---
_id: '14889'
abstract:
- lang: eng
text: We consider the Fröhlich Hamiltonian with large coupling constant α. For initial
data of Pekar product form with coherent phonon field and with the electron minimizing
the corresponding energy, we provide a norm approximation of the evolution, valid
up to times of order α2. The approximation is given in terms of a Pekar product
state, evolved through the Landau-Pekar equations, corrected by a Bogoliubov dynamics
taking quantum fluctuations into account. This allows us to show that the Landau-Pekar
equations approximately describe the evolution of the electron- and one-phonon
reduced density matrices under the Fröhlich dynamics up to times of order α2.
acknowledgement: "Financial support by the European Union’s Horizon 2020 research
and innovation programme\r\nunder the Marie Skłodowska-Curie grant agreement No.
754411 (S.R.) and the European\r\nResearch Council under grant agreement No. 694227
(N.L. and R.S.), as well as by the SNSF\r\nEccellenza project PCEFP2 181153 (N.L.),
the NCCR SwissMAP (N.L. and B.S.) and by the\r\nDeutsche Forschungsgemeinschaft
(DFG) through the Research Training Group 1838: Spectral\r\nTheory and Dynamics
of Quantum Systems (D.M.) is gratefully acknowledged. B.S. gratefully\r\nacknowledges
financial support from the Swiss National Science Foundation through the Grant\r\n“Dynamical
and energetic properties of Bose-Einstein condensates” and from the European\r\nResearch
Council through the ERC-AdG CLaQS (grant agreement No 834782). D.M. thanks\r\nMarcel
Griesemer for helpful discussions."
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Nikolai K
full_name: Leopold, Nikolai K
id: 4BC40BEC-F248-11E8-B48F-1D18A9856A87
last_name: Leopold
orcid: 0000-0002-0495-6822
- first_name: David Johannes
full_name: Mitrouskas, David Johannes
id: cbddacee-2b11-11eb-a02e-a2e14d04e52d
last_name: Mitrouskas
- first_name: Simone Anna Elvira
full_name: Rademacher, Simone Anna Elvira
id: 856966FE-A408-11E9-977E-802DE6697425
last_name: Rademacher
orcid: 0000-0001-5059-4466
- first_name: Benjamin
full_name: Schlein, Benjamin
last_name: Schlein
- first_name: Robert
full_name: Seiringer, Robert
id: 4AFD0470-F248-11E8-B48F-1D18A9856A87
last_name: Seiringer
orcid: 0000-0002-6781-0521
citation:
ama: Leopold NK, Mitrouskas DJ, Rademacher SAE, Schlein B, Seiringer R. Landau–Pekar
equations and quantum fluctuations for the dynamics of a strongly coupled polaron.
Pure and Applied Analysis. 2021;3(4):653-676. doi:10.2140/paa.2021.3.653
apa: Leopold, N. K., Mitrouskas, D. J., Rademacher, S. A. E., Schlein, B., &
Seiringer, R. (2021). Landau–Pekar equations and quantum fluctuations for the
dynamics of a strongly coupled polaron. Pure and Applied Analysis. Mathematical
Sciences Publishers. https://doi.org/10.2140/paa.2021.3.653
chicago: Leopold, Nikolai K, David Johannes Mitrouskas, Simone Anna Elvira Rademacher,
Benjamin Schlein, and Robert Seiringer. “Landau–Pekar Equations and Quantum Fluctuations
for the Dynamics of a Strongly Coupled Polaron.” Pure and Applied Analysis.
Mathematical Sciences Publishers, 2021. https://doi.org/10.2140/paa.2021.3.653.
ieee: N. K. Leopold, D. J. Mitrouskas, S. A. E. Rademacher, B. Schlein, and R. Seiringer,
“Landau–Pekar equations and quantum fluctuations for the dynamics of a strongly
coupled polaron,” Pure and Applied Analysis, vol. 3, no. 4. Mathematical
Sciences Publishers, pp. 653–676, 2021.
ista: Leopold NK, Mitrouskas DJ, Rademacher SAE, Schlein B, Seiringer R. 2021. Landau–Pekar
equations and quantum fluctuations for the dynamics of a strongly coupled polaron.
Pure and Applied Analysis. 3(4), 653–676.
mla: Leopold, Nikolai K., et al. “Landau–Pekar Equations and Quantum Fluctuations
for the Dynamics of a Strongly Coupled Polaron.” Pure and Applied Analysis,
vol. 3, no. 4, Mathematical Sciences Publishers, 2021, pp. 653–76, doi:10.2140/paa.2021.3.653.
short: N.K. Leopold, D.J. Mitrouskas, S.A.E. Rademacher, B. Schlein, R. Seiringer,
Pure and Applied Analysis 3 (2021) 653–676.
date_created: 2024-01-28T23:01:43Z
date_published: 2021-10-01T00:00:00Z
date_updated: 2024-02-05T10:02:45Z
day: '01'
department:
- _id: RoSe
doi: 10.2140/paa.2021.3.653
ec_funded: 1
external_id:
arxiv:
- '2005.02098'
intvolume: ' 3'
issue: '4'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2005.02098
month: '10'
oa: 1
oa_version: Preprint
page: 653-676
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
- _id: 25C6DC12-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '694227'
name: Analysis of quantum many-body systems
publication: Pure and Applied Analysis
publication_identifier:
eissn:
- 2578-5885
issn:
- 2578-5893
publication_status: published
publisher: Mathematical Sciences Publishers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Landau–Pekar equations and quantum fluctuations for the dynamics of a strongly
coupled polaron
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 3
year: '2021'
...
---
_id: '14890'
abstract:
- lang: eng
text: We consider a system of N interacting bosons in the mean-field scaling regime
and construct corrections to the Bogoliubov dynamics that approximate the true
N-body dynamics in norm to arbitrary precision. The N-independent corrections
are given in terms of the solutions of the Bogoliubov and Hartree equations and
satisfy a generalized form of Wick's theorem. We determine the n-point correlation
functions of the excitations around the condensate, as well as the reduced densities
of the N-body system, to arbitrary accuracy, given only the knowledge of the two-point
functions of a quasi-free state and the solution of the Hartree equation. In this
way, the complex problem of computing all n-point correlation functions for an
interacting N-body system is essentially reduced to the problem of solving the
Hartree equation and the PDEs for the Bogoliubov two-point functions.
acknowledgement: "We are grateful for the hospitality of Central China Normal University
(CCNU),\r\nwhere parts of this work were done, and thank Phan Th`anh Nam, Simone\r\nRademacher,
Robert Seiringer and Stefan Teufel for helpful discussions. L.B. gratefully acknowledges
the support by the German Research Foundation (DFG) within the Research\r\nTraining
Group 1838 “Spectral Theory and Dynamics of Quantum Systems”, and the funding\r\nfrom
the European Union’s Horizon 2020 research and innovation programme under the Marie\r\nSk
lodowska-Curie Grant Agreement No. 754411."
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Lea
full_name: Bossmann, Lea
id: A2E3BCBE-5FCC-11E9-AA4B-76F3E5697425
last_name: Bossmann
orcid: 0000-0002-6854-1343
- first_name: Sören P
full_name: Petrat, Sören P
id: 40AC02DC-F248-11E8-B48F-1D18A9856A87
last_name: Petrat
orcid: 0000-0002-9166-5889
- first_name: Peter
full_name: Pickl, Peter
last_name: Pickl
- first_name: Avy
full_name: Soffer, Avy
last_name: Soffer
citation:
ama: Bossmann L, Petrat SP, Pickl P, Soffer A. Beyond Bogoliubov dynamics. Pure
and Applied Analysis. 2021;3(4):677-726. doi:10.2140/paa.2021.3.677
apa: Bossmann, L., Petrat, S. P., Pickl, P., & Soffer, A. (2021). Beyond Bogoliubov
dynamics. Pure and Applied Analysis. Mathematical Sciences Publishers.
https://doi.org/10.2140/paa.2021.3.677
chicago: Bossmann, Lea, Sören P Petrat, Peter Pickl, and Avy Soffer. “Beyond Bogoliubov
Dynamics.” Pure and Applied Analysis. Mathematical Sciences Publishers,
2021. https://doi.org/10.2140/paa.2021.3.677.
ieee: L. Bossmann, S. P. Petrat, P. Pickl, and A. Soffer, “Beyond Bogoliubov dynamics,”
Pure and Applied Analysis, vol. 3, no. 4. Mathematical Sciences Publishers,
pp. 677–726, 2021.
ista: Bossmann L, Petrat SP, Pickl P, Soffer A. 2021. Beyond Bogoliubov dynamics.
Pure and Applied Analysis. 3(4), 677–726.
mla: Bossmann, Lea, et al. “Beyond Bogoliubov Dynamics.” Pure and Applied Analysis,
vol. 3, no. 4, Mathematical Sciences Publishers, 2021, pp. 677–726, doi:10.2140/paa.2021.3.677.
short: L. Bossmann, S.P. Petrat, P. Pickl, A. Soffer, Pure and Applied Analysis
3 (2021) 677–726.
date_created: 2024-01-28T23:01:43Z
date_published: 2021-10-01T00:00:00Z
date_updated: 2024-02-05T09:26:31Z
day: '01'
department:
- _id: RoSe
doi: 10.2140/paa.2021.3.677
ec_funded: 1
external_id:
arxiv:
- '1912.11004'
intvolume: ' 3'
issue: '4'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1912.11004
month: '10'
oa: 1
oa_version: Preprint
page: 677-726
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
publication: Pure and Applied Analysis
publication_identifier:
eissn:
- 2578-5885
issn:
- 2578-5893
publication_status: published
publisher: Mathematical Sciences Publishers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Beyond Bogoliubov dynamics
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 3
year: '2021'
...
