Dagstuhl, Germany:
Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2022:3:1-3:6. doi:10.4230/LIPIcs.MFCS.2022.3'
apa: 'Avni, G., & Henzinger, T. A. (2022). An updated survey of bidding games
on graphs. In 47th International Symposium on Mathematical Foundations of Computer
Science (Vol. 241, p. 3:1-3:6). Dagstuhl, Germany: Schloss Dagstuhl - Leibniz-Zentrum
für Informatik. https://doi.org/10.4230/LIPIcs.MFCS.2022.3'
chicago: 'Avni, Guy, and Thomas A Henzinger. “An Updated Survey of Bidding Games
on Graphs.” In 47th International Symposium on Mathematical Foundations of
Computer Science, 241:3:1-3:6. Leibniz International Proceedings in Informatics
(LIPIcs). Dagstuhl, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik,
2022. https://doi.org/10.4230/LIPIcs.MFCS.2022.3.'
ieee: G. Avni and T. A. Henzinger, “An updated survey of bidding games on graphs,”
in 47th International Symposium on Mathematical Foundations of Computer Science,
Vienna, Austria, 2022, vol. 241, p. 3:1-3:6.
ista: 'Avni G, Henzinger TA. 2022. An updated survey of bidding games on graphs.
47th International Symposium on Mathematical Foundations of Computer Science.
MFCS: Symposium on Mathematical Foundations of Computer ScienceLeibniz International
Proceedings in Informatics (LIPIcs) vol. 241, 3:1-3:6.'
mla: Avni, Guy, and Thomas A. Henzinger. “An Updated Survey of Bidding Games on
Graphs.” 47th International Symposium on Mathematical Foundations of Computer
Science, vol. 241, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022,
p. 3:1-3:6, doi:10.4230/LIPIcs.MFCS.2022.3.
short: G. Avni, T.A. Henzinger, in:, 47th International Symposium on Mathematical
Foundations of Computer Science, Schloss Dagstuhl - Leibniz-Zentrum für Informatik,
Dagstuhl, Germany, 2022, p. 3:1-3:6.
conference:
end_date: 2022-08-26
location: Vienna, Austria
name: 'MFCS: Symposium on Mathematical Foundations of Computer Science'
start_date: 2022-08-22
date_created: 2023-02-05T17:26:01Z
date_published: 2022-08-22T00:00:00Z
date_updated: 2023-02-06T09:16:54Z
day: '22'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.4230/LIPIcs.MFCS.2022.3
ec_funded: 1
file:
- access_level: open_access
checksum: 1888ec9421622f9526fbec2de035f132
content_type: application/pdf
creator: dernst
date_created: 2023-02-06T09:13:04Z
date_updated: 2023-02-06T09:13:04Z
file_id: '12519'
file_name: 2022_LIPICs_Avni.pdf
file_size: 624586
relation: main_file
success: 1
file_date_updated: 2023-02-06T09:13:04Z
has_accepted_license: '1'
intvolume: ' 241'
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
page: 3:1-3:6
place: Dagstuhl, Germany
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
publication: 47th International Symposium on Mathematical Foundations of Computer
Science
publication_identifier:
isbn:
- '9783959772563'
issn:
- 1868-8969
publication_status: published
publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik
quality_controlled: '1'
scopus_import: '1'
series_title: Leibniz International Proceedings in Informatics (LIPIcs)
status: public
title: An updated survey of bidding games on graphs
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: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 241
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: '12511'
abstract:
- lang: eng
text: "We consider the problem of formally verifying almost-sure (a.s.) asymptotic
stability in discrete-time nonlinear stochastic control systems. While verifying
stability in deterministic control systems is extensively studied in the literature,
verifying stability in stochastic control systems is an open problem. The few
existing works on this topic either consider only specialized forms of stochasticity
or make restrictive assumptions on the system, rendering them inapplicable to
learning algorithms with neural network policies. \r\n In this work, we present
an approach for general nonlinear stochastic control problems with two novel aspects:
(a) instead of classical stochastic extensions of Lyapunov functions, we use ranking
supermartingales (RSMs) to certify a.s. asymptotic stability, and (b) we present
a method for learning neural network RSMs. \r\n We prove that our approach guarantees
a.s. asymptotic stability of the system and\r\n provides the first method to obtain
bounds on the stabilization time, which stochastic Lyapunov functions do not.\r\n
Finally, we validate our approach experimentally on a set of nonlinear stochastic
reinforcement learning environments with neural network policies."
acknowledgement: "This work was supported in part by the ERC-2020-AdG 101020093, ERC
CoG 863818 (FoRM-SMArt) and the European Union’s Horizon 2020 research and innovation
programme\r\nunder the Marie Skłodowska-Curie Grant Agreement No. 665385."
article_processing_charge: No
article_type: original
arxiv: 1
author:
- 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
- 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, Zikelic D, Chatterjee K, Henzinger TA. Stability verification in
stochastic control systems via neural network supermartingales. Proceedings
of the AAAI Conference on Artificial Intelligence. 2022;36(7):7326-7336. doi:10.1609/aaai.v36i7.20695
apa: Lechner, M., Zikelic, D., Chatterjee, K., & Henzinger, T. A. (2022). Stability
verification in stochastic control systems via neural network supermartingales.
Proceedings of the AAAI Conference on Artificial Intelligence. Association
for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v36i7.20695
chicago: Lechner, Mathias, Dorde Zikelic, Krishnendu Chatterjee, and Thomas A Henzinger.
