---
OA_place: repository
OA_type: green
_id: '19665'
abstract:
- lang: eng
text: As AI-based decision-makers increasingly influence human lives, it is a growing
concern that their decisions may be unfair or biased with respect to people's
protected attributes, such as gender and race. Most existing bias prevention measures
provide probabilistic fairness guarantees in the long run, and it is possible
that the decisions are biased on any decision sequence of fixed length. We introduce
*fairness shielding*, where a symbolic decision-maker---the fairness shield---continuously
monitors the sequence of decisions of another deployed black-box decision-maker,
and makes interventions so that a given fairness criterion is met while the total
intervention costs are minimized. We present four different algorithms for computing
fairness shields, among which one guarantees fairness over fixed horizons, and
three guarantee fairness periodically after fixed intervals. Given a distribution
over future decisions and their intervention costs, our algorithms solve different
instances of bounded-horizon optimal control problems with different levels of
computational costs and optimality guarantees. Our empirical evaluation demonstrates
the effectiveness of these shields in ensuring fairness while maintaining cost
efficiency across various scenarios.
acknowledgement: 'This work is partly supported by the European Research Council under
Grant No.: ERC-2020-AdG 101020093. It is also partially supported by the State Government
of Styria, Austria – Department Zukunftsfonds Steiermark.'
article_processing_charge: No
arxiv: 1
author:
- first_name: Filip
full_name: Cano Cordoba, Filip
id: 708cad98-e86a-11ef-8098-bdae2d7c6af1
last_name: Cano Cordoba
orcid: 0000-0002-0783-904X
- 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: Bettina
full_name: Könighofer, Bettina
last_name: Könighofer
- first_name: Konstantin
full_name: Kueffner, Konstantin
id: 8121a2d0-dc85-11ea-9058-af578f3b4515
last_name: Kueffner
orcid: 0000-0001-8974-2542
- first_name: Kaushik
full_name: Mallik, Kaushik
id: 0834ff3c-6d72-11ec-94e0-b5b0a4fb8598
last_name: Mallik
orcid: 0000-0001-9864-7475
citation:
ama: 'Cano Cordoba F, Henzinger TA, Könighofer B, Kueffner K, Mallik K. Fairness
shields: Safeguarding against biased decision makers. In: Proceedings of the
39th AAAI Conference on Artificial Intelligence. Vol 39. Association for the
Advancement of Artificial Intelligence; 2025:15659-15668. doi:10.1609/aaai.v39i15.33719'
apa: 'Cano Cordoba, F., Henzinger, T. A., Könighofer, B., Kueffner, K., & Mallik,
K. (2025). Fairness shields: Safeguarding against biased decision makers. In Proceedings
of the 39th AAAI Conference on Artificial Intelligence (Vol. 39, pp. 15659–15668).
Philadelphia, PA, United States: Association for the Advancement of Artificial
Intelligence. https://doi.org/10.1609/aaai.v39i15.33719'
chicago: 'Cano Cordoba, Filip, Thomas A Henzinger, Bettina Könighofer, Konstantin
Kueffner, and Kaushik Mallik. “Fairness Shields: Safeguarding against Biased Decision
Makers.” In Proceedings of the 39th AAAI Conference on Artificial Intelligence,
39:15659–68. Association for the Advancement of Artificial Intelligence, 2025.
https://doi.org/10.1609/aaai.v39i15.33719.'
ieee: 'F. Cano Cordoba, T. A. Henzinger, B. Könighofer, K. Kueffner, and K. Mallik,
“Fairness shields: Safeguarding against biased decision makers,” in Proceedings
of the 39th AAAI Conference on Artificial Intelligence, Philadelphia, PA,
United States, 2025, vol. 39, no. 15, pp. 15659–15668.'
ista: 'Cano Cordoba F, Henzinger TA, Könighofer B, Kueffner K, Mallik K. 2025. Fairness
shields: Safeguarding against biased decision makers. Proceedings of the 39th
AAAI Conference on Artificial Intelligence. AAAI: Conference on Artificial Intelligence
vol. 39, 15659–15668.'
mla: 'Cano Cordoba, Filip, et al. “Fairness Shields: Safeguarding against Biased
Decision Makers.” Proceedings of the 39th AAAI Conference on Artificial Intelligence,
vol. 39, no. 15, Association for the Advancement of Artificial Intelligence, 2025,
pp. 15659–68, doi:10.1609/aaai.v39i15.33719.'
short: F. Cano Cordoba, T.A. Henzinger, B. Könighofer, K. Kueffner, K. Mallik, in:,
Proceedings of the 39th AAAI Conference on Artificial Intelligence, Association
for the Advancement of Artificial Intelligence, 2025, pp. 15659–15668.
conference:
end_date: 2025-03-04
location: Philadelphia, PA, United States
name: 'AAAI: Conference on Artificial Intelligence'
start_date: 2025-02-25
corr_author: '1'
date_created: 2025-05-11T22:02:39Z
date_published: 2025-04-11T00:00:00Z
date_updated: 2026-02-16T12:24:30Z
day: '11'
department:
- _id: ToHe
doi: 10.1609/aaai.v39i15.33719
ec_funded: 1
external_id:
arxiv:
- '2412.11994'
intvolume: ' 39'
issue: '15'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2412.11994
month: '04'
oa: 1
oa_version: Preprint
page: 15659-15668
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
publication: Proceedings of the 39th AAAI Conference on Artificial Intelligence
publication_identifier:
eissn:
- 2374-3468
issn:
- 2159-5399
publication_status: published
publisher: Association for the Advancement of Artificial Intelligence
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Fairness shields: Safeguarding against biased decision makers'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 39
year: '2025'
...
---
OA_place: repository
OA_type: green
_id: '19666'
abstract:
- lang: eng
text: Markov decision processes (MDP) are a well-established model for sequential
decision-making in the presence of probabilities. In *robust* MDP (RMDP), every
action is associated with an *uncertainty set* of probability distributions, modelling
that transition probabilities are not known precisely. Based on the known theoretical
connection to stochastic games, we provide a framework for solving RMDPs that
is generic, reliable, and efficient. It is *generic* both with respect to the
model, allowing for a wide range of uncertainty sets, including but not limited
to intervals, L1- or L2-balls, and polytopes; and with respect to the objective,
including long-run average reward, undiscounted total reward, and stochastic shortest
path. It is *reliable*, as our approach not only converges in the limit, but provides
precision guarantees at any time during the computation. It is *efficient* because
-- in contrast to state-of-the-art approaches -- it avoids explicitly constructing
the underlying stochastic game. Consequently, our prototype implementation outperforms
existing tools by several orders of magnitude and can solve RMDPs with a million
states in under a minute.
acknowledgement: "This project has received funding from the European Union’s Horizon
2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement
No. 101034413,\r\nthe ERC CoG 863818 (ForM-SMArt), and the DFG through the Cluster
of Excellence EXC 2050/1 (CeTI, project ID 390696704, as part of Germany’s Excellence
Strategy) and the TRR 248 (see https://perspicuous-computing.science, project ID
389792660)."
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
- first_name: Maximilian
full_name: Weininger, Maximilian
id: 02ab0197-cc70-11ed-ab61-918e71f56881
last_name: Weininger
orcid: 0000-0002-0163-2152
- first_name: Patrick
full_name: Wienhöft, Patrick
last_name: Wienhöft
citation:
ama: 'Meggendorfer T, Weininger M, Wienhöft P. Solving robust Markov decision processes:
Generic, reliable, efficient. In: Proceedings of the 39th AAAI Conference on
Artificial Intelligence. Vol 39. Association for the Advancement of Artificial
Intelligence; 2025:26631-26641. doi:10.1609/aaai.v39i25.34865'
apa: 'Meggendorfer, T., Weininger, M., & Wienhöft, P. (2025). Solving robust
Markov decision processes: Generic, reliable, efficient. In Proceedings of
the 39th AAAI Conference on Artificial Intelligence (Vol. 39, pp. 26631–26641).