---
_id: '14984'
abstract:
- lang: eng
text: Hybrid zones are narrow geographic regions where different populations, races
or interbreeding species meet and mate, producing mixed ‘hybrid’ offspring. They
are relatively common and can be found in a diverse range of organisms and environments.
The study of hybrid zones has played an important role in our understanding of
the origin of species, with hybrid zones having been described as ‘natural laboratories’.
This is because they allow us to study,in situ, the conditions and evolutionary
forces that enable divergent taxa to remain distinct despite some ongoing gene
exchange between them.
article_processing_charge: No
author:
- first_name: Sean
full_name: Stankowski, Sean
id: 43161670-5719-11EA-8025-FABC3DDC885E
last_name: Stankowski
- first_name: Daria
full_name: Shipilina, Daria
id: 428A94B0-F248-11E8-B48F-1D18A9856A87
last_name: Shipilina
orcid: 0000-0002-1145-9226
- first_name: Anja M
full_name: Westram, Anja M
id: 3C147470-F248-11E8-B48F-1D18A9856A87
last_name: Westram
orcid: 0000-0003-1050-4969
citation:
ama: 'Stankowski S, Shipilina D, Westram AM. Hybrid Zones. In: Encyclopedia of
Life Sciences. Vol 2. eLS. Wiley; 2021. doi:10.1002/9780470015902.a0029355'
apa: Stankowski, S., Shipilina, D., & Westram, A. M. (2021). Hybrid Zones. In
Encyclopedia of Life Sciences (Vol. 2). Wiley. https://doi.org/10.1002/9780470015902.a0029355
chicago: Stankowski, Sean, Daria Shipilina, and Anja M Westram. “Hybrid Zones.”
In Encyclopedia of Life Sciences, Vol. 2. ELS. Wiley, 2021. https://doi.org/10.1002/9780470015902.a0029355.
ieee: S. Stankowski, D. Shipilina, and A. M. Westram, “Hybrid Zones,” in Encyclopedia
of Life Sciences, vol. 2, Wiley, 2021.
ista: 'Stankowski S, Shipilina D, Westram AM. 2021.Hybrid Zones. In: Encyclopedia
of Life Sciences. vol. 2.'
mla: Stankowski, Sean, et al. “Hybrid Zones.” Encyclopedia of Life Sciences,
vol. 2, Wiley, 2021, doi:10.1002/9780470015902.a0029355.
short: S. Stankowski, D. Shipilina, A.M. Westram, in:, Encyclopedia of Life Sciences,
Wiley, 2021.
date_created: 2024-02-14T12:05:50Z
date_published: 2021-05-28T00:00:00Z
date_updated: 2024-02-19T09:54:18Z
day: '28'
department:
- _id: NiBa
doi: 10.1002/9780470015902.a0029355
intvolume: ' 2'
language:
- iso: eng
month: '05'
oa_version: None
publication: Encyclopedia of Life Sciences
publication_identifier:
eisbn:
- '9780470015902'
isbn:
- '9780470016176'
publication_status: published
publisher: Wiley
quality_controlled: '1'
series_title: eLS
status: public
title: Hybrid Zones
type: book_chapter
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2
year: '2021'
...
---
_id: '14987'
abstract:
- lang: eng
text: "The goal of zero-shot learning is to construct a classifier that can identify
object classes for which no training examples are available. When training data
for some of the object classes is available but not for others, the name generalized
zero-shot learning is commonly used.\r\nIn a wider sense, the phrase zero-shot
is also used to describe other machine learning-based approaches that require
no training data from the problem of interest, such as zero-shot action recognition
or zero-shot machine translation."
article_processing_charge: No
author:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Lampert C. Zero-Shot Learning. In: Ikeuchi K, ed. Computer Vision.
2nd ed. Cham: Springer; 2021:1395-1397. doi:10.1007/978-3-030-63416-2_874'
apa: 'Lampert, C. (2021). Zero-Shot Learning. In K. Ikeuchi (Ed.), Computer Vision
(2nd ed., pp. 1395–1397). Cham: Springer. https://doi.org/10.1007/978-3-030-63416-2_874'
chicago: 'Lampert, Christoph. “Zero-Shot Learning.” In Computer Vision, edited
by Katsushi Ikeuchi, 2nd ed., 1395–97. Cham: Springer, 2021. https://doi.org/10.1007/978-3-030-63416-2_874.'
ieee: 'C. Lampert, “Zero-Shot Learning,” in Computer Vision, 2nd ed., K.
Ikeuchi, Ed. Cham: Springer, 2021, pp. 1395–1397.'
ista: 'Lampert C. 2021.Zero-Shot Learning. In: Computer Vision. , 1395–1397.'
mla: Lampert, Christoph. “Zero-Shot Learning.” Computer Vision, edited by
Katsushi Ikeuchi, 2nd ed., Springer, 2021, pp. 1395–97, doi:10.1007/978-3-030-63416-2_874.
short: C. Lampert, in:, K. Ikeuchi (Ed.), Computer Vision, 2nd ed., Springer, Cham,
2021, pp. 1395–1397.
date_created: 2024-02-14T14:05:32Z
date_published: 2021-10-13T00:00:00Z
date_updated: 2024-02-19T10:59:04Z
day: '13'
department:
- _id: ChLa
doi: 10.1007/978-3-030-63416-2_874
edition: '2'
editor:
- first_name: Katsushi
full_name: Ikeuchi, Katsushi
last_name: Ikeuchi
language:
- iso: eng
month: '10'
oa_version: None
page: 1395-1397
place: Cham
publication: Computer Vision
publication_identifier:
eisbn:
- '9783030634162'
isbn:
- '9783030634155'
publication_status: published
publisher: Springer
quality_controlled: '1'
status: public
title: Zero-Shot Learning
type: book_chapter
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '14988'
abstract:
- lang: eng
text: Raw data generated from the publication - The TPLATE complex mediates membrane
bending during plant clathrin-mediated endocytosis by Johnson et al., 2021 In
PNAS
article_processing_charge: No
author:
- first_name: Alexander J
full_name: Johnson, Alexander J
id: 46A62C3A-F248-11E8-B48F-1D18A9856A87
last_name: Johnson
orcid: 0000-0002-2739-8843
citation:
ama: Johnson AJ. Raw data from Johnson et al, PNAS, 2021. 2021. doi:10.5281/ZENODO.5747100
apa: Johnson, A. J. (2021). Raw data from Johnson et al, PNAS, 2021. Zenodo. https://doi.org/10.5281/ZENODO.5747100
chicago: Johnson, Alexander J. “Raw Data from Johnson et Al, PNAS, 2021.” Zenodo,
2021. https://doi.org/10.5281/ZENODO.5747100.
ieee: A. J. Johnson, “Raw data from Johnson et al, PNAS, 2021.” Zenodo, 2021.
ista: Johnson AJ. 2021. Raw data from Johnson et al, PNAS, 2021, Zenodo, 10.5281/ZENODO.5747100.
mla: Johnson, Alexander J. Raw Data from Johnson et Al, PNAS, 2021. Zenodo,
2021, doi:10.5281/ZENODO.5747100.
short: A.J. Johnson, (2021).
date_created: 2024-02-14T14:13:48Z
date_published: 2021-12-01T00:00:00Z
date_updated: 2024-02-19T11:06:09Z
day: '01'
ddc:
- '580'
department:
- _id: JiFr
doi: 10.5281/ZENODO.5747100
has_accepted_license: '1'
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.5747100
month: '12'
oa: 1
oa_version: Published Version
publisher: Zenodo
related_material:
record:
- id: '9887'
relation: used_in_publication
status: public
status: public
title: Raw data from Johnson et al, PNAS, 2021
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_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '15013'
abstract:
- lang: eng
text: We consider random n×n matrices X with independent and centered entries and
a general variance profile. We show that the spectral radius of X converges with
very high probability to the square root of the spectral radius of the variance
matrix of X when n tends to infinity. We also establish the optimal rate of convergence,
that is a new result even for general i.i.d. matrices beyond the explicitly solvable
Gaussian cases. The main ingredient is the proof of the local inhomogeneous circular
law [arXiv:1612.07776] at the spectral edge.
acknowledgement: Partially supported by ERC Starting Grant RandMat No. 715539 and
the SwissMap grant of Swiss National Science Foundation. Partially supported by
ERC Advanced Grant RanMat No. 338804. Partially supported by the Hausdorff Center
for Mathematics in Bonn.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Johannes
full_name: Alt, Johannes
id: 36D3D8B6-F248-11E8-B48F-1D18A9856A87
last_name: Alt
- first_name: László
full_name: Erdös, László
id: 4DBD5372-F248-11E8-B48F-1D18A9856A87
last_name: Erdös
orcid: 0000-0001-5366-9603
- first_name: Torben H
full_name: Krüger, Torben H
id: 3020C786-F248-11E8-B48F-1D18A9856A87
last_name: Krüger
orcid: 0000-0002-4821-3297
citation:
ama: Alt J, Erdös L, Krüger TH. Spectral radius of random matrices with independent
entries. Probability and Mathematical Physics. 2021;2(2):221-280. doi:10.2140/pmp.2021.2.221
apa: Alt, J., Erdös, L., & Krüger, T. H. (2021). Spectral radius of random matrices
with independent entries. Probability and Mathematical Physics. Mathematical
Sciences Publishers. https://doi.org/10.2140/pmp.2021.2.221
chicago: Alt, Johannes, László Erdös, and Torben H Krüger. “Spectral Radius of Random
Matrices with Independent Entries.” Probability and Mathematical Physics.