“Stability Verification in Stochastic Control Systems via Neural Network Supermartingales.”
Proceedings of the AAAI Conference on Artificial Intelligence. Association
for the Advancement of Artificial Intelligence, 2022. https://doi.org/10.1609/aaai.v36i7.20695.
ieee: M. Lechner, D. Zikelic, K. Chatterjee, and T. A. Henzinger, “Stability verification
in stochastic control systems via neural network supermartingales,” Proceedings
of the AAAI Conference on Artificial Intelligence, vol. 36, no. 7. Association
for the Advancement of Artificial Intelligence, pp. 7326–7336, 2022.
ista: Lechner M, Zikelic D, Chatterjee K, Henzinger TA. 2022. Stability verification
in stochastic control systems via neural network supermartingales. Proceedings
of the AAAI Conference on Artificial Intelligence. 36(7), 7326–7336.
mla: Lechner, Mathias, et al. “Stability Verification in Stochastic Control Systems
via Neural Network Supermartingales.” Proceedings of the AAAI Conference on
Artificial Intelligence, vol. 36, no. 7, Association for the Advancement of
Artificial Intelligence, 2022, pp. 7326–36, doi:10.1609/aaai.v36i7.20695.
short: M. Lechner, D. Zikelic, K. Chatterjee, T.A. Henzinger, Proceedings of the
AAAI Conference on Artificial Intelligence 36 (2022) 7326–7336.
date_created: 2023-02-05T17:29:50Z
date_published: 2022-06-28T00:00:00Z
date_updated: 2025-07-14T09:09:58Z
day: '28'
department:
- _id: ToHe
- _id: KrCh
doi: 10.1609/aaai.v36i7.20695
ec_funded: 1
external_id:
arxiv:
- '2112.09495'
intvolume: ' 36'
issue: '7'
keyword:
- General Medicine
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2112.09495
month: '06'
oa: 1
oa_version: Preprint
page: 7326-7336
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
- _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 AAAI Conference on Artificial Intelligence
publication_identifier:
eissn:
- 2374-3468
isbn:
- '9781577358350'
issn:
- 2159-5399
publication_status: published
publisher: Association for the Advancement of Artificial Intelligence
quality_controlled: '1'
related_material:
record:
- id: '14539'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Stability verification in stochastic control systems via neural network supermartingales
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 36
year: '2022'
...
---
_id: '12516'
abstract:
- lang: eng
text: "The homogeneous continuous LWE (hCLWE) problem is to distinguish samples
of a specific high-dimensional Gaussian mixture from standard normal samples.
It was shown to be at least as hard as Learning with Errors, but no reduction
in the other direction is currently known.\r\nWe present four new public-key encryption
schemes based on the hardness of hCLWE, with varying tradeoffs between decryption
and security errors, and different discretization techniques. Our schemes yield
a polynomial-time algorithm for solving hCLWE using a Statistical Zero-Knowledge
oracle."
acknowledgement: "We are grateful to Devika Sharma and Luca Trevisan for their insight
and advice and to an anonymous reviewer for helpful comments.\r\n\r\nThis work was
supported by the European Research Council (ERC) under the European Union’s Horizon
2020 research and innovation programme (Grant agreement No. 101019547). The first
author was additionally supported by RGC GRF CUHK14209920 and the fourth author
was additionally supported by ISF grant No. 1399/17, project PROMETHEUS (Grant 780701),
and Cariplo CRYPTONOMEX grant."
alternative_title:
- LNCS
article_processing_charge: No
author:
- first_name: Andrej
full_name: Bogdanov, Andrej
last_name: Bogdanov
- first_name: Miguel
full_name: Cueto Noval, Miguel
id: ffc563a3-f6e0-11ea-865d-e3cce03d17cc
last_name: Cueto Noval
- first_name: Charlotte
full_name: Hoffmann, Charlotte
id: 0f78d746-dc7d-11ea-9b2f-83f92091afe7
last_name: Hoffmann
- first_name: Alon
full_name: Rosen, Alon
last_name: Rosen
citation:
ama: 'Bogdanov A, Cueto Noval M, Hoffmann C, Rosen A. Public-Key Encryption from Homogeneous
CLWE. In: Theory of Cryptography. Vol 13748. Springer Nature; 2022:565-592.
doi:10.1007/978-3-031-22365-5_20'
apa: 'Bogdanov, A., Cueto Noval, M., Hoffmann, C., & Rosen, A. (2022). Public-Key
Encryption from Homogeneous CLWE. In Theory of Cryptography (Vol. 13748,
pp. 565–592). Chicago, IL, United States: Springer Nature. https://doi.org/10.1007/978-3-031-22365-5_20'
chicago: Bogdanov, Andrej, Miguel Cueto Noval, Charlotte Hoffmann, and Alon Rosen.