Philadelphia, PA, United States: Association for the Advancement of Artificial
Intelligence. https://doi.org/10.1609/aaai.v39i25.34865'
chicago: 'Meggendorfer, Tobias, Maximilian Weininger, and Patrick Wienhöft. “Solving
Robust Markov Decision Processes: Generic, Reliable, Efficient.” In Proceedings
of the 39th AAAI Conference on Artificial Intelligence, 39:26631–41. Association
for the Advancement of Artificial Intelligence, 2025. https://doi.org/10.1609/aaai.v39i25.34865.'
ieee: 'T. Meggendorfer, M. Weininger, and P. Wienhöft, “Solving robust Markov decision
processes: Generic, reliable, efficient,” in Proceedings of the 39th AAAI Conference
on Artificial Intelligence, Philadelphia, PA, United States, 2025, vol. 39,
no. 25, pp. 26631–26641.'
ista: 'Meggendorfer T, Weininger M, Wienhöft P. 2025. Solving robust Markov decision
processes: Generic, reliable, efficient. Proceedings of the 39th AAAI Conference
on Artificial Intelligence. AAAI: Conference on Artificial Intelligence vol. 39,
26631–26641.'
mla: 'Meggendorfer, Tobias, et al. “Solving Robust Markov Decision Processes: Generic,
Reliable, Efficient.” Proceedings of the 39th AAAI Conference on Artificial
Intelligence, vol. 39, no. 25, Association for the Advancement of Artificial
Intelligence, 2025, pp. 26631–41, doi:10.1609/aaai.v39i25.34865.'
short: T. Meggendorfer, M. Weininger, P. Wienhöft, in:, Proceedings of the 39th
AAAI Conference on Artificial Intelligence, Association for the Advancement of
Artificial Intelligence, 2025, pp. 26631–26641.
conference:
end_date: 2025-03-04
location: Philadelphia, PA, United States
name: 'AAAI: Conference on Artificial Intelligence'
start_date: 2025-02-25
date_created: 2025-05-11T22:02:39Z
date_published: 2025-04-11T00:00:00Z
date_updated: 2026-02-16T12:25:05Z
day: '11'
department:
- _id: KrCh
doi: 10.1609/aaai.v39i25.34865
ec_funded: 1
external_id:
arxiv:
- '2412.10185'
intvolume: ' 39'
issue: '25'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2412.10185
month: '04'
oa: 1
oa_version: Preprint
page: 26631-26641
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 39th AAAI Conference on Artificial Intelligence
publication_identifier:
eissn:
- 2374-3468
issn:
- 2159-5399
publication_status: published
publisher: Association for the Advancement of Artificial Intelligence
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://doi.org/10.5281/zenodo.14385449
scopus_import: '1'
status: public
title: 'Solving robust Markov decision processes: Generic, reliable, efficient'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 39
year: '2025'
...
---
OA_place: repository
OA_type: green
_id: '19667'
abstract:
- lang: eng
text: The problem of checking satisfiability of linear real arithmetic (LRA) and
non-linear real arithmetic (NRA) formulas has broad applications, in particular,
they are at the heart of logic-related applications such as logic for artificial
intelligence, program analysis, etc. While there has been much work on checking
satisfiability of unquantified LRA and NRA formulas, the problem of checking satisfiability
of quantified LRA and NRA formulas remains a significant challenge. The main bottleneck
in the existing methods is a computationally expensive quantifier elimination
step. In this work, we propose a novel method for efficient quantifier elimination
in quantified LRA and NRA formulas. We propose a template-based Skolemization
approach, where we automatically synthesize linear/polynomial Skolem functions
in order to eliminate quantifiers in the formula. The key technical ingredient
in our approach are Positivstellensätze theorems from algebraic geometry, which
allow for an efficient manipulation of polynomial inequalities. Our method offers
a range of appealing theoretical properties combined with a strong practical performance.
On the theory side, our method is sound, semi-complete, and runs in subexponential
time and polynomial space, as opposed to existing sound and complete quantifier
elimination methods that run in doubly-exponential time and at least exponential
space. On the practical side, our experiments show superior performance compared
to state of the art SMT solvers in terms of the number of solved instances and
runtime, both on LRA and on NRA benchmarks.
acknowledgement: This work was partially funded by ERC CoG 863818 (ForM-SMArt) and
Austrian Science Fund (FWF) 10.55776/COE12.
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: Ehsan
full_name: Kafshdar Goharshadi, Ehsan
id: 103b4fa0-896a-11ed-bdf8-87b697bef40d
last_name: Kafshdar Goharshadi
orcid: 0000-0002-8595-0587
- first_name: Mehrdad
full_name: Karrabi, Mehrdad
id: 67638922-f394-11eb-9cf6-f20423e08757
last_name: Karrabi
orcid: 0009-0007-5253-9170
- first_name: Harshit J.
full_name: Motwani, Harshit J.
last_name: Motwani
- first_name: Maximilian
full_name: Seeliger, Maximilian
last_name: Seeliger
- first_name: Dorde
full_name: Zikelic, Dorde
id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
last_name: Zikelic
orcid: 0000-0002-4681-1699
citation:
ama: 'Chatterjee K, Goharshady E, Karrabi M, Motwani HJ, Seeliger M, Zikelic D.
Quantified linear and polynomial arithmetic satisfiability via template-based
skolemization. In: Proceedings of the 39th AAAI Conference on Artificial Intelligence.
Vol 39. Association for the Advancement of Artificial Intelligence; 2025:11158-11166.
doi:10.1609/aaai.v39i11.33213'
apa: 'Chatterjee, K., Goharshady, E., Karrabi, M., Motwani, H. J., Seeliger, M.,
& Zikelic, D. (2025). Quantified linear and polynomial arithmetic satisfiability
via template-based skolemization. In Proceedings of the 39th AAAI Conference
on Artificial Intelligence (Vol. 39, pp. 11158–11166). Philadelphia, PA, United
States: Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v39i11.33213'
chicago: Chatterjee, Krishnendu, Ehsan Goharshady, Mehrdad Karrabi, Harshit J. Motwani,
Maximilian Seeliger, and Dorde Zikelic. “Quantified Linear and Polynomial Arithmetic
Satisfiability via Template-Based Skolemization.” In Proceedings of the 39th
AAAI Conference on Artificial Intelligence, 39:11158–66. Association for the
Advancement of Artificial Intelligence, 2025. https://doi.org/10.1609/aaai.v39i11.33213.
ieee: K. Chatterjee, E. Goharshady, M. Karrabi, H. J. Motwani, M. Seeliger, and
D. Zikelic, “Quantified linear and polynomial arithmetic satisfiability via template-based
skolemization,” in Proceedings of the 39th AAAI Conference on Artificial Intelligence,
Philadelphia, PA, United States, 2025, vol. 39, no. 11, pp. 11158–11166.
ista: 'Chatterjee K, Goharshady E, Karrabi M, Motwani HJ, Seeliger M, Zikelic D.
2025. Quantified linear and polynomial arithmetic satisfiability via template-based
skolemization. Proceedings of the 39th AAAI Conference on Artificial Intelligence.
AAAI: Conference on Artificial Intelligence vol. 39, 11158–11166.'
mla: Chatterjee, Krishnendu, et al. “Quantified Linear and Polynomial Arithmetic
Satisfiability via Template-Based Skolemization.” Proceedings of the 39th AAAI
Conference on Artificial Intelligence, vol. 39, no. 11, Association for the
Advancement of Artificial Intelligence, 2025, pp. 11158–66, doi:10.1609/aaai.v39i11.33213.
short: K. Chatterjee, E. Goharshady, M. Karrabi, H.J. Motwani, M. Seeliger, D. Zikelic,
in:, Proceedings of the 39th AAAI Conference on Artificial Intelligence, Association
for the Advancement of Artificial Intelligence, 2025, pp. 11158–11166.
conference:
end_date: 2025-03-04
location: Philadelphia, PA, United States
name: 'AAAI: Conference on Artificial Intelligence'
start_date: 2025-02-25
corr_author: '1'
date_created: 2025-05-11T22:02:39Z
date_published: 2025-04-11T00:00:00Z
date_updated: 2026-02-16T12:24:47Z
day: '11'
department:
- _id: KrCh
doi: 10.1609/aaai.v39i11.33213
ec_funded: 1
external_id:
arxiv:
- '2412.16226'
intvolume: ' 39'
issue: '11'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2412.16226
month: '04'
oa: 1
oa_version: Preprint
page: 11158-11166
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 39th AAAI Conference on Artificial Intelligence
publication_identifier:
eissn:
- 2374-3468
issn:
- 2159-5399
publication_status: published
publisher: Association for the Advancement of Artificial Intelligence
quality_controlled: '1'
scopus_import: '1'
status: public
title: Quantified linear and polynomial arithmetic satisfiability via template-based
skolemization
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 39
year: '2025'
...