Mathematical Sciences Publishers, 2021. https://doi.org/10.2140/pmp.2021.2.221.
ieee: J. Alt, L. Erdös, and T. H. Krüger, “Spectral radius of random matrices with
independent entries,” Probability and Mathematical Physics, vol. 2, no.
2. Mathematical Sciences Publishers, pp. 221–280, 2021.
ista: Alt J, Erdös L, Krüger TH. 2021. Spectral radius of random matrices with independent
entries. Probability and Mathematical Physics. 2(2), 221–280.
mla: Alt, Johannes, et al. “Spectral Radius of Random Matrices with Independent
Entries.” Probability and Mathematical Physics, vol. 2, no. 2, Mathematical
Sciences Publishers, 2021, pp. 221–80, doi:10.2140/pmp.2021.2.221.
short: J. Alt, L. Erdös, T.H. Krüger, Probability and Mathematical Physics 2 (2021)
221–280.
date_created: 2024-02-18T23:01:03Z
date_published: 2021-05-21T00:00:00Z
date_updated: 2024-02-19T08:30:00Z
day: '21'
department:
- _id: LaEr
doi: 10.2140/pmp.2021.2.221
ec_funded: 1
external_id:
arxiv:
- '1907.13631'
intvolume: ' 2'
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1907.13631
month: '05'
oa: 1
oa_version: Preprint
page: 221-280
project:
- _id: 258DCDE6-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '338804'
name: Random matrices, universality and disordered quantum systems
publication: Probability and Mathematical Physics
publication_identifier:
eissn:
- 2690-1005
issn:
- 2690-0998
publication_status: published
publisher: Mathematical Sciences Publishers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Spectral radius of random matrices with independent entries
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2
year: '2021'
...
---
_id: '14117'
abstract:
- lang: eng
text: 'The two fields of machine learning and graphical causality arose and are
developed separately. However, there is, now, cross-pollination and increasing
interest in both fields to benefit from the advances of the other. In this article,
we review fundamental concepts of causal inference and relate them to crucial
open problems of machine learning, including transfer and generalization, thereby
assaying how causality can contribute to modern machine learning research. This
also applies in the opposite direction: we note that most work in causality starts
from the premise that the causal variables are given. A central problem for AI
and causality is, thus, causal representation learning, that is, the discovery
of high-level causal variables from low-level observations. Finally, we delineate
some implications of causality for machine learning and propose key research areas
at the intersection of both communities.'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Bernhard
full_name: Scholkopf, Bernhard
last_name: Scholkopf
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Stefan
full_name: Bauer, Stefan
last_name: Bauer
- first_name: Nan Rosemary
full_name: Ke, Nan Rosemary
last_name: Ke
- first_name: Nal
full_name: Kalchbrenner, Nal
last_name: Kalchbrenner
- first_name: Anirudh
full_name: Goyal, Anirudh
last_name: Goyal
- first_name: Yoshua
full_name: Bengio, Yoshua
last_name: Bengio
citation:
ama: Scholkopf B, Locatello F, Bauer S, et al. Toward causal representation learning.
Proceedings of the IEEE. 2021;109(5):612-634. doi:10.1109/jproc.2021.3058954
apa: Scholkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal,
A., & Bengio, Y. (2021). Toward causal representation learning. Proceedings
of the IEEE. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/jproc.2021.3058954
chicago: Scholkopf, Bernhard, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke,
Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio. “Toward Causal Representation
Learning.” Proceedings of the IEEE. Institute of Electrical and Electronics
Engineers, 2021. https://doi.org/10.1109/jproc.2021.3058954.
ieee: B. Scholkopf et al., “Toward causal representation learning,” Proceedings
of the IEEE, vol. 109, no. 5. Institute of Electrical and Electronics Engineers,
pp. 612–634, 2021.
ista: Scholkopf B, Locatello F, Bauer S, Ke NR, Kalchbrenner N, Goyal A, Bengio
Y. 2021. Toward causal representation learning. Proceedings of the IEEE. 109(5),
612–634.
mla: Scholkopf, Bernhard, et al. “Toward Causal Representation Learning.” Proceedings
of the IEEE, vol. 109, no. 5, Institute of Electrical and Electronics Engineers,
2021, pp. 612–34, doi:10.1109/jproc.2021.3058954.
short: B. Scholkopf, F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal,
Y. Bengio, Proceedings of the IEEE 109 (2021) 612–634.
date_created: 2023-08-21T12:19:30Z
date_published: 2021-05-01T00:00:00Z
date_updated: 2023-09-11T11:43:35Z
day: '01'
department:
- _id: FrLo
doi: 10.1109/jproc.2021.3058954
extern: '1'
external_id:
arxiv:
- '2102.11107'
intvolume: ' 109'
issue: '5'
keyword:
- Electrical and Electronic Engineering
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1109/JPROC.2021.3058954
month: '05'
oa: 1
oa_version: Published Version
page: 612-634
publication: Proceedings of the IEEE
publication_identifier:
eissn:
- 1558-2256
issn:
- 0018-9219
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Toward causal representation learning
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 109
year: '2021'
...
---
_id: '14176'
abstract:
- lang: eng
text: "Intensive care units (ICU) are increasingly looking towards machine learning
for methods to provide online monitoring of critically ill patients. In machine
learning, online monitoring is often formulated as a supervised learning problem.
Recently, contrastive learning approaches have demonstrated promising improvements
over competitive supervised benchmarks. These methods rely on well-understood
data augmentation techniques developed for image data which do not apply to online
monitoring. In this work, we overcome this limitation by\r\nsupplementing time-series
data augmentation techniques with a novel contrastive\r\nlearning objective which
we call neighborhood contrastive learning (NCL). Our objective explicitly groups
together contiguous time segments from each patient while maintaining state-specific
information. Our experiments demonstrate a marked improvement over existing work
applying contrastive methods to medical time-series."
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Hugo
full_name: Yèche, Hugo
last_name: Yèche
- first_name: Gideon
full_name: Dresdner, Gideon
last_name: Dresdner
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Matthias
full_name: Hüser, Matthias
last_name: Hüser
- first_name: Gunnar
full_name: Rätsch, Gunnar
last_name: Rätsch
citation:
ama: 'Yèche H, Dresdner G, Locatello F, Hüser M, Rätsch G. Neighborhood contrastive
learning applied to online patient monitoring. In: Proceedings of 38th International
Conference on Machine Learning. Vol 139. ML Research Press; 2021:11964-11974.'
apa: 'Yèche, H., Dresdner, G., Locatello, F., Hüser, M., & Rätsch, G. (2021).
Neighborhood contrastive learning applied to online patient monitoring. In Proceedings
of 38th International Conference on Machine Learning (Vol. 139, pp. 11964–11974).
Virtual: ML Research Press.'
chicago: Yèche, Hugo, Gideon Dresdner, Francesco Locatello, Matthias Hüser, and
Gunnar Rätsch. “Neighborhood Contrastive Learning Applied to Online Patient Monitoring.”
In Proceedings of 38th International Conference on Machine Learning, 139:11964–74.
ML Research Press, 2021.
ieee: H. Yèche, G. Dresdner, F. Locatello, M. Hüser, and G. Rätsch, “Neighborhood
contrastive learning applied to online patient monitoring,” in Proceedings
of 38th International Conference on Machine Learning, Virtual, 2021, vol.
139, pp. 11964–11974.
ista: Yèche H, Dresdner G, Locatello F, Hüser M, Rätsch G. 2021. Neighborhood contrastive
learning applied to online patient monitoring. Proceedings of 38th International
Conference on Machine Learning. International Conference on Machine Learning,
PMLR, vol. 139, 11964–11974.
mla: Yèche, Hugo, et al. “Neighborhood Contrastive Learning Applied to Online Patient
Monitoring.” Proceedings of 38th International Conference on Machine Learning,
vol. 139, ML Research Press, 2021, pp. 11964–74.
short: H. Yèche, G. Dresdner, F. Locatello, M. Hüser, G. Rätsch, in:, Proceedings
of 38th International Conference on Machine Learning, ML Research Press, 2021,
pp. 11964–11974.
conference:
end_date: 2021-07-24
location: Virtual
name: International Conference on Machine Learning
start_date: 2021-07-18
date_created: 2023-08-22T14:03:04Z
date_published: 2021-08-01T00:00:00Z
date_updated: 2023-09-11T10:16:55Z
day: '01'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2106.05142'
intvolume: ' 139'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2106.05142
month: '08'
oa: 1
oa_version: Preprint
page: 11964-11974
publication: Proceedings of 38th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Neighborhood contrastive learning applied to online patient monitoring
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 139
year: '2021'
...
---
_id: '14177'
abstract:
- lang: eng
text: "The focus of disentanglement approaches has been on identifying independent
factors of variation in data. However, the causal variables underlying real-world
observations are often not statistically independent. In this work, we bridge
the gap to real-world scenarios by analyzing the behavior of the most prominent
disentanglement approaches on correlated data in a large-scale empirical study
(including 4260 models). We show and quantify that systematically induced correlations
in the dataset are being learned and reflected in the latent representations,
which has implications for downstream applications of disentanglement such as
fairness. We also demonstrate how to resolve these latent correlations, either
using weak supervision during\r\ntraining or by post-hoc correcting a pre-trained
model with a small number of labels."