“Public-Key Encryption from Homogeneous CLWE.” In Theory of Cryptography,
13748:565–92. Springer Nature, 2022. https://doi.org/10.1007/978-3-031-22365-5_20.
ieee: A. Bogdanov, M. Cueto Noval, C. Hoffmann, and A. Rosen, “Public-Key Encryption
from Homogeneous CLWE,” in Theory of Cryptography, Chicago, IL, United
States, 2022, vol. 13748, pp. 565–592.
ista: 'Bogdanov A, Cueto Noval M, Hoffmann C, Rosen A. 2022. Public-Key Encryption
from Homogeneous CLWE. Theory of Cryptography. TCC: Theory of Cryptography, LNCS,
vol. 13748, 565–592.'
mla: Bogdanov, Andrej, et al. “Public-Key Encryption from Homogeneous CLWE.” Theory
of Cryptography, vol. 13748, Springer Nature, 2022, pp. 565–92, doi:10.1007/978-3-031-22365-5_20.
short: A. Bogdanov, M. Cueto Noval, C. Hoffmann, A. Rosen, in:, Theory of Cryptography,
Springer Nature, 2022, pp. 565–592.
conference:
end_date: 2022-11-10
location: Chicago, IL, United States
name: 'TCC: Theory of Cryptography'
start_date: 2022-11-07
date_created: 2023-02-05T23:01:00Z
date_published: 2022-12-21T00:00:00Z
date_updated: 2023-08-04T10:39:30Z
day: '21'
department:
- _id: KrPi
doi: 10.1007/978-3-031-22365-5_20
external_id:
isi:
- '000921318200020'
intvolume: ' 13748'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://eprint.iacr.org/2022/093
month: '12'
oa: 1
oa_version: Preprint
page: 565-592
publication: Theory of Cryptography
publication_identifier:
eissn:
- 1611-3349
isbn:
- '9783031223648'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Public-Key Encryption from Homogeneous CLWE
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 13748
year: '2022'
...
---
_id: '12522'
abstract:
- lang: eng
text: This .zip File contains the transport data, the codes for the data analysis,
the microscopy analysis and the codes for the theoretical simulations for "Majorana-like
Coulomb spectroscopy in the absence of zero bias peaks" by M. Valentini, et. al.
The transport data are saved with hdf5 file format. The files can be open with
the log browser of Labber.
article_processing_charge: No
author:
- first_name: Marco
full_name: Valentini, Marco
id: C0BB2FAC-D767-11E9-B658-BC13E6697425
last_name: Valentini
- first_name: Pablo
full_name: San-Jose, Pablo
last_name: San-Jose
- first_name: Jordi
full_name: Arbiol, Jordi
last_name: Arbiol
- first_name: Sara
full_name: Marti-Sanchez, Sara
last_name: Marti-Sanchez
- first_name: Marc
full_name: Botifoll, Marc
last_name: Botifoll
citation:
ama: Valentini M, San-Jose P, Arbiol J, Marti-Sanchez S, Botifoll M. Data for “Majorana-like
Coulomb spectroscopy in the absence of zero bias peaks.” 2022. doi:10.15479/AT:ISTA:12102
apa: Valentini, M., San-Jose, P., Arbiol, J., Marti-Sanchez, S., & Botifoll,
M. (2022). Data for “Majorana-like Coulomb spectroscopy in the absence of zero
bias peaks.” Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:12102
chicago: Valentini, Marco, Pablo San-Jose, Jordi Arbiol, Sara Marti-Sanchez, and
Marc Botifoll. “Data for ‘Majorana-like Coulomb Spectroscopy in the Absence of
Zero Bias Peaks.’” Institute of Science and Technology Austria, 2022. https://doi.org/10.15479/AT:ISTA:12102.
ieee: M. Valentini, P. San-Jose, J. Arbiol, S. Marti-Sanchez, and M. Botifoll, “Data
for ‘Majorana-like Coulomb spectroscopy in the absence of zero bias peaks.’” Institute
of Science and Technology Austria, 2022.
ista: Valentini M, San-Jose P, Arbiol J, Marti-Sanchez S, Botifoll M. 2022. Data
for ‘Majorana-like Coulomb spectroscopy in the absence of zero bias peaks’, Institute
of Science and Technology Austria, 10.15479/AT:ISTA:12102.
mla: Valentini, Marco, et al. Data for “Majorana-like Coulomb Spectroscopy in
the Absence of Zero Bias Peaks.” Institute of Science and Technology Austria,
2022, doi:10.15479/AT:ISTA:12102.
short: M. Valentini, P. San-Jose, J. Arbiol, S. Marti-Sanchez, M. Botifoll, (2022).
contributor:
- contributor_type: contact_person
first_name: Marco
id: C0BB2FAC-D767-11E9-B658-BC13E6697425
last_name: Valentini
date_created: 2023-02-07T08:13:39Z
date_published: 2022-09-25T00:00:00Z
date_updated: 2024-02-21T12:35:34Z
day: '25'
ddc:
- '530'
department:
- _id: GeKa
doi: 10.15479/AT:ISTA:12102
file:
- access_level: open_access
checksum: 0dbd6327bf84c7e81b295c4bc9d12826
content_type: application/x-zip-compressed
creator: dernst
date_created: 2023-02-07T08:18:24Z
date_updated: 2023-02-07T08:18:24Z
file_id: '12523'
file_name: Majorana_like.zip
file_size: 3609122411
relation: main_file
success: 1
file_date_updated: 2023-02-07T08:18:24Z
has_accepted_license: '1'
month: '09'
oa: 1
oa_version: Published Version
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '12118'
relation: used_in_publication
status: public
- id: '13286'
relation: used_in_publication
status: public
status: public
title: Data for "Majorana-like Coulomb spectroscopy in the absence of zero bias peaks"
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: research_data
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '12529'
abstract:
- lang: eng
text: "We consider turn-based stochastic 2-player games on graphs with ω-regular
winning conditions. We provide a direct symbolic algorithm for solving such games
when the winning condition is formulated as a Rabin condition. For a stochastic
Rabin game with k pairs over a game graph with n vertices, our algorithm runs
in O(nk+2k!) symbolic steps, which improves the state of the art.\r\nWe have implemented
our symbolic algorithm, along with performance optimizations including parallellization
and acceleration, in a BDD-based synthesis tool called Fairsyn. We demonstrate
the superiority of Fairsyn compared to the state of the art on a set of synthetic
benchmarks derived from the VLTS benchmark suite and on a control system benchmark
from the literature. In our experiments, Fairsyn performed significantly faster
with up to two orders of magnitude improvement in computation time."