---
OA_place: repository
OA_type: green
_id: '19668'
abstract:
- lang: eng
text: Learning-based methods provide a promising approach to solving highly non-linear
control tasks that are often challenging for classical control methods. To ensure
the satisfaction of a safety property, learning-based methods jointly learn a
control policy together with a certificate function for the property. Popular
examples include barrier functions for safety and Lyapunov functions for asymptotic
stability. While there has been significant progress on learning-based control
with certificate functions in the white-box setting, where the correctness of
the certificate function can be formally verified, there has been little work
on ensuring their reliability in the black-box setting where the system dynamics
are unknown. In this work, we consider the problems of certifying and repairing
neural network control policies and certificate functions in the black-box setting.
We propose a novel framework that utilizes runtime monitoring to detect system
behaviors that violate the property of interest under some initially trained neural
network policy and certificate. These violating behaviors are used to extract
new training data, that is used to re-train the neural network policy and the
certificate function and to ultimately repair them. We demonstrate the effectiveness
of our approach empirically by using it to repair and to boost the safety rate
of neural network policies learned by a state-of-the-art method for learning-based
control on two autonomous system control tasks.
acknowledgement: This work was supported in part by the ERC project ERC2020-AdG 101020093
article_processing_charge: No
arxiv: 1
author:
- first_name: Zhengqi
full_name: Yu, Zhengqi
id: 20aa2ae8-f2f1-11ed-bbfa-8205053f1342
last_name: Yu
- first_name: Dorde
full_name: Zikelic, Dorde
id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
last_name: Zikelic
orcid: 0000-0002-4681-1699
- 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: 'Yu E, Zikelic D, Henzinger TA. Neural control and certificate repair via runtime
monitoring. In: Proceedings of the 39th AAAI Conference on Artificial Intelligence.
Vol 39. Association for the Advancement of Artificial Intelligence; 2025:26409-26417.
doi:10.1609/aaai.v39i25.34840'
apa: 'Yu, E., Zikelic, D., & Henzinger, T. A. (2025). Neural control and certificate
repair via runtime monitoring. In Proceedings of the 39th AAAI Conference on
Artificial Intelligence (Vol. 39, pp. 26409–26417). Philadelphia, PA, United
States: Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v39i25.34840'
chicago: Yu, Emily, Dorde Zikelic, and Thomas A Henzinger. “Neural Control and Certificate
Repair via Runtime Monitoring.” In Proceedings of the 39th AAAI Conference
on Artificial Intelligence, 39:26409–17. Association for the Advancement of
Artificial Intelligence, 2025. https://doi.org/10.1609/aaai.v39i25.34840.
ieee: E. Yu, D. Zikelic, and T. A. Henzinger, “Neural control and certificate repair
via runtime monitoring,” in Proceedings of the 39th AAAI Conference on Artificial
Intelligence, Philadelphia, PA, United States, 2025, vol. 39, no. 25, pp.
26409–26417.
ista: 'Yu E, Zikelic D, Henzinger TA. 2025. Neural control and certificate repair
via runtime monitoring. Proceedings of the 39th AAAI Conference on Artificial
Intelligence. AAAI: Conference on Artificial Intelligence vol. 39, 26409–26417.'
mla: Yu, Emily, et al. “Neural Control and Certificate Repair via Runtime Monitoring.”
Proceedings of the 39th AAAI Conference on Artificial Intelligence, vol.
39, no. 25, Association for the Advancement of Artificial Intelligence, 2025,
pp. 26409–17, doi:10.1609/aaai.v39i25.34840.
short: E. Yu, D. Zikelic, T.A. Henzinger, in:, Proceedings of the 39th AAAI Conference
on Artificial Intelligence, Association for the Advancement of Artificial Intelligence,
2025, pp. 26409–26417.
conference:
end_date: 2025-03-04
location: Philadelphia, PA, United States
name: 'AAAI: Conference on Artificial Intelligence'
start_date: 2025-02-25
corr_author: '1'
date_created: 2025-05-11T22:02:40Z
date_published: 2025-04-11T00:00:00Z
date_updated: 2025-05-12T09:49:25Z
day: '11'
department:
- _id: ToHe
doi: 10.1609/aaai.v39i25.34840
ec_funded: 1
external_id:
arxiv:
- '2412.12996'
intvolume: ' 39'
issue: '25'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2412.12996
month: '04'
oa: 1
oa_version: Preprint
page: 26409-26417
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
publication: Proceedings of the 39th AAAI Conference on Artificial Intelligence
publication_identifier:
eissn:
- 2374-3468
issn:
- 2159-5399
publication_status: published
publisher: Association for the Advancement of Artificial Intelligence
quality_controlled: '1'
scopus_import: '1'
status: public
title: Neural control and certificate repair via runtime monitoring
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 39
year: '2025'
...
---
OA_place: repository
OA_type: green
_id: '19669'
abstract:
- lang: eng
text: 'We consider a class of optimization problems defined by a system of linear
equations with min and max operators. This class of optimization problems has
been studied under restrictive conditions, such as, (C1) the halting or stability
condition; (C2) the non-negative coefficients condition; (C3) the sum upto 1 condition;
and (C4) the only min or only max operator condition. Several seminal results
in the literature focus on special cases. For example, turn-based stochastic games
correspond to conditions C2 and C3; and Markov decision process to conditions
C2, C3, and C4. However, the systematic computational complexity study of all
the cases has not been explored, which we address in this work. Some highlights
of our results are: with conditions C2 and C4, and with conditions C3 and C4,
the problem is NP-complete, whereas with condition C1 only, the problem is in
UP intersects coUP. Finally, we establish the computational complexity of the
decision problem of checking the respective conditions.'
acknowledgement: This research was partially supported by the ERC CoG 863818 (ForM-SMArt)
grant and the Austrian Science Fund (FWF) 10.55776/COE12 grant.
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: Ruichen
full_name: Luo, Ruichen
id: b391db08-1ffe-11ee-8b67-d18ddcfb5a14
last_name: Luo
- first_name: Raimundo J
full_name: Saona Urmeneta, Raimundo J
id: BD1DF4C4-D767-11E9-B658-BC13E6697425
last_name: Saona Urmeneta
orcid: 0000-0001-5103-038X
- first_name: Jakub
full_name: Svoboda, Jakub
id: 130759D2-D7DD-11E9-87D2-DE0DE6697425
last_name: Svoboda
orcid: 0000-0002-1419-3267
citation:
ama: 'Chatterjee K, Luo R, Saona Urmeneta RJ, Svoboda J. Linear equations with min
and max operators: Computational complexity. In: Proceedings of the 39th AAAI
Conference on Artificial Intelligence. Vol 39. Association for the Advancement
of Artificial Intelligence; 2025:11150-11157. doi:10.1609/aaai.v39i11.33212'
apa: 'Chatterjee, K., Luo, R., Saona Urmeneta, R. J., & Svoboda, J. (2025).
Linear equations with min and max operators: Computational complexity. In Proceedings
of the 39th AAAI Conference on Artificial Intelligence (Vol. 39, pp. 11150–11157).
Philadelphia, PA, United States: Association for the Advancement of Artificial
Intelligence. https://doi.org/10.1609/aaai.v39i11.33212'
chicago: 'Chatterjee, Krishnendu, Ruichen Luo, Raimundo J Saona Urmeneta, and Jakub
Svoboda. “Linear Equations with Min and Max Operators: Computational Complexity.”