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Frederik
full_name: Träuble, Frederik
last_name: Träuble
- first_name: Elliot
full_name: Creager, Elliot
last_name: Creager
- first_name: Niki
full_name: Kilbertus, Niki
last_name: Kilbertus
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Andrea
full_name: Dittadi, Andrea
last_name: Dittadi
- first_name: Anirudh
full_name: Goyal, Anirudh
last_name: Goyal
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Stefan
full_name: Bauer, Stefan
last_name: Bauer
citation:
ama: 'Träuble F, Creager E, Kilbertus N, et al. On disentangled representations
learned from correlated data. In: Proceedings of the 38th International Conference
on Machine Learning. Vol 139. ML Research Press; 2021:10401-10412.'
apa: 'Träuble, F., Creager, E., Kilbertus, N., Locatello, F., Dittadi, A., Goyal,
A., … Bauer, S. (2021). On disentangled representations learned from correlated
data. In Proceedings of the 38th International Conference on Machine Learning
(Vol. 139, pp. 10401–10412). Virtual: ML Research Press.'
chicago: Träuble, Frederik, Elliot Creager, Niki Kilbertus, Francesco Locatello,
Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, and Stefan Bauer. “On Disentangled
Representations Learned from Correlated Data.” In Proceedings of the 38th International
Conference on Machine Learning, 139:10401–12. ML Research Press, 2021.
ieee: F. Träuble et al., “On disentangled representations learned from correlated
data,” in Proceedings of the 38th International Conference on Machine Learning,
Virtual, 2021, vol. 139, pp. 10401–10412.
ista: 'Träuble F, Creager E, Kilbertus N, Locatello F, Dittadi A, Goyal A, Schölkopf
B, Bauer S. 2021. On disentangled representations learned from correlated data.
Proceedings of the 38th International Conference on Machine Learning. ICML: International
Conference on Machine Learning, PMLR, vol. 139, 10401–10412.'
mla: Träuble, Frederik, et al. “On Disentangled Representations Learned from Correlated
Data.” Proceedings of the 38th International Conference on Machine Learning,
vol. 139, ML Research Press, 2021, pp. 10401–12.
short: F. Träuble, E. Creager, N. Kilbertus, F. Locatello, A. Dittadi, A. Goyal,
B. Schölkopf, S. Bauer, in:, Proceedings of the 38th International Conference
on Machine Learning, ML Research Press, 2021, pp. 10401–10412.
conference:
end_date: 2021-07-24
location: Virtual
name: 'ICML: International Conference on Machine Learning'
start_date: 2021-07-18
date_created: 2023-08-22T14:03:47Z
date_published: 2021-08-01T00:00:00Z
date_updated: 2023-09-11T10:18:48Z
day: '01'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2006.07886'
intvolume: ' 139'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2006.07886
month: '08'
oa: 1
oa_version: Published Version
page: 10401-10412
publication: Proceedings of the 38th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: On disentangled representations learned from correlated data
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 139
year: '2021'
...
---
_id: '14178'
abstract:
- lang: eng
text: Learning meaningful representations that disentangle the underlying structure
of the data generating process is considered to be of key importance in machine
learning. While disentangled representations were found to be useful for diverse
tasks such as abstract reasoning and fair classification, their scalability and
real-world impact remain questionable. We introduce a new high-resolution dataset
with 1M simulated images and over 1,800 annotated real-world images of the same
setup. In contrast to previous work, this new dataset exhibits correlations, a
complex underlying structure, and allows to evaluate transfer to unseen simulated
and real-world settings where the encoder i) remains in distribution or ii) is
out of distribution. We propose new architectures in order to scale disentangled
representation learning to realistic high-resolution settings and conduct a large-scale
empirical study of disentangled representations on this dataset. We observe that
disentanglement is a good predictor for out-of-distribution (OOD) task performance.
article_processing_charge: No
arxiv: 1
author:
- first_name: Andrea
full_name: Dittadi, Andrea
last_name: Dittadi
- first_name: Frederik
full_name: Träuble, Frederik
last_name: Träuble
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Manuel
full_name: Wüthrich, Manuel
last_name: Wüthrich
- first_name: Vaibhav
full_name: Agrawal, Vaibhav
last_name: Agrawal
- first_name: Ole
full_name: Winther, Ole
last_name: Winther
- first_name: Stefan
full_name: Bauer, Stefan
last_name: Bauer
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
citation:
ama: 'Dittadi A, Träuble F, Locatello F, et al. On the transfer of disentangled
representations in realistic settings. In: The Ninth International Conference
on Learning Representations. ; 2021.'
apa: Dittadi, A., Träuble, F., Locatello, F., Wüthrich, M., Agrawal, V., Winther,
O., … Schölkopf, B. (2021). On the transfer of disentangled representations in
realistic settings. In The Ninth International Conference on Learning Representations.
Virtual.
chicago: Dittadi, Andrea, Frederik Träuble, Francesco Locatello, Manuel Wüthrich,
Vaibhav Agrawal, Ole Winther, Stefan Bauer, and Bernhard Schölkopf. “On the Transfer
of Disentangled Representations in Realistic Settings.” In The Ninth International
Conference on Learning Representations, 2021.
ieee: A. Dittadi et al., “On the transfer of disentangled representations
in realistic settings,” in The Ninth International Conference on Learning Representations,
Virtual, 2021.
ista: 'Dittadi A, Träuble F, Locatello F, Wüthrich M, Agrawal V, Winther O, Bauer
S, Schölkopf B. 2021. On the transfer of disentangled representations in realistic
settings. The Ninth International Conference on Learning Representations. ICLR:
International Conference on Learning Representations.'
mla: Dittadi, Andrea, et al. “On the Transfer of Disentangled Representations in
Realistic Settings.” The Ninth International Conference on Learning Representations,
2021.
short: A. Dittadi, F. Träuble, F. Locatello, M. Wüthrich, V. Agrawal, O. Winther,
S. Bauer, B. Schölkopf, in:, The Ninth International Conference on Learning Representations,
2021.
conference:
end_date: 2021-05-07
location: Virtual
name: 'ICLR: International Conference on Learning Representations'
start_date: 2021-05-03
date_created: 2023-08-22T14:04:16Z
date_published: 2021-05-04T00:00:00Z
date_updated: 2023-09-11T10:55:30Z
day: '04'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2010.14407'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2010.14407
month: '05'
oa: 1
oa_version: Preprint
publication: The Ninth International Conference on Learning Representations
publication_status: published
quality_controlled: '1'
status: public
title: On the transfer of disentangled representations in realistic settings
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '14179'
abstract:
- lang: eng
text: Self-supervised representation learning has shown remarkable success in a
number of domains. A common practice is to perform data augmentation via hand-crafted
transformations intended to leave the semantics of the data invariant. We seek
to understand the empirical success of this approach from a theoretical perspective.
We formulate the augmentation process as a latent variable model by postulating
a partition of the latent representation into a content component, which is assumed
invariant to augmentation, and a style component, which is allowed to change.
Unlike prior work on disentanglement and independent component analysis, we allow
for both nontrivial statistical and causal dependencies in the latent space. We
study the identifiability of the latent representation based on pairs of views
of the observations and prove sufficient conditions that allow us to identify
the invariant content partition up to an invertible mapping in both generative
and discriminative settings. We find numerical simulations with dependent latent
variables are consistent with our theory. Lastly, we introduce Causal3DIdent,
a dataset of high-dimensional, visually complex images with rich causal dependencies,
which we use to study the effect of data augmentations performed in practice.
article_processing_charge: No
arxiv: 1
author:
- first_name: Julius von
full_name: Kügelgen, Julius von
last_name: Kügelgen
- first_name: Yash
full_name: Sharma, Yash
last_name: Sharma
- first_name: Luigi
full_name: Gresele, Luigi
last_name: Gresele
- first_name: Wieland
full_name: Brendel, Wieland
last_name: Brendel
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Michel
full_name: Besserve, Michel
last_name: Besserve
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Kügelgen J von, Sharma Y, Gresele L, et al. Self-supervised learning with
data augmentations provably isolates content from style. In: Advances in Neural
Information Processing Systems. Vol 34. ; 2021:16451-16467.'
apa: Kügelgen, J. von, Sharma, Y., Gresele, L., Brendel, W., Schölkopf, B., Besserve,
M., & Locatello, F. (2021). Self-supervised learning with data augmentations
provably isolates content from style. In Advances in Neural Information Processing
Systems (Vol. 34, pp. 16451–16467). Virtual.
chicago: Kügelgen, Julius von, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard
Schölkopf, Michel Besserve, and Francesco Locatello. “Self-Supervised Learning
with Data Augmentations Provably Isolates Content from Style.” In Advances
in Neural Information Processing Systems, 34:16451–67, 2021.
ieee: J. von Kügelgen et al., “Self-supervised learning with data augmentations
provably isolates content from style,” in Advances in Neural Information Processing
Systems, Virtual, 2021, vol. 34, pp. 16451–16467.
ista: 'Kügelgen J von, Sharma Y, Gresele L, Brendel W, Schölkopf B, Besserve M,
Locatello F. 2021. Self-supervised learning with data augmentations provably isolates
content from style. Advances in Neural Information Processing Systems. NeurIPS:
Neural Information Processing Systems vol. 34, 16451–16467.'
mla: Kügelgen, Julius von, et al. “Self-Supervised Learning with Data Augmentations
Provably Isolates Content from Style.” Advances in Neural Information Processing
Systems, vol. 34, 2021, pp. 16451–67.
short: J. von Kügelgen, Y. Sharma, L. Gresele, W. Brendel, B. Schölkopf, M. Besserve,
F. Locatello, in:, Advances in Neural Information Processing Systems, 2021, pp.
16451–16467.
conference:
end_date: 2021-12-10
location: Virtual
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2021-12-07
date_created: 2023-08-22T14:04:36Z
date_published: 2021-06-08T00:00:00Z
date_updated: 2023-09-11T10:33:19Z
day: '08'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2106.04619'
intvolume: ' 34'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2106.04619
month: '06'
oa: 1
oa_version: Preprint
page: 16451-16467
publication: Advances in Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713845393'
publication_status: published
quality_controlled: '1'
status: public
title: Self-supervised learning with data augmentations provably isolates content
from style
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 34
year: '2021'
...