alternative_title:
- LNCS
article_processing_charge: No
author:
- first_name: Tamajit
full_name: Banerjee, Tamajit
last_name: Banerjee
- first_name: Rupak
full_name: Majumdar, Rupak
last_name: Majumdar
- first_name: Kaushik
full_name: Mallik, Kaushik
id: 0834ff3c-6d72-11ec-94e0-b5b0a4fb8598
last_name: Mallik
orcid: 0000-0001-9864-7475
- first_name: Anne-Kathrin
full_name: Schmuck, Anne-Kathrin
last_name: Schmuck
- first_name: Sadegh
full_name: Soudjani, Sadegh
last_name: Soudjani
citation:
ama: 'Banerjee T, Majumdar R, Mallik K, Schmuck A-K, Soudjani S. A direct symbolic
algorithm for solving stochastic rabin games. In: 28th International Conference
on Tools and Algorithms for the Construction and Analysis of Systems. Vol
13244. Springer Nature; 2022:81-98. doi:10.1007/978-3-030-99527-0_5'
apa: 'Banerjee, T., Majumdar, R., Mallik, K., Schmuck, A.-K., & Soudjani, S.
(2022). A direct symbolic algorithm for solving stochastic rabin games. In 28th
International Conference on Tools and Algorithms for the Construction and Analysis
of Systems (Vol. 13244, pp. 81–98). Munich, Germany: Springer Nature. https://doi.org/10.1007/978-3-030-99527-0_5'
chicago: Banerjee, Tamajit, Rupak Majumdar, Kaushik Mallik, Anne-Kathrin Schmuck,
and Sadegh Soudjani. “A Direct Symbolic Algorithm for Solving Stochastic Rabin
Games.” In 28th International Conference on Tools and Algorithms for the Construction
and Analysis of Systems, 13244:81–98. Springer Nature, 2022. https://doi.org/10.1007/978-3-030-99527-0_5.
ieee: T. Banerjee, R. Majumdar, K. Mallik, A.-K. Schmuck, and S. Soudjani, “A direct
symbolic algorithm for solving stochastic rabin games,” in 28th International
Conference on Tools and Algorithms for the Construction and Analysis of Systems,
Munich, Germany, 2022, vol. 13244, pp. 81–98.
ista: 'Banerjee T, Majumdar R, Mallik K, Schmuck A-K, Soudjani S. 2022. A direct
symbolic algorithm for solving stochastic rabin games. 28th International Conference
on Tools and Algorithms for the Construction and Analysis of Systems. TACAS: Tools
and Algorithms for the Construction and Analysis of Systems, LNCS, vol. 13244,
81–98.'
mla: Banerjee, Tamajit, et al. “A Direct Symbolic Algorithm for Solving Stochastic
Rabin Games.” 28th International Conference on Tools and Algorithms for the
Construction and Analysis of Systems, vol. 13244, Springer Nature, 2022, pp.
81–98, doi:10.1007/978-3-030-99527-0_5.
short: T. Banerjee, R. Majumdar, K. Mallik, A.-K. Schmuck, S. Soudjani, in:, 28th
International Conference on Tools and Algorithms for the Construction and Analysis
of Systems, Springer Nature, 2022, pp. 81–98.
conference:
end_date: 2022-04-07
location: Munich, Germany
name: 'TACAS: Tools and Algorithms for the Construction and Analysis of Systems'
start_date: 2022-04-02
date_created: 2023-02-08T11:43:34Z
date_published: 2022-03-29T00:00:00Z
date_updated: 2023-02-09T08:58:48Z
day: '29'
doi: 10.1007/978-3-030-99527-0_5
extern: '1'
intvolume: ' 13244'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1007/978-3-030-99527-0_5
month: '03'
oa: 1
oa_version: Published Version
page: 81-98
publication: 28th International Conference on Tools and Algorithms for the Construction
and Analysis of Systems
publication_identifier:
eisbn:
- '9783030995270'
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: A direct symbolic algorithm for solving stochastic rabin games
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13244
year: '2022'
...