In Proceedings of the 39th AAAI Conference on Artificial Intelligence,
39:11150–57. Association for the Advancement of Artificial Intelligence, 2025.
https://doi.org/10.1609/aaai.v39i11.33212.'
ieee: 'K. Chatterjee, R. Luo, R. J. Saona Urmeneta, and J. Svoboda, “Linear equations
with min and max operators: Computational complexity,” in Proceedings of the
39th AAAI Conference on Artificial Intelligence, Philadelphia, PA, United
States, 2025, vol. 39, no. 11, pp. 11150–11157.'
ista: 'Chatterjee K, Luo R, Saona Urmeneta RJ, Svoboda J. 2025. Linear equations
with min and max operators: Computational complexity. Proceedings of the 39th
AAAI Conference on Artificial Intelligence. AAAI: Conference on Artificial Intelligence
vol. 39, 11150–11157.'
mla: 'Chatterjee, Krishnendu, et al. “Linear Equations with Min and Max Operators:
Computational Complexity.” Proceedings of the 39th AAAI Conference on Artificial
Intelligence, vol. 39, no. 11, Association for the Advancement of Artificial
Intelligence, 2025, pp. 11150–57, doi:10.1609/aaai.v39i11.33212.'
short: K. Chatterjee, R. Luo, R.J. Saona Urmeneta, J. Svoboda, in:, Proceedings
of the 39th AAAI Conference on Artificial Intelligence, Association for the Advancement
of Artificial Intelligence, 2025, pp. 11150–11157.
conference:
end_date: 2025-03-04
location: Philadelphia, PA, United States
name: 'AAAI: Conference on Artificial Intelligence'
start_date: 2025-02-25
corr_author: '1'
date_created: 2025-05-11T22:02:40Z
date_published: 2025-04-11T00:00:00Z
date_updated: 2025-05-12T09:42:09Z
day: '11'
department:
- _id: KrCh
doi: 10.1609/aaai.v39i11.33212
ec_funded: 1
external_id:
arxiv:
- '2412.12228'
intvolume: ' 39'
issue: '11'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2412.12228
month: '04'
oa: 1
oa_version: Preprint
page: 11150-11157
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 39th AAAI Conference on Artificial Intelligence
publication_identifier:
eissn:
- 2374-3468
issn:
- 2159-5399
publication_status: published
publisher: Association for the Advancement of Artificial Intelligence
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Linear equations with min and max operators: Computational complexity'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 39
year: '2025'
...
---
OA_place: publisher
OA_type: free access
_id: '19713'
abstract:
- lang: eng
text: Distributed optimization is the standard way of speeding up machine learning
training, and most of the research in the area focuses on distributed first-order,
gradient-based methods. Yet, there are settings where some computationally-bounded
nodes may not be able to implement first-order, gradient-based optimization, while
they could still contribute to joint optimization tasks. In this paper, we initiate
the study of hybrid decentralized optimization, studying settings where nodes
with zeroth-order and first-order optimization capabilities co-exist in a distributed
system, and attempt to jointly solve an optimization task over some data distribution.
We essentially show that, under reasonable parameter settings, such a system can
not only withstand noisier zeroth-order agents but can even benefit from integrating
such agents into the optimization process, rather than ignoring their information.
At the core of our approach is a new analysis of distributed optimization with
noisy and possibly-biased gradient estimators, which may be of independent interest.
Our results hold for both convex and non-convex objectives. Experimental results
on standard optimization tasks confirm our analysis, showing that hybrid first-zeroth
order optimization can be practical, even when training deep neural networks.
acknowledgement: "This project has received funding from the European Research Council
(ERC) under the European Union’s Horizon 2020 research and innovation programme
(grant agreement\r\nNo 805223 ScaleML). The authors would like to acknowledge Eugenia
Iofinova for useful discussions during the inception of this project."
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Shayan
full_name: Talaei, Shayan
last_name: Talaei
- first_name: Matin
full_name: Ansaripour, Matin
last_name: Ansaripour
- first_name: Giorgi
full_name: Nadiradze, Giorgi
id: 3279A00C-F248-11E8-B48F-1D18A9856A87
last_name: Nadiradze
orcid: 0000-0001-5634-0731
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Talaei S, Ansaripour M, Nadiradze G, Alistarh D-A. Hybrid decentralized optimization:
Leveraging both first- and zeroth-order optimizers for faster convergence. Proceedings
of the 39th AAAI Conference on Artificial Intelligence. 2025;39(19):20778-20786.
doi:10.1609/aaai.v39i19.34290'
apa: 'Talaei, S., Ansaripour, M., Nadiradze, G., & Alistarh, D.-A. (2025). Hybrid
decentralized optimization: Leveraging both first- and zeroth-order optimizers
for faster convergence. Proceedings of the 39th AAAI Conference on Artificial
Intelligence. Association for the Advancement of Artificial Intelligence.
https://doi.org/10.1609/aaai.v39i19.34290'
chicago: 'Talaei, Shayan, Matin Ansaripour, Giorgi Nadiradze, and Dan-Adrian Alistarh.
“Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers
for Faster Convergence.” Proceedings of the 39th AAAI Conference on Artificial
Intelligence. Association for the Advancement of Artificial Intelligence,
2025. https://doi.org/10.1609/aaai.v39i19.34290.'
ieee: 'S. Talaei, M. Ansaripour, G. Nadiradze, and D.-A. Alistarh, “Hybrid decentralized
optimization: Leveraging both first- and zeroth-order optimizers for faster convergence,”
Proceedings of the 39th AAAI Conference on Artificial Intelligence, vol.
39, no. 19. Association for the Advancement of Artificial Intelligence, pp. 20778–20786,
2025.'
ista: 'Talaei S, Ansaripour M, Nadiradze G, Alistarh D-A. 2025. Hybrid decentralized
optimization: Leveraging both first- and zeroth-order optimizers for faster convergence.
Proceedings of the 39th AAAI Conference on Artificial Intelligence. 39(19), 20778–20786.'
mla: 'Talaei, Shayan, et al. “Hybrid Decentralized Optimization: Leveraging Both
First- and Zeroth-Order Optimizers for Faster Convergence.” Proceedings of
the 39th AAAI Conference on Artificial Intelligence, vol. 39, no. 19, Association
for the Advancement of Artificial Intelligence, 2025, pp. 20778–86, doi:10.1609/aaai.v39i19.34290.'
short: S. Talaei, M. Ansaripour, G. Nadiradze, D.-A. Alistarh, Proceedings of the
39th AAAI Conference on Artificial Intelligence 39 (2025) 20778–20786.
corr_author: '1'
date_created: 2025-05-19T14:15:35Z
date_published: 2025-04-11T00:00:00Z
date_updated: 2026-02-16T12:34:44Z
day: '11'
department:
- _id: DaAl
doi: 10.1609/aaai.v39i19.34290
ec_funded: 1
external_id:
arxiv:
- '2210.07703'
intvolume: ' 39'
issue: '19'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1609/aaai.v39i19.34290
month: '04'
oa: 1
oa_version: Preprint
page: 20778-20786
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: Proceedings of the 39th AAAI Conference on Artificial Intelligence
publication_identifier:
eissn:
- 2374-3468
issn:
- 2159-5399
publication_status: published
publisher: Association for the Advancement of Artificial Intelligence
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/ShayanTalaei/HDO
scopus_import: '1'
status: public
title: 'Hybrid decentralized optimization: Leveraging both first- and zeroth-order
optimizers for faster convergence'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 39
year: '2025'
...
---
_id: '15321'
abstract:
- lang: eng
text: Boolean Networks (BNs) are widely used as a modeling formalism in several
domains, notably systems biology and computer science. A fundamental problem in
BN analysis is the enumeration of trap spaces, which are hypercubes in the state
space that cannot be escaped once entered. Several methods have been proposed
for enumerating trap spaces, however they often suffer from scalability and efficiency
issues, particularly for large and complex models. To our knowledge, the most
efficient and recent methods for the trap space enumeration all rely on Answer
Set Programming (ASP), which has been widely applied to the analysis of BNs. Motivated
by these considerations, our work proposes a new method for enumerating trap spaces
in BNs using ASP. We evaluate the method on a mix of 250+ real-world and 400+
randomly generated BNs, showing that it enables analysis of models beyond the
capabilities of existing tools (namely pyboolnet, mpbn, trappist, and trapmvn).
acknowledgement: This work was supported by Institut Carnot STAR, Marseille, France
and by the European Union’s Horizon 2020 research and innovation programme under
the Marie Skłodowska-Curie Grant Agreement No. 101034413.
article_processing_charge: No
author:
- first_name: Giang
full_name: Trinh, Giang
last_name: Trinh
- first_name: Belaid
full_name: Benhamou, Belaid
last_name: Benhamou
- first_name: Samuel
full_name: Pastva, Samuel
id: 07c5ea74-f61c-11ec-a664-aa7c5d957b2b
last_name: Pastva
orcid: 0000-0003-1993-0331
- first_name: Sylvain
full_name: Soliman, Sylvain
last_name: Soliman
citation:
ama: 'Trinh G, Benhamou B, Pastva S, Soliman S. Scalable enumeration of trap spaces
in boolean networks via answer set programming. In: Proceedings of the 38th
AAAI Conference on Artificial Intelligence. Vol 38. Association for the Advancement
of Artificial Intelligence; 2024:10714-10722. doi:10.1609/aaai.v38i9.28943'
apa: Trinh, G., Benhamou, B., Pastva, S., & Soliman, S. (2024). Scalable enumeration
of trap spaces in boolean networks via answer set programming. In Proceedings
of the 38th AAAI Conference on Artificial Intelligence (Vol. 38, pp. 10714–10722).
Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v38i9.28943
chicago: Trinh, Giang, Belaid Benhamou, Samuel Pastva, and Sylvain Soliman. “Scalable
Enumeration of Trap Spaces in Boolean Networks via Answer Set Programming.” In
Proceedings of the 38th AAAI Conference on Artificial Intelligence, 38:10714–22.
Association for the Advancement of Artificial Intelligence, 2024. https://doi.org/10.1609/aaai.v38i9.28943.
ieee: G. Trinh, B. Benhamou, S. Pastva, and S. Soliman, “Scalable enumeration of
trap spaces in boolean networks via answer set programming,” in Proceedings
of the 38th AAAI Conference on Artificial Intelligence, 2024, vol. 38, no.
9, pp. 10714–10722.
ista: Trinh G, Benhamou B, Pastva S, Soliman S. 2024. Scalable enumeration of trap
spaces in boolean networks via answer set programming. Proceedings of the 38th
AAAI Conference on Artificial Intelligence. vol. 38, 10714–10722.
mla: Trinh, Giang, et al. “Scalable Enumeration of Trap Spaces in Boolean Networks
via Answer Set Programming.” Proceedings of the 38th AAAI Conference on Artificial
Intelligence, vol. 38, no. 9, Association for the Advancement of Artificial
Intelligence, 2024, pp. 10714–22, doi:10.1609/aaai.v38i9.28943.
short: G. Trinh, B. Benhamou, S. Pastva, S. Soliman, in:, Proceedings of the 38th
AAAI Conference on Artificial Intelligence, Association for the Advancement of
Artificial Intelligence, 2024, pp. 10714–10722.
date_created: 2024-04-14T22:01:02Z
date_published: 2024-03-25T00:00:00Z
date_updated: 2025-04-14T07:54:55Z
day: '25'
department:
- _id: ToHe
doi: 10.1609/aaai.v38i9.28943
ec_funded: 1
intvolume: ' 38'
issue: '9'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://amu.hal.science/hal-04523118/
month: '03'
oa: 1
oa_version: Published Version
page: 10714-10722
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: Proceedings of the 38th AAAI Conference on Artificial Intelligence
publication_identifier:
eissn:
- 2374-3468
isbn:
- '1577358872'
issn:
- 2159-5399
publication_status: published
publisher: Association for the Advancement of Artificial Intelligence
quality_controlled: '1'
scopus_import: '1'
status: public
title: Scalable enumeration of trap spaces in boolean networks via answer set programming
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 38
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '14830'
abstract:
- lang: eng
text: We study the problem of learning controllers for discrete-time non-linear
stochastic dynamical systems with formal reach-avoid guarantees. This work presents
the first method for providing formal reach-avoid guarantees, which combine and
generalize stability and safety guarantees, with a tolerable probability threshold
p in [0,1] over the infinite time horizon. Our method leverages advances in machine
learning literature and it represents formal certificates as neural networks.
In particular, we learn a certificate in the form of a reach-avoid supermartingale
(RASM), a novel notion that we introduce in this work. Our RASMs provide reachability
and avoidance guarantees by imposing constraints on what can be viewed as a stochastic
extension of level sets of Lyapunov functions for deterministic systems. Our approach
solves several important problems -- it can be used to learn a control policy
from scratch, to verify a reach-avoid specification for a fixed control policy,
or to fine-tune a pre-trained policy if it does not satisfy the reach-avoid specification.
We validate our approach on 3 stochastic non-linear reinforcement learning tasks.
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 under the Marie Skłodowska-Curie Grant Agreement No. 665385.
article_processing_charge: No
arxiv: 1
author:
- first_name: Dorde
full_name: Zikelic, Dorde
id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
last_name: Zikelic
orcid: 0000-0002-4681-1699
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- 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: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
citation:
ama: 'Zikelic D, Lechner M, Henzinger TA, Chatterjee K. Learning control policies
for stochastic systems with reach-avoid guarantees. In: Proceedings of the
37th AAAI Conference on Artificial Intelligence. Vol 37. Association for the
Advancement of Artificial Intelligence; 2023:11926-11935. doi:10.1609/aaai.v37i10.26407'
apa: 'Zikelic, D., Lechner, M., Henzinger, T. A., & Chatterjee, K. (2023). Learning
control policies for stochastic systems with reach-avoid guarantees. In Proceedings
of the 37th AAAI Conference on Artificial Intelligence (Vol. 37, pp. 11926–11935).
Washington, DC, United States: Association for the Advancement of Artificial Intelligence.
https://doi.org/10.1609/aaai.v37i10.26407'
chicago: Zikelic, Dorde, Mathias Lechner, Thomas A Henzinger, and Krishnendu Chatterjee.
“Learning Control Policies for Stochastic Systems with Reach-Avoid Guarantees.”
In Proceedings of the 37th AAAI Conference on Artificial Intelligence,
37:11926–35. Association for the Advancement of Artificial Intelligence, 2023.
https://doi.org/10.1609/aaai.v37i10.26407.
ieee: D. Zikelic, M. Lechner, T. A. Henzinger, and K. Chatterjee, “Learning control
policies for stochastic systems with reach-avoid guarantees,” in Proceedings
of the 37th AAAI Conference on Artificial Intelligence, Washington, DC, United
States, 2023, vol. 37, no. 10, pp. 11926–11935.
ista: 'Zikelic D, Lechner M, Henzinger TA, Chatterjee K. 2023. Learning control
policies for stochastic systems with reach-avoid guarantees. Proceedings of the
37th AAAI Conference on Artificial Intelligence. AAAI: Conference on Artificial
Intelligence vol. 37, 11926–11935.'
mla: Zikelic, Dorde, et al. “Learning Control Policies for Stochastic Systems with
Reach-Avoid Guarantees.” Proceedings of the 37th AAAI Conference on Artificial
Intelligence, vol. 37, no. 10, Association for the Advancement of Artificial
Intelligence, 2023, pp. 11926–35, doi:10.1609/aaai.v37i10.26407.
short: D. Zikelic, M. Lechner, T.A. Henzinger, K. Chatterjee, in:, Proceedings of
the 37th AAAI Conference on Artificial Intelligence, Association for the Advancement
of Artificial Intelligence, 2023, pp. 11926–11935.
conference:
end_date: 2023-02-14
location: Washington, DC, United States
name: 'AAAI: Conference on Artificial Intelligence'
start_date: 2023-02-07
corr_author: '1'
date_created: 2024-01-18T07:44:31Z
date_published: 2023-06-26T00:00:00Z
date_updated: 2025-07-03T11:38:12Z
day: '26'
department:
- _id: ToHe
- _id: KrCh
doi: 10.1609/aaai.v37i10.26407
ec_funded: 1
external_id:
arxiv:
- '2210.05308'
intvolume: ' 37'
issue: '10'
keyword:
- General Medicine
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2210.05308
month: '06'
oa: 1
oa_version: Preprint
page: 11926-11935
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 37th AAAI Conference on Artificial Intelligence
publication_identifier:
eissn:
- 2374-3468
issn:
- 2159-5399
publication_status: published
publisher: Association for the Advancement of Artificial Intelligence
quality_controlled: '1'
related_material:
record:
- id: '14600'
relation: earlier_version
status: public
scopus_import: '1'
status: public
title: Learning control policies for stochastic systems with reach-avoid guarantees
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2023'
...
---
_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: 2025-04-15T06:26:14Z
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: Formal methods for the design and analysis of complex systems
- _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: '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
corr_author: '1'
date_created: 2023-02-19T23:00:56Z
date_published: 2022-06-28T00:00:00Z
date_updated: 2024-10-09T21:04:32Z
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'
...