---
_id: '14180'
abstract:
- lang: eng
text: 'Modern neural network architectures can leverage large amounts of data to
generalize well within the training distribution. However, they are less capable
of systematic generalization to data drawn from unseen but related distributions,
a feat that is hypothesized to require compositional reasoning and reuse of knowledge.
In this work, we present Neural Interpreters, an architecture that factorizes
inference in a self-attention network as a system of modules, which we call \emph{functions}.
Inputs to the model are routed through a sequence of functions in a way that is
end-to-end learned. The proposed architecture can flexibly compose computation
along width and depth, and lends itself well to capacity extension after training.
To demonstrate the versatility of Neural Interpreters, we evaluate it in two distinct
settings: image classification and visual abstract reasoning on Raven Progressive
Matrices. In the former, we show that Neural Interpreters perform on par with
the vision transformer using fewer parameters, while being transferrable to a
new task in a sample efficient manner. In the latter, we find that Neural Interpreters
are competitive with respect to the state-of-the-art in terms of systematic generalization. '
article_processing_charge: No
arxiv: 1
author:
- first_name: Nasim
full_name: Rahaman, Nasim
last_name: Rahaman
- first_name: Muhammad Waleed
full_name: Gondal, Muhammad Waleed
last_name: Gondal
- first_name: Shruti
full_name: Joshi, Shruti
last_name: Joshi
- first_name: Peter
full_name: Gehler, Peter
last_name: Gehler
- first_name: Yoshua
full_name: Bengio, Yoshua
last_name: Bengio
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
citation:
ama: 'Rahaman N, Gondal MW, Joshi S, et al. Dynamic inference with neural interpreters.
In: Advances in Neural Information Processing Systems. Vol 34. ; 2021:10985-10998.'
apa: Rahaman, N., Gondal, M. W., Joshi, S., Gehler, P., Bengio, Y., Locatello, F.,
& Schölkopf, B. (2021). Dynamic inference with neural interpreters. In Advances
in Neural Information Processing Systems (Vol. 34, pp. 10985–10998). Virtual.
chicago: Rahaman, Nasim, Muhammad Waleed Gondal, Shruti Joshi, Peter Gehler, Yoshua
Bengio, Francesco Locatello, and Bernhard Schölkopf. “Dynamic Inference with Neural
Interpreters.” In Advances in Neural Information Processing Systems, 34:10985–98,
2021.
ieee: N. Rahaman et al., “Dynamic inference with neural interpreters,” in
Advances in Neural Information Processing Systems, Virtual, 2021, vol.
34, pp. 10985–10998.
ista: 'Rahaman N, Gondal MW, Joshi S, Gehler P, Bengio Y, Locatello F, Schölkopf
B. 2021. Dynamic inference with neural interpreters. Advances in Neural Information
Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 10985–10998.'
mla: Rahaman, Nasim, et al. “Dynamic Inference with Neural Interpreters.” Advances
in Neural Information Processing Systems, vol. 34, 2021, pp. 10985–98.
short: N. Rahaman, M.W. Gondal, S. Joshi, P. Gehler, Y. Bengio, F. Locatello, B.
Schölkopf, in:, Advances in Neural Information Processing Systems, 2021, pp. 10985–10998.
conference:
end_date: 2021-12-10
location: Virtual
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2021-12-07
date_created: 2023-08-22T14:04:55Z
date_published: 2021-10-12T00:00:00Z
date_updated: 2023-09-11T11:33:46Z
day: '12'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2110.06399'
intvolume: ' 34'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2110.06399
month: '10'
oa: 1
oa_version: Preprint
page: 10985-10998
publication: Advances in Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713845393'
publication_status: published
quality_controlled: '1'
status: public
title: Dynamic inference with neural interpreters
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 34
year: '2021'
...
---
_id: '14181'
abstract:
- lang: eng
text: Variational Inference makes a trade-off between the capacity of the variational
family and the tractability of finding an approximate posterior distribution.
Instead, Boosting Variational Inference allows practitioners to obtain increasingly
good posterior approximations by spending more compute. The main obstacle to widespread
adoption of Boosting Variational Inference is the amount of resources necessary
to improve over a strong Variational Inference baseline. In our work, we trace
this limitation back to the global curvature of the KL-divergence. We characterize
how the global curvature impacts time and memory consumption, address the problem
with the notion of local curvature, and provide a novel approximate backtracking
algorithm for estimating local curvature. We give new theoretical convergence
rates for our algorithms and provide experimental validation on synthetic and
real-world datasets.
article_processing_charge: No
arxiv: 1
author:
- first_name: Gideon
full_name: Dresdner, Gideon
last_name: Dresdner
- first_name: Saurav
full_name: Shekhar, Saurav
last_name: Shekhar
- first_name: Fabian
full_name: Pedregosa, Fabian
last_name: Pedregosa
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Gunnar
full_name: Rätsch, Gunnar
last_name: Rätsch
citation:
ama: 'Dresdner G, Shekhar S, Pedregosa F, Locatello F, Rätsch G. Boosting variational
inference with locally adaptive step-sizes. In: Proceedings of the Thirtieth
International Joint Conference on Artificial Intelligence. International Joint
Conferences on Artificial Intelligence; 2021:2337-2343. doi:10.24963/ijcai.2021/322'
apa: 'Dresdner, G., Shekhar, S., Pedregosa, F., Locatello, F., & Rätsch, G.
(2021). Boosting variational inference with locally adaptive step-sizes. In Proceedings
of the Thirtieth International Joint Conference on Artificial Intelligence
(pp. 2337–2343). Montreal, Canada: International Joint Conferences on Artificial
Intelligence. https://doi.org/10.24963/ijcai.2021/322'
chicago: Dresdner, Gideon, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello,
and Gunnar Rätsch. “Boosting Variational Inference with Locally Adaptive Step-Sizes.”
In Proceedings of the Thirtieth International Joint Conference on Artificial
Intelligence, 2337–43. International Joint Conferences on Artificial Intelligence,
2021. https://doi.org/10.24963/ijcai.2021/322.
ieee: G. Dresdner, S. Shekhar, F. Pedregosa, F. Locatello, and G. Rätsch, “Boosting
variational inference with locally adaptive step-sizes,” in Proceedings of
the Thirtieth International Joint Conference on Artificial Intelligence, Montreal,
Canada, 2021, pp. 2337–2343.
ista: 'Dresdner G, Shekhar S, Pedregosa F, Locatello F, Rätsch G. 2021. Boosting
variational inference with locally adaptive step-sizes. Proceedings of the Thirtieth
International Joint Conference on Artificial Intelligence. IJCAI: International
Joint Conference on Artificial Intelligence, 2337–2343.'
mla: Dresdner, Gideon, et al. “Boosting Variational Inference with Locally Adaptive
Step-Sizes.” Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, International Joint Conferences on Artificial Intelligence,
2021, pp. 2337–43, doi:10.24963/ijcai.2021/322.
short: G. Dresdner, S. Shekhar, F. Pedregosa, F. Locatello, G. Rätsch, in:, Proceedings
of the Thirtieth International Joint Conference on Artificial Intelligence, International
Joint Conferences on Artificial Intelligence, 2021, pp. 2337–2343.
conference:
end_date: 2021-08-27
location: Montreal, Canada
name: 'IJCAI: International Joint Conference on Artificial Intelligence'
start_date: 2021-08-19
date_created: 2023-08-22T14:05:14Z
date_published: 2021-05-19T00:00:00Z
date_updated: 2023-09-11T11:14:30Z
day: '19'
department:
- _id: FrLo
doi: 10.24963/ijcai.2021/322
extern: '1'
external_id:
arxiv:
- '2105.09240'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2105.09240
month: '05'
oa: 1
oa_version: Published Version
page: 2337-2343
publication: Proceedings of the Thirtieth International Joint Conference on Artificial
Intelligence
publication_identifier:
eisbn:
- '9780999241196'
publication_status: published
publisher: International Joint Conferences on Artificial Intelligence
quality_controlled: '1'
status: public
title: Boosting variational inference with locally adaptive step-sizes
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '14182'
abstract:
- lang: eng
text: "When machine learning systems meet real world applications, accuracy is only\r\none
of several requirements. In this paper, we assay a complementary\r\nperspective
originating from the increasing availability of pre-trained and\r\nregularly improving
state-of-the-art models. While new improved models develop\r\nat a fast pace,
downstream tasks vary more slowly or stay constant. Assume that\r\nwe have a large
unlabelled data set for which we want to maintain accurate\r\npredictions. Whenever
a new and presumably better ML models becomes available,\r\nwe encounter two problems:
(i) given a limited budget, which data points should\r\nbe re-evaluated using
the new model?; and (ii) if the new predictions differ\r\nfrom the current ones,
should we update? Problem (i) is about compute cost,\r\nwhich matters for very
large data sets and models. Problem (ii) is about\r\nmaintaining consistency of
the predictions, which can be highly relevant for\r\ndownstream applications;
our demand is to avoid negative flips, i.e., changing\r\ncorrect to incorrect
predictions. In this paper, we formalize the Prediction\r\nUpdate Problem and
present an efficient probabilistic approach as answer to the\r\nabove questions.