---
_id: '12530'
abstract:
- lang: eng
text: We present BOCoSy, a tool for Bounded symbolic Output-feedback Controller
Synthesis. Given a specification, BOCoSy synthesizes symbolic output-feedback
controllers which interact with a given plant via a pre-defined finite symbolic
interface. BOCoSy solves this problem by a new lazy abstraction-refinement technique
which starts with a very coarse abstraction of the external trace semantics of
the given plant and iteratively removes non-admissible behavior from this abstract
model until a controller is found. BOCoSy steers the search for controllers towards
small and concise state space representations by utilizing ideas from bounded
synthesis. As a result, BOCoSy returns small and explainable controllers that
are still powerful enough to solve the given synthesis problem. We show that BOCoSy
is able to synthesize small, human readable symbolic controllers quickly on a
set of benchmarks.
article_processing_charge: No
author:
- first_name: Bernd
full_name: Finkbeiner, Bernd
last_name: Finkbeiner
- first_name: Kaushik
full_name: Mallik, Kaushik
id: 0834ff3c-6d72-11ec-94e0-b5b0a4fb8598
last_name: Mallik
orcid: 0000-0001-9864-7475
- first_name: Noemi
full_name: Passing, Noemi
last_name: Passing
- first_name: Malte
full_name: Schledjewski, Malte
last_name: Schledjewski
- first_name: Anne-Kathrin
full_name: Schmuck, Anne-Kathrin
last_name: Schmuck
citation:
ama: 'Finkbeiner B, Mallik K, Passing N, Schledjewski M, Schmuck A-K. BOCoSy: Small
but powerful symbolic output-feedback control. In: 25th ACM International Conference
on Hybrid Systems: Computation and Control. ACM; 2022:24:1-24:11. doi:10.1145/3501710.3519535'
apa: 'Finkbeiner, B., Mallik, K., Passing, N., Schledjewski, M., & Schmuck,
A.-K. (2022). BOCoSy: Small but powerful symbolic output-feedback control. In
25th ACM International Conference on Hybrid Systems: Computation and Control
(p. 24:1-24:11). Milan, Italy: ACM. https://doi.org/10.1145/3501710.3519535'
chicago: 'Finkbeiner, Bernd, Kaushik Mallik, Noemi Passing, Malte Schledjewski,
and Anne-Kathrin Schmuck. “BOCoSy: Small but Powerful Symbolic Output-Feedback
Control.” In 25th ACM International Conference on Hybrid Systems: Computation
and Control, 24:1-24:11. ACM, 2022. https://doi.org/10.1145/3501710.3519535.'
ieee: 'B. Finkbeiner, K. Mallik, N. Passing, M. Schledjewski, and A.-K. Schmuck,
“BOCoSy: Small but powerful symbolic output-feedback control,” in 25th ACM
International Conference on Hybrid Systems: Computation and Control, Milan,
Italy, 2022, p. 24:1-24:11.'
ista: 'Finkbeiner B, Mallik K, Passing N, Schledjewski M, Schmuck A-K. 2022. BOCoSy:
Small but powerful symbolic output-feedback control. 25th ACM International Conference
on Hybrid Systems: Computation and Control. HSCC: International Conference on
Hybrid Systems Computation and Control, 24:1-24:11.'
mla: 'Finkbeiner, Bernd, et al. “BOCoSy: Small but Powerful Symbolic Output-Feedback
Control.” 25th ACM International Conference on Hybrid Systems: Computation
and Control, ACM, 2022, p. 24:1-24:11, doi:10.1145/3501710.3519535.'
short: 'B. Finkbeiner, K. Mallik, N. Passing, M. Schledjewski, A.-K. Schmuck, in:,
25th ACM International Conference on Hybrid Systems: Computation and Control,
ACM, 2022, p. 24:1-24:11.'
conference:
end_date: 2022-05-06
location: Milan, Italy
name: 'HSCC: International Conference on Hybrid Systems Computation and Control'
start_date: 2022-05-04
date_created: 2023-02-08T11:43:50Z
date_published: 2022-05-01T00:00:00Z
date_updated: 2023-02-09T08:53:13Z
day: '01'
doi: 10.1145/3501710.3519535
extern: '1'
language:
- iso: eng
month: '05'
oa_version: None
page: 24:1-24:11
publication: '25th ACM International Conference on Hybrid Systems: Computation and
Control'
publication_identifier:
isbn:
- '9781450391962'
publication_status: published
publisher: ACM
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'BOCoSy: Small but powerful symbolic output-feedback control'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '12536'
abstract:
- lang: eng
text: 'We consider the problem of estimating a rank-1 signal corrupted by structured
rotationally invariant noise, and address the following question: how well do
inference algorithms perform when the noise statistics is unknown and hence Gaussian
noise is assumed? While the matched Bayes-optimal setting with unstructured noise
is well understood, the analysis of this mismatched problem is only at its premises.
In this paper, we make a step towards understanding the effect of the strong source
of mismatch which is the noise statistics. Our main technical contribution is
the rigorous analysis of a Bayes estimator and of an approximate message passing
(AMP) algorithm, both of which incorrectly assume a Gaussian setup. The first
result exploits the theory of spherical integrals and of low-rank matrix perturbations;
the idea behind the second one is to design and analyze an artificial AMP which,
by taking advantage of the flexibility in the denoisers, is able to "correct"
the mismatch. Armed with these sharp asymptotic characterizations, we unveil a
rich and often unexpected phenomenology. For example, despite AMP is in principle
designed to efficiently compute the Bayes estimator, the former is outperformed
by the latter in terms of mean-square error. We show that this performance gap
is due to an incorrect estimation of the signal norm. In fact, when the SNR is
large enough, the overlaps of the AMP and the Bayes estimator coincide, and they
even match those of optimal estimators taking into account the structure of the
noise.'
article_number: '2205.10009'
article_processing_charge: No
arxiv: 1
author:
- first_name: Jean
full_name: Barbier, Jean
last_name: Barbier
- first_name: TianQi
full_name: Hou, TianQi
last_name: Hou
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
- first_name: Manuel
full_name: Saenz, Manuel
last_name: Saenz
citation:
ama: 'Barbier J, Hou T, Mondelli M, Saenz M. The price of ignorance: How much does
it cost to forget noise structure in low-rank matrix estimation? arXiv.