---
_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.
corr_author: '1'
date_created: 2023-02-05T17:29:50Z
date_published: 2022-06-28T00:00:00Z
date_updated: 2026-04-07T13:27:55Z
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: '10669'
abstract:
- lang: eng
text: "We show that Neural ODEs, an emerging class of timecontinuous neural networks,
can be verified by solving a set of global-optimization problems. For this purpose,
we introduce Stochastic Lagrangian Reachability (SLR), an\r\nabstraction-based
technique for constructing a tight Reachtube (an over-approximation of the set
of reachable states\r\nover a given time-horizon), and provide stochastic guarantees
in the form of confidence intervals for the Reachtube bounds. SLR inherently avoids
the infamous wrapping effect (accumulation of over-approximation errors) by performing
local optimization steps to expand safe regions instead of repeatedly forward-propagating
them as is done by deterministic reachability methods. To enable fast local optimizations,
we introduce a novel forward-mode adjoint sensitivity method to compute gradients
without the need for backpropagation. Finally, we establish asymptotic and non-asymptotic
convergence rates for SLR."
acknowledgement: "The authors would like to thank the reviewers for their insightful
comments. RH and RG were partially supported by\r\nHorizon-2020 ECSEL Project grant
No. 783163 (iDev40). RH was partially supported by Boeing. ML was supported\r\nin
part by the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein Award).
SG was funded by FWF\r\nproject W1255-N23. JC was partially supported by NAWA Polish
Returns grant PPN/PPO/2018/1/00029. SS was supported by NSF awards DCL-2040599,
CCF-1918225, and CPS-1446832.\r\n"
alternative_title:
- Technical Tracks
article_processing_charge: No
arxiv: 1
author:
- first_name: Sophie
full_name: Grunbacher, Sophie
last_name: Grunbacher
- first_name: Ramin
full_name: Hasani, Ramin
last_name: Hasani
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Jacek
full_name: Cyranka, Jacek
last_name: Cyranka
- first_name: Scott A
full_name: Smolka, Scott A
last_name: Smolka
- first_name: Radu
full_name: Grosu, Radu
last_name: Grosu
citation:
ama: 'Grunbacher S, Hasani R, Lechner M, Cyranka J, Smolka SA, Grosu R. On the verification
of neural ODEs with stochastic guarantees. In: Proceedings of the AAAI Conference
on Artificial Intelligence. Vol 35. AAAI Press; 2021:11525-11535.'
apa: 'Grunbacher, S., Hasani, R., Lechner, M., Cyranka, J., Smolka, S. A., &
Grosu, R. (2021). On the verification of neural ODEs with stochastic guarantees.
In Proceedings of the AAAI Conference on Artificial Intelligence (Vol.
35, pp. 11525–11535). Virtual: AAAI Press.'
chicago: Grunbacher, Sophie, Ramin Hasani, Mathias Lechner, Jacek Cyranka, Scott
A Smolka, and Radu Grosu. “On the Verification of Neural ODEs with Stochastic
Guarantees.” In Proceedings of the AAAI Conference on Artificial Intelligence,
35:11525–35. AAAI Press, 2021.
ieee: S. Grunbacher, R. Hasani, M. Lechner, J. Cyranka, S. A. Smolka, and R. Grosu,
“On the verification of neural ODEs with stochastic guarantees,” in Proceedings
of the AAAI Conference on Artificial Intelligence, Virtual, 2021, vol. 35,
no. 13, pp. 11525–11535.
ista: 'Grunbacher S, Hasani R, Lechner M, Cyranka J, Smolka SA, Grosu R. 2021. On
the verification of neural ODEs with stochastic guarantees. Proceedings of the
AAAI Conference on Artificial Intelligence. AAAI: Association for the Advancement
of Artificial Intelligence, Technical Tracks, vol. 35, 11525–11535.'
mla: Grunbacher, Sophie, et al. “On the Verification of Neural ODEs with Stochastic
Guarantees.” Proceedings of the AAAI Conference on Artificial Intelligence,
vol. 35, no. 13, AAAI Press, 2021, pp. 11525–35.
short: S. Grunbacher, R. Hasani, M. Lechner, J. Cyranka, S.A. Smolka, R. Grosu,
in:, Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Press,
2021, pp. 11525–11535.
conference:
end_date: 2021-02-09
location: Virtual
name: 'AAAI: Association for the Advancement of Artificial Intelligence'
start_date: 2021-02-02
corr_author: '1'
date_created: 2022-01-25T15:47:20Z
date_published: 2021-05-28T00:00:00Z
date_updated: 2025-04-15T06:25:56Z
day: '28'
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
external_id:
arxiv:
- '2012.08863'
file:
- access_level: open_access
checksum: 468d07041e282a1d46ffdae92f709630
content_type: application/pdf
creator: mlechner
date_created: 2022-01-26T07:38:08Z
date_updated: 2022-01-26T07:38:08Z
file_id: '10680'
file_name: 17372-Article Text-20866-1-2-20210518.pdf
file_size: 286906
relation: main_file
success: 1
file_date_updated: 2022-01-26T07:38:08Z
has_accepted_license: '1'
intvolume: ' 35'
issue: '13'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://ojs.aaai.org/index.php/AAAI/article/view/17372
month: '05'
oa: 1
oa_version: Published Version
page: 11525-11535
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: Formal methods for the design and analysis of complex systems
publication: Proceedings of the AAAI Conference on Artificial Intelligence
publication_identifier:
eissn:
- 2374-3468
isbn:
- 978-1-57735-866-4
issn:
- 2159-5399
publication_status: published
publisher: AAAI Press
quality_controlled: '1'
status: public
title: On the verification of neural ODEs with stochastic guarantees
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 35
year: '2021'
...
---
_id: '10671'
abstract:
- lang: eng
text: We introduce a new class of time-continuous recurrent neural network models.
Instead of declaring a learning system’s dynamics by implicit nonlinearities,
we construct networks of linear first-order dynamical systems modulated via nonlinear
interlinked gates. The resulting models represent dynamical systems with varying
(i.e., liquid) time-constants coupled to their hidden state, with outputs being
computed by numerical differential equation solvers. These neural networks exhibit
stable and bounded behavior, yield superior expressivity within the family of
neural ordinary differential equations, and give rise to improved performance
on time-series prediction tasks. To demonstrate these properties, we first take
a theoretical approach to find bounds over their dynamics, and compute their expressive
power by the trajectory length measure in a latent trajectory space. We then conduct
a series of time-series prediction experiments to manifest the approximation capability
of Liquid Time-Constant Networks (LTCs) compared to classical and modern RNNs.
acknowledgement: "R.H. and D.R. are partially supported by Boeing. R.H. and R.G. were
partially supported by the Horizon-2020 ECSEL\r\nProject grant No. 783163 (iDev40).
M.L. was supported in part by the Austrian Science Fund (FWF) under grant Z211-N23
(Wittgenstein Award). A.A. is supported by the National Science Foundation (NSF)
Graduate Research Fellowship Program. This research work is partially drawn from
the PhD dissertation of R.H."
alternative_title:
- Technical Tracks
article_processing_charge: No
arxiv: 1
author:
- first_name: Ramin
full_name: Hasani, Ramin
last_name: Hasani
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Alexander
full_name: Amini, Alexander
last_name: Amini
- first_name: Daniela
full_name: Rus, Daniela
last_name: Rus
- first_name: Radu
full_name: Grosu, Radu
last_name: Grosu
citation:
ama: 'Hasani R, Lechner M, Amini A, Rus D, Grosu R. Liquid time-constant networks.