In extensive experiments on standard classification benchmark\r\ndata sets, we
show that our method outperforms alternative strategies along key\r\nmetrics for
backward-compatible prediction updates."
article_processing_charge: No
arxiv: 1
author:
- first_name: Frederik
full_name: Träuble, Frederik
last_name: Träuble
- first_name: Julius von
full_name: Kügelgen, Julius von
last_name: Kügelgen
- first_name: Matthäus
full_name: Kleindessner, Matthäus
last_name: Kleindessner
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Peter
full_name: Gehler, Peter
last_name: Gehler
citation:
ama: 'Träuble F, Kügelgen J von, Kleindessner M, Locatello F, Schölkopf B, Gehler
P. Backward-compatible prediction updates: A probabilistic approach. In: 35th
Conference on Neural Information Processing Systems. Vol 34. ; 2021:116-128.'
apa: 'Träuble, F., Kügelgen, J. von, Kleindessner, M., Locatello, F., Schölkopf,
B., & Gehler, P. (2021). Backward-compatible prediction updates: A probabilistic
approach. In 35th Conference on Neural Information Processing Systems (Vol.
34, pp. 116–128). Virtual.'
chicago: 'Träuble, Frederik, Julius von Kügelgen, Matthäus Kleindessner, Francesco
Locatello, Bernhard Schölkopf, and Peter Gehler. “Backward-Compatible Prediction
Updates: A Probabilistic Approach.” In 35th Conference on Neural Information
Processing Systems, 34:116–28, 2021.'
ieee: 'F. Träuble, J. von Kügelgen, M. Kleindessner, F. Locatello, B. Schölkopf,
and P. Gehler, “Backward-compatible prediction updates: A probabilistic approach,”
in 35th Conference on Neural Information Processing Systems, Virtual, 2021,
vol. 34, pp. 116–128.'
ista: 'Träuble F, Kügelgen J von, Kleindessner M, Locatello F, Schölkopf B, Gehler
P. 2021. Backward-compatible prediction updates: A probabilistic approach. 35th
Conference on Neural Information Processing Systems. NeurIPS: Neural Information
Processing Systems vol. 34, 116–128.'
mla: 'Träuble, Frederik, et al. “Backward-Compatible Prediction Updates: A Probabilistic
Approach.” 35th Conference on Neural Information Processing Systems, vol.
34, 2021, pp. 116–28.'
short: F. Träuble, J. von Kügelgen, M. Kleindessner, F. Locatello, B. Schölkopf,
P. Gehler, in:, 35th Conference on Neural Information Processing Systems, 2021,
pp. 116–128.
conference:
end_date: 2021-12-10
location: Virtual
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2021-12-07
date_created: 2023-08-22T14:05:41Z
date_published: 2021-07-02T00:00:00Z
date_updated: 2023-09-11T11:31:59Z
day: '02'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2107.01057'
intvolume: ' 34'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2107.01057
month: '07'
oa: 1
oa_version: Preprint
page: 116-128
publication: 35th Conference on Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713845393'
publication_status: published
quality_controlled: '1'
status: public
title: 'Backward-compatible prediction updates: A probabilistic approach'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 34
year: '2021'
...
---
_id: '14221'
abstract:
- lang: eng
text: 'The world is structured in countless ways. It may be prudent to enforce corresponding
structural properties to a learning algorithm''s solution, such as incorporating
prior beliefs, natural constraints, or causal structures. Doing so may translate
to faster, more accurate, and more flexible models, which may directly relate
to real-world impact. In this dissertation, we consider two different research
areas that concern structuring a learning algorithm''s solution: when the structure
is known and when it has to be discovered.'
article_number: '2111.13693'
article_processing_charge: No
arxiv: 1
author:
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: Locatello F. Enforcing and discovering structure in machine learning. arXiv.
doi:10.48550/arXiv.2111.13693
apa: Locatello, F. (n.d.). Enforcing and discovering structure in machine learning.
arXiv. https://doi.org/10.48550/arXiv.2111.13693
chicago: Locatello, Francesco. “Enforcing and Discovering Structure in Machine Learning.”
ArXiv, n.d. https://doi.org/10.48550/arXiv.2111.13693.
ieee: F. Locatello, “Enforcing and discovering structure in machine learning,” arXiv.
.
ista: Locatello F. Enforcing and discovering structure in machine learning. arXiv,
2111.13693.
mla: Locatello, Francesco. “Enforcing and Discovering Structure in Machine Learning.”
ArXiv, 2111.13693, doi:10.48550/arXiv.2111.13693.
short: F. Locatello, ArXiv (n.d.).
date_created: 2023-08-22T14:23:35Z
date_published: 2021-11-26T00:00:00Z
date_updated: 2023-09-12T07:04:44Z
day: '26'
department:
- _id: FrLo
doi: 10.48550/arXiv.2111.13693
extern: '1'
external_id:
arxiv:
- '2111.13693'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2111.13693
month: '11'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Enforcing and discovering structure in machine learning
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '14332'
abstract:
- lang: eng
text: Learning data representations that are useful for various downstream tasks
is a cornerstone of artificial intelligence. While existing methods are typically
evaluated on downstream tasks such as classification or generative image quality,
we propose to assess representations through their usefulness in downstream control
tasks, such as reaching or pushing objects. By training over 10,000 reinforcement
learning policies, we extensively evaluate to what extent different representation
properties affect out-of-distribution (OOD) generalization. Finally, we demonstrate
zero-shot transfer of these policies from simulation to the real world, without
any domain randomization or fine-tuning. This paper aims to establish the first
systematic characterization of the usefulness of learned representations for real-world
OOD downstream tasks.
article_processing_charge: No
author:
- first_name: Frederik
full_name: Träuble, Frederik
last_name: Träuble
- first_name: Andrea
full_name: Dittadi, Andrea
last_name: Dittadi
- first_name: Manuel
full_name: Wuthrich, Manuel
last_name: Wuthrich
- first_name: Felix
full_name: Widmaier, Felix
last_name: Widmaier
- first_name: Peter Vincent
full_name: Gehler, Peter Vincent
last_name: Gehler
- first_name: Ole
full_name: Winther, Ole
last_name: Winther
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Olivier
full_name: Bachem, Olivier
last_name: Bachem
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Stefan
full_name: Bauer, Stefan
last_name: Bauer
citation:
ama: 'Träuble F, Dittadi A, Wuthrich M, et al. Representation learning for out-of-distribution
generalization in reinforcement learning. In: ICML 2021 Workshop on Unsupervised
Reinforcement Learning. ; 2021.'
apa: Träuble, F., Dittadi, A., Wuthrich, M., Widmaier, F., Gehler, P. V., Winther,
O., … Bauer, S. (2021). Representation learning for out-of-distribution generalization
in reinforcement learning. In ICML 2021 Workshop on Unsupervised Reinforcement
Learning. Virtual.
chicago: Träuble, Frederik, Andrea Dittadi, Manuel Wuthrich, Felix Widmaier, Peter
Vincent Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf,
and Stefan Bauer. “Representation Learning for Out-of-Distribution Generalization
in Reinforcement Learning.” In ICML 2021 Workshop on Unsupervised Reinforcement
Learning, 2021.
ieee: F. Träuble et al., “Representation learning for out-of-distribution
generalization in reinforcement learning,” in ICML 2021 Workshop on Unsupervised
Reinforcement Learning, Virtual, 2021.
ista: 'Träuble F, Dittadi A, Wuthrich M, Widmaier F, Gehler PV, Winther O, Locatello
F, Bachem O, Schölkopf B, Bauer S. 2021. Representation learning for out-of-distribution
generalization in reinforcement learning. ICML 2021 Workshop on Unsupervised Reinforcement
Learning. ICML: International Conference on Machine Learning.'
mla: Träuble, Frederik, et al. “Representation Learning for Out-of-Distribution
Generalization in Reinforcement Learning.” ICML 2021 Workshop on Unsupervised
Reinforcement Learning, 2021.
short: F. Träuble, A. Dittadi, M. Wuthrich, F. Widmaier, P.V. Gehler, O. Winther,
F. Locatello, O. Bachem, B. Schölkopf, S. Bauer, in:, ICML 2021 Workshop on Unsupervised
Reinforcement Learning, 2021.
conference:
end_date: 2021-07-23
location: Virtual
name: 'ICML: International Conference on Machine Learning'
start_date: 2021-07-23
date_created: 2023-09-13T12:43:14Z
date_published: 2021-07-23T00:00:00Z
date_updated: 2023-09-13T12:44:00Z
day: '23'
department:
- _id: FrLo
extern: '1'
language:
- iso: eng
month: '07'
oa_version: None
publication: ICML 2021 Workshop on Unsupervised Reinforcement Learning
publication_status: published
quality_controlled: '1'
status: public
title: Representation learning for out-of-distribution generalization in reinforcement
learning
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '10000'
abstract:
- lang: eng
text: Inhibition or targeted deletion of histone deacetylase 3 (HDAC3) is neuroprotective
in a variety neurodegenerative conditions, including retinal ganglion cells (RGCs)
after acute optic nerve damage. Consistent with this, induced HDAC3 expression
in cultured cells shows selective toxicity to neurons. Despite an established
role for HDAC3 in neuronal pathology, little is known regarding the mechanism
of this pathology.
acknowledgement: 'The authors thank Joel Dietz for maintaining the mice used in this
study, Satoshi Kinoshita and the Translational Research Initiative in Pathology
Laboratory at the University of Wisconsin-Madison for cutting retinal sections analyzed
in this study, and Mark Banghart for statistical review of the data analysis. Supported
by National Eye Institute Grants R01 EY012223 (RWN), R01 EY030123 (RWN), R01 EY029809
(LWG), R01 EY029809 (LWG) and a Vision Research CORE grant P30 EY016665, NRSA grant
T32 GM081061, by an unrestricted research grant from Research to Prevent Blindness,
Inc., and by a University of Wisconsin-Madison Vilas Life Cycle award and the Frederick
A. Davis Research Chair (RWN). '
article_number: '14'
article_processing_charge: Yes
article_type: original
author:
- first_name: Heather M.