doi:10.48550/arXiv.2205.10009'
apa: 'Barbier, J., Hou, T., Mondelli, M., & Saenz, M. (n.d.). The price of ignorance:
How much does it cost to forget noise structure in low-rank matrix estimation?
arXiv. https://doi.org/10.48550/arXiv.2205.10009'
chicago: 'Barbier, Jean, TianQi Hou, Marco Mondelli, and Manuel Saenz. “The Price
of Ignorance: How Much Does It Cost to Forget Noise Structure in Low-Rank Matrix
Estimation?” ArXiv, n.d. https://doi.org/10.48550/arXiv.2205.10009.'
ieee: 'J. Barbier, T. Hou, M. Mondelli, and M. Saenz, “The price of ignorance: How
much does it cost to forget noise structure in low-rank matrix estimation?,” arXiv.
.'
ista: 'Barbier J, Hou T, Mondelli M, Saenz M. The price of ignorance: How much does
it cost to forget noise structure in low-rank matrix estimation? arXiv, 2205.10009.'
mla: 'Barbier, Jean, et al. “The Price of Ignorance: How Much Does It Cost to Forget
Noise Structure in Low-Rank Matrix Estimation?” ArXiv, 2205.10009, doi:10.48550/arXiv.2205.10009.'
short: J. Barbier, T. Hou, M. Mondelli, M. Saenz, ArXiv (n.d.).
date_created: 2023-02-10T13:45:41Z
date_published: 2022-05-20T00:00:00Z
date_updated: 2023-02-16T09:41:25Z
day: '20'
department:
- _id: MaMo
doi: 10.48550/arXiv.2205.10009
external_id:
arxiv:
- '2205.10009'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2205.10009
month: '05'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: accepted
status: public
title: 'The price of ignorance: How much does it cost to forget noise structure in
low-rank matrix estimation?'
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '12537'
abstract:
- lang: eng
text: 'The Neural Tangent Kernel (NTK) has emerged as a powerful tool to provide
memorization, optimization and generalization guarantees in deep neural networks.
A line of work has studied the NTK spectrum for two-layer and deep networks with
at least a layer with Ω(N) neurons, N being the number of training samples. Furthermore,
there is increasing evidence suggesting that deep networks with sub-linear layer
widths are powerful memorizers and optimizers, as long as the number of parameters
exceeds the number of samples. Thus, a natural open question is whether the NTK
is well conditioned in such a challenging sub-linear setup. In this paper, we
answer this question in the affirmative. Our key technical contribution is a lower
bound on the smallest NTK eigenvalue for deep networks with the minimum possible
over-parameterization: the number of parameters is roughly Ω(N) and, hence, the
number of neurons is as little as Ω(N−−√). To showcase the applicability of our
NTK bounds, we provide two results concerning memorization capacity and optimization
guarantees for gradient descent training.'
acknowledgement: "The authors were partially supported by the 2019 Lopez-Loreta prize,
and they would like to thank\r\nQuynh Nguyen, Mahdi Soltanolkotabi and Adel Javanmard
for helpful discussions.\r\n"
article_processing_charge: No
arxiv: 1
author:
- first_name: Simone
full_name: Bombari, Simone
id: ca726dda-de17-11ea-bc14-f9da834f63aa
last_name: Bombari
- first_name: Mohammad Hossein
full_name: Amani, Mohammad Hossein
last_name: Amani
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
citation:
ama: 'Bombari S, Amani MH, Mondelli M. Memorization and optimization in deep neural
networks with minimum over-parameterization. In: 36th Conference on Neural
Information Processing Systems. Vol 35. Curran Associates; 2022:7628-7640.'
apa: Bombari, S., Amani, M. H., & Mondelli, M. (2022). Memorization and optimization
in deep neural networks with minimum over-parameterization. In 36th Conference
on Neural Information Processing Systems (Vol. 35, pp. 7628–7640). Curran
Associates.
chicago: Bombari, Simone, Mohammad Hossein Amani, and Marco Mondelli. “Memorization
and Optimization in Deep Neural Networks with Minimum Over-Parameterization.”
In 36th Conference on Neural Information Processing Systems, 35:7628–40.
Curran Associates, 2022.
ieee: S. Bombari, M. H. Amani, and M. Mondelli, “Memorization and optimization in
deep neural networks with minimum over-parameterization,” in 36th Conference
on Neural Information Processing Systems, 2022, vol. 35, pp. 7628–7640.
ista: Bombari S, Amani MH, Mondelli M. 2022. Memorization and optimization in deep
neural networks with minimum over-parameterization. 36th Conference on Neural
Information Processing Systems. vol. 35, 7628–7640.
mla: Bombari, Simone, et al. “Memorization and Optimization in Deep Neural Networks
with Minimum Over-Parameterization.” 36th Conference on Neural Information
Processing Systems, vol. 35, Curran Associates, 2022, pp. 7628–40.
short: S. Bombari, M.H. Amani, M. Mondelli, in:, 36th Conference on Neural Information
Processing Systems, Curran Associates, 2022, pp. 7628–7640.
date_created: 2023-02-10T13:46:37Z
date_published: 2022-07-24T00:00:00Z
date_updated: 2024-09-10T13:03:19Z
day: '24'
department:
- _id: MaMo
external_id:
arxiv:
- '2205.10217'
intvolume: ' 35'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2205.10217'
month: '07'
oa: 1
oa_version: Preprint
page: 7628-7640
project:
- _id: 059876FA-7A3F-11EA-A408-12923DDC885E
name: Prix Lopez-Loretta 2019 - Marco Mondelli
publication: 36th Conference on Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713871088'
publication_status: published
publisher: Curran Associates
quality_controlled: '1'
status: public
title: Memorization and optimization in deep neural networks with minimum over-parameterization
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 35
year: '2022'
...