In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol
35. AAAI Press; 2021:7657-7666.'
apa: 'Hasani, R., Lechner, M., Amini, A., Rus, D., & Grosu, R. (2021). Liquid
time-constant networks. In Proceedings of the AAAI Conference on Artificial
Intelligence (Vol. 35, pp. 7657–7666). Virtual: AAAI Press.'
chicago: Hasani, Ramin, Mathias Lechner, Alexander Amini, Daniela Rus, and Radu
Grosu. “Liquid Time-Constant Networks.” In Proceedings of the AAAI Conference
on Artificial Intelligence, 35:7657–66. AAAI Press, 2021.
ieee: R. Hasani, M. Lechner, A. Amini, D. Rus, and R. Grosu, “Liquid time-constant
networks,” in Proceedings of the AAAI Conference on Artificial Intelligence,
Virtual, 2021, vol. 35, no. 9, pp. 7657–7666.
ista: 'Hasani R, Lechner M, Amini A, Rus D, Grosu R. 2021. Liquid time-constant
networks. Proceedings of the AAAI Conference on Artificial Intelligence. AAAI:
Association for the Advancement of Artificial Intelligence, Technical Tracks,
vol. 35, 7657–7666.'
mla: Hasani, Ramin, et al. “Liquid Time-Constant Networks.” Proceedings of the
AAAI Conference on Artificial Intelligence, vol. 35, no. 9, AAAI Press, 2021,
pp. 7657–66.
short: R. Hasani, M. Lechner, A. Amini, D. Rus, R. Grosu, in:, Proceedings of the
AAAI Conference on Artificial Intelligence, AAAI Press, 2021, pp. 7657–7666.
conference:
end_date: 2021-02-09
location: Virtual
name: 'AAAI: Association for the Advancement of Artificial Intelligence'
start_date: 2021-02-02
corr_author: '1'
date_created: 2022-01-25T15:48:36Z
date_published: 2021-05-28T00:00:00Z
date_updated: 2025-04-15T06:25:56Z
day: '28'
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
external_id:
arxiv:
- '2006.04439'
file:
- access_level: open_access
checksum: 0f06995fba06dbcfa7ed965fc66027ff
content_type: application/pdf
creator: mlechner
date_created: 2022-01-26T07:36:03Z
date_updated: 2022-01-26T07:36:03Z
file_id: '10678'
file_name: 16936-Article Text-20430-1-2-20210518 (1).pdf
file_size: 4302669
relation: main_file
success: 1
file_date_updated: 2022-01-26T07:36:03Z
has_accepted_license: '1'
intvolume: ' 35'
issue: '9'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://ojs.aaai.org/index.php/AAAI/article/view/16936
month: '05'
oa: 1
oa_version: Published Version
page: 7657-7666
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: Formal methods for the design and analysis of complex systems
publication: Proceedings of the AAAI Conference on Artificial Intelligence
publication_identifier:
eissn:
- 2374-3468
isbn:
- 978-1-57735-866-4
issn:
- 2159-5399
publication_status: published
publisher: AAAI Press
quality_controlled: '1'
status: public
title: Liquid time-constant networks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 35
year: '2021'
...
---
_id: '11436'
abstract:
- lang: eng
text: Asynchronous distributed algorithms are a popular way to reduce synchronization
costs in large-scale optimization, and in particular for neural network training.
However, for nonsmooth and nonconvex objectives, few convergence guarantees exist
beyond cases where closed-form proximal operator solutions are available. As training
most popular deep neural networks corresponds to optimizing nonsmooth and nonconvex
objectives, there is a pressing need for such convergence guarantees. In this
paper, we analyze for the first time the convergence of stochastic asynchronous
optimization for this general class of objectives. In particular, we focus on
stochastic subgradient methods allowing for block variable partitioning, where
the shared model is asynchronously updated by concurrent processes. To this end,
we use a probabilistic model which captures key features of real asynchronous
scheduling between concurrent processes. Under this model, we establish convergence
with probability one to an invariant set for stochastic subgradient methods with
momentum. From a practical perspective, one issue with the family of algorithms
that we consider is that they are not efficiently supported by machine learning
frameworks, which mostly focus on distributed data-parallel strategies. To address
this, we propose a new implementation strategy for shared-memory based training
of deep neural networks for a partitioned but shared model in single- and multi-GPU
settings. Based on this implementation, we achieve on average1.2x speed-up in
comparison to state-of-the-art training methods for popular image classification
tasks, without compromising accuracy.
acknowledgement: Vyacheslav Kungurtsev was supported by the OP VVV project CZ.02.1.01/0.0/0.0/16
019/0000765 “Research Center for Informatics. Bapi Chatterjee was supported by the
European Union’s Horizon 2020 research and innovation programme under the Marie
Sklodowska-Curie grant agreement No. 754411 (ISTPlus). Dan Alistarh has received
funding from the European Research Council (ERC) under the European Union’s Horizon
2020 research and innovation programme (grant agreement No 805223 ScaleML).
article_processing_charge: No
arxiv: 1
author:
- first_name: Vyacheslav
full_name: Kungurtsev, Vyacheslav
last_name: Kungurtsev
- first_name: Malcolm
full_name: Egan, Malcolm
last_name: Egan
- first_name: Bapi
full_name: Chatterjee, Bapi
id: 3C41A08A-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-2742-4028
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Kungurtsev V, Egan M, Chatterjee B, Alistarh D-A. Asynchronous optimization
methods for efficient training of deep neural networks with guarantees. In: 35th
AAAI Conference on Artificial Intelligence, AAAI 2021. Vol 35. AAAI Press;
2021:8209-8216.'
apa: 'Kungurtsev, V., Egan, M., Chatterjee, B., & Alistarh, D.-A. (2021). Asynchronous
optimization methods for efficient training of deep neural networks with guarantees.
In 35th AAAI Conference on Artificial Intelligence, AAAI 2021 (Vol. 35,
pp. 8209–8216). Virtual, Online: AAAI Press.'
chicago: Kungurtsev, Vyacheslav, Malcolm Egan, Bapi Chatterjee, and Dan-Adrian Alistarh.
“Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks
with Guarantees.” In 35th AAAI Conference on Artificial Intelligence, AAAI
2021, 35:8209–16. AAAI Press, 2021.
ieee: V. Kungurtsev, M. Egan, B. Chatterjee, and D.-A. Alistarh, “Asynchronous optimization
methods for efficient training of deep neural networks with guarantees,” in 35th
AAAI Conference on Artificial Intelligence, AAAI 2021, Virtual, Online, 2021,
vol. 35, no. 9B, pp. 8209–8216.
ista: 'Kungurtsev V, Egan M, Chatterjee B, Alistarh D-A. 2021. Asynchronous optimization
methods for efficient training of deep neural networks with guarantees. 35th AAAI
Conference on Artificial Intelligence, AAAI 2021. AAAI: Conference on Artificial
Intelligence vol. 35, 8209–8216.'
mla: Kungurtsev, Vyacheslav, et al. “Asynchronous Optimization Methods for Efficient
Training of Deep Neural Networks with Guarantees.” 35th AAAI Conference on
Artificial Intelligence, AAAI 2021, vol. 35, no. 9B, AAAI Press, 2021, pp.
8209–16.
short: V. Kungurtsev, M. Egan, B. Chatterjee, D.-A. Alistarh, in:, 35th AAAI Conference
on Artificial Intelligence, AAAI 2021, AAAI Press, 2021, pp. 8209–8216.
conference:
end_date: 2021-02-09
location: Virtual, Online
name: 'AAAI: Conference on Artificial Intelligence'
start_date: 2021-02-02
date_created: 2022-06-05T22:01:52Z
date_published: 2021-05-18T00:00:00Z
date_updated: 2025-04-14T07:43:57Z
day: '18'
department:
- _id: DaAl
ec_funded: 1
external_id:
arxiv:
- '1905.11845'
intvolume: ' 35'
issue: 9B
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.1905.11845'
month: '05'
oa: 1
oa_version: Preprint
page: 8209-8216
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: 35th AAAI Conference on Artificial Intelligence, AAAI 2021
publication_identifier:
eissn:
- 2374-3468
isbn:
- '9781713835974'
issn:
- 2159-5399
publication_status: published
publisher: AAAI Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Asynchronous optimization methods for efficient training of deep neural networks
with guarantees
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 35
year: '2021'
...
---
_id: '10665'
abstract:
- lang: eng
text: "Formal verification of neural networks is an active topic of research, and
recent advances have significantly increased the size of the networks that verification
tools can handle. However, most methods are designed for verification of an idealized
model of the actual network which works over real arithmetic and ignores rounding
imprecisions. This idealization is in stark contrast to network quantization,
which is a technique that trades numerical precision for computational efficiency
and is, therefore, often applied in practice. Neglecting rounding errors of such
low-bit quantized neural networks has been shown to lead to wrong conclusions
about the network’s correctness. Thus, the desired approach for verifying quantized
neural networks would be one that takes these rounding errors\r\ninto account.