full_name: Schmitt, Heather M.
last_name: Schmitt
- first_name: Rachel L.
full_name: Fehrman, Rachel L.
last_name: Fehrman
- first_name: Margaret E
full_name: Maes, Margaret E
id: 3838F452-F248-11E8-B48F-1D18A9856A87
last_name: Maes
orcid: 0000-0001-9642-1085
- first_name: Huan
full_name: Yang, Huan
last_name: Yang
- first_name: Lian Wang
full_name: Guo, Lian Wang
last_name: Guo
- first_name: Cassandra L.
full_name: Schlamp, Cassandra L.
last_name: Schlamp
- first_name: Heather R.
full_name: Pelzel, Heather R.
last_name: Pelzel
- first_name: Robert W.
full_name: Nickells, Robert W.
last_name: Nickells
citation:
ama: Schmitt HM, Fehrman RL, Maes ME, et al. Increased susceptibility and intrinsic
apoptotic signaling in neurons by induced HDAC3 expression. Investigative Ophthalmology
and Visual Science. 2021;62(10). doi:10.1167/IOVS.62.10.14
apa: Schmitt, H. M., Fehrman, R. L., Maes, M. E., Yang, H., Guo, L. W., Schlamp,
C. L., … Nickells, R. W. (2021). Increased susceptibility and intrinsic apoptotic
signaling in neurons by induced HDAC3 expression. Investigative Ophthalmology
and Visual Science. Association for Research in Vision and Ophthalmology.
https://doi.org/10.1167/IOVS.62.10.14
chicago: Schmitt, Heather M., Rachel L. Fehrman, Margaret E Maes, Huan Yang, Lian
Wang Guo, Cassandra L. Schlamp, Heather R. Pelzel, and Robert W. Nickells. “Increased
Susceptibility and Intrinsic Apoptotic Signaling in Neurons by Induced HDAC3 Expression.”
Investigative Ophthalmology and Visual Science. Association for Research
in Vision and Ophthalmology, 2021. https://doi.org/10.1167/IOVS.62.10.14.
ieee: H. M. Schmitt et al., “Increased susceptibility and intrinsic apoptotic
signaling in neurons by induced HDAC3 expression,” Investigative Ophthalmology
and Visual Science, vol. 62, no. 10. Association for Research in Vision and
Ophthalmology, 2021.
ista: Schmitt HM, Fehrman RL, Maes ME, Yang H, Guo LW, Schlamp CL, Pelzel HR, Nickells
RW. 2021. Increased susceptibility and intrinsic apoptotic signaling in neurons
by induced HDAC3 expression. Investigative Ophthalmology and Visual Science. 62(10),
14.
mla: Schmitt, Heather M., et al. “Increased Susceptibility and Intrinsic Apoptotic
Signaling in Neurons by Induced HDAC3 Expression.” Investigative Ophthalmology
and Visual Science, vol. 62, no. 10, 14, Association for Research in Vision
and Ophthalmology, 2021, doi:10.1167/IOVS.62.10.14.
short: H.M. Schmitt, R.L. Fehrman, M.E. Maes, H. Yang, L.W. Guo, C.L. Schlamp, H.R.
Pelzel, R.W. Nickells, Investigative Ophthalmology and Visual Science 62 (2021).
date_created: 2021-09-12T22:01:23Z
date_published: 2021-08-16T00:00:00Z
date_updated: 2023-08-14T06:35:17Z
day: '16'
ddc:
- '570'
department:
- _id: SaSi
doi: 10.1167/IOVS.62.10.14
external_id:
isi:
- '000695230000014'
pmid:
- '34398198'
file:
- access_level: open_access
checksum: c430967746f653aa1ae84ee617f62b73
content_type: application/pdf
creator: dernst
date_created: 2022-05-13T07:40:15Z
date_updated: 2022-05-13T07:40:15Z
file_id: '11369'
file_name: 2021_IOVS_Schmitt.pdf
file_size: 19707796
relation: main_file
success: 1
file_date_updated: 2022-05-13T07:40:15Z
has_accepted_license: '1'
intvolume: ' 62'
isi: 1
issue: '10'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
month: '08'
oa: 1
oa_version: Published Version
pmid: 1
publication: Investigative Ophthalmology and Visual Science
publication_identifier:
eissn:
- 1552-5783
issn:
- 0146-0404
publication_status: published
publisher: Association for Research in Vision and Ophthalmology
quality_controlled: '1'
scopus_import: '1'
status: public
title: Increased susceptibility and intrinsic apoptotic signaling in neurons by induced
HDAC3 expression
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 62
year: '2021'
...
---
_id: '10002'
abstract:
- lang: eng
text: 'We present a faster symbolic algorithm for the following central problem
in probabilistic verification: Compute the maximal end-component (MEC) decomposition
of Markov decision processes (MDPs). This problem generalizes the SCC decomposition
problem of graphs and closed recurrent sets of Markov chains. The model of symbolic
algorithms is widely used in formal verification and model-checking, where access
to the input model is restricted to only symbolic operations (e.g., basic set
operations and computation of one-step neighborhood). For an input MDP with n vertices
and m edges, the classical symbolic algorithm from the 1990s for the MEC decomposition
requires O(n2) symbolic operations and O(1) symbolic space. The only other
symbolic algorithm for the MEC decomposition requires O(nm−−√) symbolic operations
and O(m−−√) symbolic space. A main open question is whether the worst-case O(n2) bound
for symbolic operations can be beaten. We present a symbolic algorithm that requires O˜(n1.5) symbolic
operations and O˜(n−−√) symbolic space. Moreover, the parametrization of our
algorithm provides a trade-off between symbolic operations and symbolic space:
for all 0<ϵ≤1/2 the symbolic algorithm requires O˜(n2−ϵ) symbolic operations
and O˜(nϵ) symbolic space ( O˜ hides poly-logarithmic factors). Using our techniques
we present faster algorithms for computing the almost-sure winning regions of ω
-regular objectives for MDPs. We consider the canonical parity objectives for ω
-regular objectives, and for parity objectives with d -priorities we present
an algorithm that computes the almost-sure winning region with O˜(n2−ϵ) symbolic
operations and O˜(nϵ) symbolic space, for all 0<ϵ≤1/2 .'
acknowledgement: The authors are grateful to the anonymous referees for their valuable
comments. A. S. is fully supported by the Vienna Science and Technology Fund (WWTF)
through project ICT15–003. K. C. is supported by the Austrian Science Fund (FWF)
NFN Grant No S11407-N23 (RiSE/SHiNE) and by the ERC CoG 863818 (ForM-SMArt). For
M. H. the research leading to these results has received funding from the European
Research Council under the European Unions Seventh Framework Programme (FP/2007–2013)
/ ERC Grant Agreement no. 340506.
article_processing_charge: No
arxiv: 1
author:
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Wolfgang
full_name: Dvorak, Wolfgang
last_name: Dvorak
- first_name: Monika H
full_name: Henzinger, Monika H
id: 540c9bbd-f2de-11ec-812d-d04a5be85630
last_name: Henzinger
orcid: 0000-0002-5008-6530
- first_name: Alexander
full_name: Svozil, Alexander
last_name: Svozil
citation:
ama: 'Chatterjee K, Dvorak W, Henzinger MH, Svozil A. Symbolic time and space tradeoffs
for probabilistic verification. In: Proceedings of the 36th Annual ACM/IEEE
Symposium on Logic in Computer Science. Institute of Electrical and Electronics
Engineers; 2021:1-13. doi:10.1109/LICS52264.2021.9470739'
apa: 'Chatterjee, K., Dvorak, W., Henzinger, M. H., & Svozil, A. (2021). Symbolic
time and space tradeoffs for probabilistic verification. In Proceedings of
the 36th Annual ACM/IEEE Symposium on Logic in Computer Science (pp. 1–13).
Rome, Italy: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/LICS52264.2021.9470739'
chicago: Chatterjee, Krishnendu, Wolfgang Dvorak, Monika H Henzinger, and Alexander
Svozil. “Symbolic Time and Space Tradeoffs for Probabilistic Verification.” In
Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science,
1–13. Institute of Electrical and Electronics Engineers, 2021. https://doi.org/10.1109/LICS52264.2021.9470739.
ieee: K. Chatterjee, W. Dvorak, M. H. Henzinger, and A. Svozil, “Symbolic time and
space tradeoffs for probabilistic verification,” in Proceedings of the 36th
Annual ACM/IEEE Symposium on Logic in Computer Science, Rome, Italy, 2021,
pp. 1–13.
ista: 'Chatterjee K, Dvorak W, Henzinger MH, Svozil A. 2021. Symbolic time and space
tradeoffs for probabilistic verification. Proceedings of the 36th Annual ACM/IEEE
Symposium on Logic in Computer Science. LICS: Symposium on Logic in Computer Science,
1–13.'
mla: Chatterjee, Krishnendu, et al. “Symbolic Time and Space Tradeoffs for Probabilistic
Verification.” Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in
Computer Science, Institute of Electrical and Electronics Engineers, 2021,
pp. 1–13, doi:10.1109/LICS52264.2021.9470739.
short: K. Chatterjee, W. Dvorak, M.H. Henzinger, A. Svozil, in:, Proceedings of
the 36th Annual ACM/IEEE Symposium on Logic in Computer Science, Institute of
Electrical and Electronics Engineers, 2021, pp. 1–13.
conference:
end_date: 2021-07-02
location: Rome, Italy
name: 'LICS: Symposium on Logic in Computer Science'
start_date: 2021-06-29
date_created: 2021-09-12T22:01:24Z
date_published: 2021-07-07T00:00:00Z
date_updated: 2025-07-14T09:10:07Z
day: '07'
department:
- _id: KrCh
doi: 10.1109/LICS52264.2021.9470739
ec_funded: 1
external_id:
arxiv:
- '2104.07466'
isi:
- '000947350400089'
isi: 1
keyword:
- Computer science
- Computational modeling
- Markov processes
- Probabilistic logic
- Formal verification
- Game Theory
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2104.07466
month: '07'
oa: 1
oa_version: Preprint
page: 1-13
project:
- _id: 25863FF4-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: S11407
name: Game Theory
- _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 36th Annual ACM/IEEE Symposium on Logic in Computer
Science
publication_identifier:
eisbn:
- 978-1-6654-4895-6
isbn:
- 978-1-6654-4896-3
issn:
- 1043-6871
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Symbolic time and space tradeoffs for probabilistic verification
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
year: '2021'
...