---
_id: '12538'
abstract:
- lang: eng
text: In this paper, we study the compression of a target two-layer neural network
with N nodes into a compressed network with MIEEE Information Theory Workshop.
2022:588-593. doi:10.1109/ITW54588.2022.9965870
apa: 'Amani, M. H., Bombari, S., Mondelli, M., Pukdee, R., & Rini, S. (2022).
Sharp asymptotics on the compression of two-layer neural networks. IEEE Information
Theory Workshop. Mumbai, India: IEEE. https://doi.org/10.1109/ITW54588.2022.9965870'
chicago: Amani, Mohammad Hossein, Simone Bombari, Marco Mondelli, Rattana Pukdee,
and Stefano Rini. “Sharp Asymptotics on the Compression of Two-Layer Neural Networks.”
IEEE Information Theory Workshop. IEEE, 2022. https://doi.org/10.1109/ITW54588.2022.9965870.
ieee: M. H. Amani, S. Bombari, M. Mondelli, R. Pukdee, and S. Rini, “Sharp asymptotics
on the compression of two-layer neural networks,” IEEE Information Theory Workshop.
IEEE, pp. 588–593, 2022.
ista: Amani MH, Bombari S, Mondelli M, Pukdee R, Rini S. 2022. Sharp asymptotics
on the compression of two-layer neural networks. IEEE Information Theory Workshop.,
588–593.
mla: Amani, Mohammad Hossein, et al. “Sharp Asymptotics on the Compression of Two-Layer
Neural Networks.” IEEE Information Theory Workshop, IEEE, 2022, pp. 588–93,
doi:10.1109/ITW54588.2022.9965870.
short: M.H. Amani, S. Bombari, M. Mondelli, R. Pukdee, S. Rini, IEEE Information
Theory Workshop (2022) 588–593.
conference:
end_date: 2022-11-09
location: Mumbai, India
name: 'ITW: Information Theory Workshop'
start_date: 2022-11-01
date_created: 2023-02-10T13:47:56Z
date_published: 2022-11-16T00:00:00Z
date_updated: 2023-12-18T11:31:47Z
day: '16'
department:
- _id: MaMo
doi: 10.1109/ITW54588.2022.9965870
external_id:
arxiv:
- '2205.08199'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2205.08199'
month: '11'
oa: 1
oa_version: Preprint
page: 588-593
publication: IEEE Information Theory Workshop
publication_identifier:
isbn:
- '9781665483414'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Sharp asymptotics on the compression of two-layer neural networks
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '12540'
abstract:
- lang: eng
text: We consider the problem of signal estimation in generalized linear models
defined via rotationally invariant design matrices. Since these matrices can have
an arbitrary spectral distribution, this model is well suited for capturing complex
correlation structures which often arise in applications. We propose a novel family
of approximate message passing (AMP) algorithms for signal estimation, and rigorously
characterize their performance in the high-dimensional limit via a state evolution
recursion. Our rotationally invariant AMP has complexity of the same order as
the existing AMP derived under the restrictive assumption of a Gaussian design;
our algorithm also recovers this existing AMP as a special case. Numerical results
showcase a performance close to Vector AMP (which is conjectured to be Bayes-optimal
in some settings), but obtained with a much lower complexity, as the proposed
algorithm does not require a computationally expensive singular value decomposition.
acknowledgement: The authors would like to thank the anonymous reviewers for their
helpful comments. KK and MM were partially supported by the 2019 Lopez-Loreta Prize.
article_number: '22'
article_processing_charge: No
author:
- first_name: Ramji
full_name: Venkataramanan, Ramji
last_name: Venkataramanan
- first_name: Kevin
full_name: Kögler, Kevin
id: 94ec913c-dc85-11ea-9058-e5051ab2428b
last_name: Kögler
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
citation:
ama: 'Venkataramanan R, Kögler K, Mondelli M. Estimation in rotationally invariant
generalized linear models via approximate message passing. In: Proceedings
of the 39th International Conference on Machine Learning. Vol 162. ML Research
Press; 2022.'
apa: 'Venkataramanan, R., Kögler, K., & Mondelli, M. (2022). Estimation in rotationally
invariant generalized linear models via approximate message passing. In Proceedings
of the 39th International Conference on Machine Learning (Vol. 162). Baltimore,
MD, United States: ML Research Press.'
chicago: Venkataramanan, Ramji, Kevin Kögler, and Marco Mondelli. “Estimation in
Rotationally Invariant Generalized Linear Models via Approximate Message Passing.”
In Proceedings of the 39th International Conference on Machine Learning,
Vol. 162. ML Research Press, 2022.
ieee: R. Venkataramanan, K. Kögler, and M. Mondelli, “Estimation in rotationally
invariant generalized linear models via approximate message passing,” in Proceedings
of the 39th International Conference on Machine Learning, Baltimore, MD, United
States, 2022, vol. 162.
ista: 'Venkataramanan R, Kögler K, Mondelli M. 2022. Estimation in rotationally
invariant generalized linear models via approximate message passing. Proceedings
of the 39th International Conference on Machine Learning. ICML: International
Conference on Machine Learning vol. 162, 22.'
mla: Venkataramanan, Ramji, et al. “Estimation in Rotationally Invariant Generalized
Linear Models via Approximate Message Passing.” Proceedings of the 39th International
Conference on Machine Learning, vol. 162, 22, ML Research Press, 2022.
short: R. Venkataramanan, K. Kögler, M. Mondelli, in:, Proceedings of the 39th International
Conference on Machine Learning, ML Research Press, 2022.
conference:
end_date: 2022-07-23
location: Baltimore, MD, United States
name: 'ICML: International Conference on Machine Learning'
start_date: 2022-07-17
date_created: 2023-02-10T13:49:04Z
date_published: 2022-01-01T00:00:00Z
date_updated: 2024-09-10T13:03:17Z
ddc:
- '000'
department:
- _id: MaMo
file:
- access_level: open_access
checksum: 67436eb0a660789514cdf9db79e84683
content_type: application/pdf
creator: dernst
date_created: 2023-02-13T10:53:11Z
date_updated: 2023-02-13T10:53:11Z
file_id: '12547'
file_name: 2022_PMLR_Venkataramanan.pdf
file_size: 2341343
relation: main_file
success: 1
file_date_updated: 2023-02-13T10:53:11Z
has_accepted_license: '1'
intvolume: ' 162'
language:
- iso: eng
oa: 1
oa_version: Published Version
project:
- _id: 059876FA-7A3F-11EA-A408-12923DDC885E
name: Prix Lopez-Loretta 2019 - Marco Mondelli
publication: Proceedings of the 39th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
status: public
title: Estimation in rotationally invariant generalized linear models via approximate
message passing
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 162
year: '2022'
...
---
_id: '12568'
abstract:
- lang: eng
text: We treat the problem of risk-aware control for stochastic shortest path (SSP)
on Markov decision processes (MDP). Typically, expectation is considered for SSP,
which however is oblivious to the incurred risk. We present an alternative view,
instead optimizing conditional value-at-risk (CVaR), an established risk measure.
We treat both Markov chains as well as MDP and introduce, through novel insights,
two algorithms, based on linear programming and value iteration, respectively.
Both algorithms offer precise and provably correct solutions. Evaluation of our
prototype implementation shows that risk-aware control is feasible on several
moderately sized models.
article_processing_charge: No
arxiv: 1
author:
- first_name: Tobias
full_name: Meggendorfer, Tobias
id: b21b0c15-30a2-11eb-80dc-f13ca25802e1
last_name: Meggendorfer
orcid: 0000-0002-1712-2165
citation:
ama: 'Meggendorfer T. Risk-aware stochastic shortest path. In: Proceedings of
the 36th AAAI Conference on Artificial Intelligence, AAAI 2022. Vol 36. Association
for the Advancement of Artificial Intelligence; 2022:9858-9867. doi:10.1609/aaai.v36i9.21222'
apa: 'Meggendorfer, T. (2022). Risk-aware stochastic shortest path. In Proceedings
of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022 (Vol. 36,
pp. 9858–9867). Virtual: Association for the Advancement of Artificial Intelligence.
https://doi.org/10.1609/aaai.v36i9.21222'
chicago: Meggendorfer, Tobias. “Risk-Aware Stochastic Shortest Path.” In Proceedings
of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022, 36:9858–67.
Association for the Advancement of Artificial Intelligence, 2022. https://doi.org/10.1609/aaai.v36i9.21222.
ieee: T. Meggendorfer, “Risk-aware stochastic shortest path,” in Proceedings
of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022, Virtual,
2022, vol. 36, no. 9, pp. 9858–9867.
ista: Meggendorfer T. 2022. Risk-aware stochastic shortest path. Proceedings of
the 36th AAAI Conference on Artificial Intelligence, AAAI 2022. Conference on
Artificial Intelligence vol. 36, 9858–9867.
mla: Meggendorfer, Tobias. “Risk-Aware Stochastic Shortest Path.” Proceedings
of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022, vol. 36,
no. 9, Association for the Advancement of Artificial Intelligence, 2022, pp. 9858–67,
doi:10.1609/aaai.v36i9.21222.
short: T. Meggendorfer, in:, Proceedings of the 36th AAAI Conference on Artificial
Intelligence, AAAI 2022, Association for the Advancement of Artificial Intelligence,
2022, pp. 9858–9867.
conference:
end_date: 2022-03-01
location: Virtual
name: Conference on Artificial Intelligence
start_date: 2022-02-22
date_created: 2023-02-19T23:00:56Z
date_published: 2022-06-28T00:00:00Z
date_updated: 2023-02-20T07:19:12Z
day: '28'
department:
- _id: KrCh
doi: 10.1609/aaai.v36i9.21222
external_id:
arxiv:
- '2203.01640'
intvolume: ' 36'
issue: '9'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2203.01640'
month: '06'
oa: 1
oa_version: Preprint
page: 9858-9867
publication: Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI
2022
publication_identifier:
eissn:
- 2374-3468
isbn:
- '1577358767'
publication_status: published
publisher: Association for the Advancement of Artificial Intelligence
quality_controlled: '1'
scopus_import: '1'
status: public
title: Risk-aware stochastic shortest path
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 36
year: '2022'
...