---
_id: '10004'
abstract:
- lang: eng
text: 'Markov chains are the de facto finite-state model for stochastic dynamical
systems, and Markov decision processes (MDPs) extend Markov chains by incorporating
non-deterministic behaviors. Given an MDP and rewards on states, a classical optimization
criterion is the maximal expected total reward where the MDP stops after T steps,
which can be computed by a simple dynamic programming algorithm. We consider a
natural generalization of the problem where the stopping times can be chosen according
to a probability distribution, such that the expected stopping time is T, to optimize
the expected total reward. Quite surprisingly we establish inter-reducibility
of the expected stopping-time problem for Markov chains with the Positivity problem
(which is related to the well-known Skolem problem), for which establishing either
decidability or undecidability would be a major breakthrough. Given the hardness
of the exact problem, we consider the approximate version of the problem: we show
that it can be solved in exponential time for Markov chains and in exponential
space for MDPs.'
acknowledgement: We are grateful to the anonymous reviewers of LICS 2021 and of a
previous version of this paper for insightful comments that helped improving the
presentation. This research was partially supported by the grant ERC CoG 863818
(ForM-SMArt).
article_processing_charge: No
arxiv: 1
author:
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Laurent
full_name: Doyen, Laurent
last_name: Doyen
citation:
ama: 'Chatterjee K, Doyen L. Stochastic processes with expected stopping time. In:
Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science.
Institute of Electrical and Electronics Engineers; 2021:1-13. doi:10.1109/LICS52264.2021.9470595'
apa: 'Chatterjee, K., & Doyen, L. (2021). Stochastic processes with expected
stopping time. In Proceedings of the 36th Annual ACM/IEEE Symposium on Logic
in Computer Science (pp. 1–13). Rome, Italy: Institute of Electrical and Electronics
Engineers. https://doi.org/10.1109/LICS52264.2021.9470595'
chicago: Chatterjee, Krishnendu, and Laurent Doyen. “Stochastic Processes with Expected
Stopping Time.” In Proceedings of the 36th Annual ACM/IEEE Symposium on Logic
in Computer Science, 1–13. Institute of Electrical and Electronics Engineers,
2021. https://doi.org/10.1109/LICS52264.2021.9470595.
ieee: K. Chatterjee and L. Doyen, “Stochastic processes with expected stopping time,”
in Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science,
Rome, Italy, 2021, pp. 1–13.
ista: 'Chatterjee K, Doyen L. 2021. Stochastic processes with expected stopping
time. Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science.
LICS: Symposium on Logic in Computer Science, 1–13.'
mla: Chatterjee, Krishnendu, and Laurent Doyen. “Stochastic Processes with Expected
Stopping Time.” Proceedings of the 36th Annual ACM/IEEE Symposium on Logic
in Computer Science, Institute of Electrical and Electronics Engineers, 2021,
pp. 1–13, doi:10.1109/LICS52264.2021.9470595.
short: K. Chatterjee, L. Doyen, in:, Proceedings of the 36th Annual ACM/IEEE Symposium
on Logic in Computer Science, Institute of Electrical and Electronics Engineers,
2021, pp. 1–13.
conference:
end_date: 2021-07-02
location: Rome, Italy
name: 'LICS: Symposium on Logic in Computer Science'
start_date: 2021-06-29
date_created: 2021-09-12T22:01:25Z
date_published: 2021-07-07T00:00:00Z
date_updated: 2025-07-14T09:10:08Z
day: '07'
department:
- _id: KrCh
doi: 10.1109/LICS52264.2021.9470595
ec_funded: 1
external_id:
arxiv:
- '2104.07278'
isi:
- '000947350400036'
isi: 1
keyword:
- Computer science
- Heuristic algorithms
- Memory management
- Automata
- Markov processes
- Probability distribution
- Complexity theory
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2104.07278
month: '07'
oa: 1
oa_version: Preprint
page: 1-13
project:
- _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 36th Annual ACM/IEEE Symposium on Logic in Computer
Science
publication_identifier:
eisbn:
- 978-1-6654-4895-6
isbn:
- 978-1-6654-4896-3
issn:
- 1043-6871
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Stochastic processes with expected stopping time
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
year: '2021'
...
---
_id: '10005'
abstract:
- lang: eng
text: We study systems of nonlinear partial differential equations of parabolic
type, in which the elliptic operator is replaced by the first-order divergence
operator acting on a flux function, which is related to the spatial gradient of
the unknown through an additional implicit equation. This setting, broad enough
in terms of applications, significantly expands the paradigm of nonlinear parabolic
problems. Formulating four conditions concerning the form of the implicit equation,
we first show that these conditions describe a maximal monotone p-coercive graph.
We then establish the global-in-time and large-data existence of a (weak) solution
and its uniqueness. To this end, we adopt and significantly generalize Minty’s
method of monotone mappings. A unified theory, containing several novel tools,
is developed in a way to be tractable from the point of view of numerical approximations.
acknowledgement: "M. Bulíček and J. Málek acknowledge the support of the project No.
18-12719S financed by the Czech\r\nScience foundation (GAČR). E. Maringová acknowledges
support from Charles University Research program \r\nUNCE/SCI/023, the grant SVV-2020-260583
by the Ministry of Education, Youth and Sports, Czech Republic\r\nand from the Austrian
Science Fund (FWF), grants P30000, W1245, and F65. M. Bulíček and J. Málek are\r\nmembers
of the Nečas Center for Mathematical Modelling.\r\n"
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Miroslav
full_name: Bulíček, Miroslav
last_name: Bulíček
- first_name: Erika
full_name: Maringová, Erika
id: dbabca31-66eb-11eb-963a-fb9c22c880b4
last_name: Maringová
- first_name: Josef
full_name: Málek, Josef
last_name: Málek
citation:
ama: Bulíček M, Maringová E, Málek J. On nonlinear problems of parabolic type with
implicit constitutive equations involving flux. Mathematical Models and Methods
in Applied Sciences. 2021;31(09). doi:10.1142/S0218202521500457
apa: Bulíček, M., Maringová, E., & Málek, J. (2021). On nonlinear problems of
parabolic type with implicit constitutive equations involving flux. Mathematical
Models and Methods in Applied Sciences. World Scientific. https://doi.org/10.1142/S0218202521500457
chicago: Bulíček, Miroslav, Erika Maringová, and Josef Málek. “On Nonlinear Problems
of Parabolic Type with Implicit Constitutive Equations Involving Flux.” Mathematical
Models and Methods in Applied Sciences. World Scientific, 2021. https://doi.org/10.1142/S0218202521500457.
ieee: M. Bulíček, E. Maringová, and J. Málek, “On nonlinear problems of parabolic
type with implicit constitutive equations involving flux,” Mathematical Models
and Methods in Applied Sciences, vol. 31, no. 09. World Scientific, 2021.
ista: Bulíček M, Maringová E, Málek J. 2021. On nonlinear problems of parabolic
type with implicit constitutive equations involving flux. Mathematical Models
and Methods in Applied Sciences. 31(09).
mla: Bulíček, Miroslav, et al. “On Nonlinear Problems of Parabolic Type with Implicit
Constitutive Equations Involving Flux.” Mathematical Models and Methods in
Applied Sciences, vol. 31, no. 09, World Scientific, 2021, doi:10.1142/S0218202521500457.
short: M. Bulíček, E. Maringová, J. Málek, Mathematical Models and Methods in Applied
Sciences 31 (2021).
date_created: 2021-09-12T22:01:25Z
date_published: 2021-08-25T00:00:00Z
date_updated: 2023-09-04T11:43:45Z
day: '25'
department:
- _id: JuFi
doi: 10.1142/S0218202521500457
external_id:
arxiv:
- '2009.06917'
isi:
- '000722222900004'
intvolume: ' 31'
isi: 1
issue: '09'
keyword:
- Nonlinear parabolic systems
- implicit constitutive theory
- weak solutions
- existence
- uniqueness
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2009.06917
month: '08'
oa: 1
oa_version: Preprint
project:
- _id: fc31cba2-9c52-11eb-aca3-ff467d239cd2
grant_number: F6504
name: Taming Complexity in Partial Differential Systems
publication: Mathematical Models and Methods in Applied Sciences
publication_identifier:
eissn:
- 1793-6314
issn:
- 0218-2025
publication_status: published
publisher: World Scientific
quality_controlled: '1'
scopus_import: '1'
status: public
title: On nonlinear problems of parabolic type with implicit constitutive equations
involving flux
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 31
year: '2021'
...