In this paper, we show that verifying the bitexact implementation of quantized
neural networks with bitvector specifications is PSPACE-hard, even though verifying
idealized real-valued networks and satisfiability of bit-vector specifications
alone are each in NP. Furthermore, we explore several practical heuristics toward
closing the complexity gap between idealized and bit-exact verification. In particular,
we propose three techniques for making SMT-based verification of quantized neural
networks more scalable. Our experiments demonstrate that our proposed methods
allow a speedup of up to three orders of magnitude over existing approaches."
acknowledgement: "This research was supported in part by the Austrian Science Fund
(FWF) under grant Z211-N23 (Wittgenstein\r\nAward), ERC CoG 863818 (FoRM-SMArt),
and the European Union’s Horizon 2020 research and innovation programme under the
Marie Skłodowska-Curie Grant Agreement No. 665385.\r\n"
alternative_title:
- Technical Tracks
article_processing_charge: No
arxiv: 1
author:
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Dorde
full_name: Zikelic, Dorde
id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
last_name: Zikelic
orcid: 0000-0002-4681-1699
citation:
ama: 'Henzinger TA, Lechner M, Zikelic D. Scalable verification of quantized neural
networks. In: Proceedings of the AAAI Conference on Artificial Intelligence.
Vol 35. AAAI Press; 2021:3787-3795.'
apa: 'Henzinger, T. A., Lechner, M., & Zikelic, D. (2021). Scalable verification
of quantized neural networks. In Proceedings of the AAAI Conference on Artificial
Intelligence (Vol. 35, pp. 3787–3795). Virtual: AAAI Press.'
chicago: Henzinger, Thomas A, Mathias Lechner, and Dorde Zikelic. “Scalable Verification
of Quantized Neural Networks.” In Proceedings of the AAAI Conference on Artificial
Intelligence, 35:3787–95. AAAI Press, 2021.
ieee: T. A. Henzinger, M. Lechner, and D. Zikelic, “Scalable verification of quantized
neural networks,” in Proceedings of the AAAI Conference on Artificial Intelligence,
Virtual, 2021, vol. 35, no. 5A, pp. 3787–3795.
ista: 'Henzinger TA, Lechner M, Zikelic D. 2021. Scalable verification of quantized
neural networks. Proceedings of the AAAI Conference on Artificial Intelligence.
AAAI: Association for the Advancement of Artificial Intelligence, Technical Tracks,
vol. 35, 3787–3795.'
mla: Henzinger, Thomas A., et al. “Scalable Verification of Quantized Neural Networks.”
Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35,
no. 5A, AAAI Press, 2021, pp. 3787–95.
short: T.A. Henzinger, M. Lechner, D. Zikelic, in:, Proceedings of the AAAI Conference
on Artificial Intelligence, AAAI Press, 2021, pp. 3787–3795.
conference:
end_date: 2021-02-09
location: Virtual
name: 'AAAI: Association for the Advancement of Artificial Intelligence'
start_date: 2021-02-02
corr_author: '1'
date_created: 2022-01-25T15:15:02Z
date_published: 2021-05-28T00:00:00Z
date_updated: 2026-04-07T14:21:58Z
day: '28'
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
ec_funded: 1
external_id:
arxiv:
- '2012.08185'
file:
- access_level: open_access
checksum: 2bc8155b2526a70fba5b7301bc89dbd1
content_type: application/pdf
creator: mlechner
date_created: 2022-01-26T07:41:16Z
date_updated: 2022-01-26T07:41:16Z
file_id: '10684'
file_name: 16496-Article Text-19990-1-2-20210518 (1).pdf
file_size: 137235
relation: main_file
success: 1
file_date_updated: 2022-01-26T07:41:16Z
has_accepted_license: '1'
intvolume: ' 35'
issue: 5A
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://ojs.aaai.org/index.php/AAAI/article/view/16496
month: '05'
oa: 1
oa_version: Published Version
page: 3787-3795
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: Formal methods for the design and analysis of complex systems
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
publication: Proceedings of the AAAI Conference on Artificial Intelligence
publication_identifier:
eissn:
- 2374-3468
isbn:
- 978-1-57735-866-4
issn:
- 2159-5399
publication_status: published
publisher: AAAI Press
quality_controlled: '1'
related_material:
record:
- id: '11362'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Scalable verification of quantized neural networks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 35
year: '2021'
...
---
_id: '14186'
abstract:
- lang: eng
text: "The goal of the unsupervised learning of disentangled representations is
to\r\nseparate the independent explanatory factors of variation in the data without\r\naccess
to supervision. In this paper, we summarize the results of Locatello et\r\nal.,
2019, and focus on their implications for practitioners. We discuss the\r\ntheoretical
result showing that the unsupervised learning of disentangled\r\nrepresentations
is fundamentally impossible without inductive biases and the\r\npractical challenges
it entails. Finally, we comment on our experimental\r\nfindings, highlighting
the limitations of state-of-the-art approaches and\r\ndirections for future research."
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
- first_name: Stefan
full_name: Bauer, Stefan
last_name: Bauer
- first_name: Mario
full_name: Lucic, Mario
last_name: Lucic
- first_name: Gunnar
full_name: Rätsch, Gunnar
last_name: Rätsch
- first_name: Sylvain
full_name: Gelly, Sylvain
last_name: Gelly
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Olivier
full_name: Bachem, Olivier
last_name: Bachem
citation:
ama: 'Locatello F, Bauer S, Lucic M, et al. A commentary on the unsupervised learning
of disentangled representations. In: The 34th AAAI Conference on Artificial
Intelligence. Vol 34. Association for the Advancement of Artificial Intelligence;
2020:13681-13684. doi:10.1609/aaai.v34i09.7120'
apa: 'Locatello, F., Bauer, S., Lucic, M., Rätsch, G., Gelly, S., Schölkopf, B.,
& Bachem, O. (2020). A commentary on the unsupervised learning of disentangled
representations. In The 34th AAAI Conference on Artificial Intelligence
(Vol. 34, pp. 13681–13684). New York, NY, United States: Association for the Advancement
of Artificial Intelligence. https://doi.org/10.1609/aaai.v34i09.7120'
chicago: Locatello, Francesco, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain
Gelly, Bernhard Schölkopf, and Olivier Bachem. “A Commentary on the Unsupervised
Learning of Disentangled Representations.” In The 34th AAAI Conference on Artificial
Intelligence, 34:13681–84. Association for the Advancement of Artificial Intelligence,
2020. https://doi.org/10.1609/aaai.v34i09.7120.
ieee: F. Locatello et al., “A commentary on the unsupervised learning of
disentangled representations,” in The 34th AAAI Conference on Artificial Intelligence,
New York, NY, United States, 2020, vol. 34, no. 9, pp. 13681–13684.
ista: 'Locatello F, Bauer S, Lucic M, Rätsch G, Gelly S, Schölkopf B, Bachem O.
2020. A commentary on the unsupervised learning of disentangled representations.
The 34th AAAI Conference on Artificial Intelligence. AAAI: Conference on Artificial
Intelligence vol. 34, 13681–13684.'
mla: Locatello, Francesco, et al. “A Commentary on the Unsupervised Learning of
Disentangled Representations.” The 34th AAAI Conference on Artificial Intelligence,
vol. 34, no. 9, Association for the Advancement of Artificial Intelligence, 2020,
pp. 13681–84, doi:10.1609/aaai.v34i09.7120.
short: F. Locatello, S. Bauer, M. Lucic, G. Rätsch, S. Gelly, B. Schölkopf, O. Bachem,
in:, The 34th AAAI Conference on Artificial Intelligence, Association for the
Advancement of Artificial Intelligence, 2020, pp. 13681–13684.
conference:
end_date: 2020-02-12
location: New York, NY, United States
name: 'AAAI: Conference on Artificial Intelligence'
start_date: 2020-02-07
date_created: 2023-08-22T14:07:26Z
date_published: 2020-07-28T00:00:00Z
date_updated: 2023-09-12T07:44:48Z
day: '28'
department:
- _id: FrLo
doi: 10.1609/aaai.v34i09.7120
extern: '1'
external_id:
arxiv:
- '2007.14184'
intvolume: ' 34'
issue: '9'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2007.14184
month: '07'
oa: 1
oa_version: Preprint
page: 13681-13684
publication: The 34th AAAI Conference on Artificial Intelligence
publication_identifier:
eissn:
- 2374-3468
isbn:
- '9781577358350'
publication_status: published
publisher: Association for the Advancement of Artificial Intelligence
quality_controlled: '1'
scopus_import: '1'
status: public
title: A commentary on the unsupervised learning of disentangled representations
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 34
year: '2020'
...
---
OA_place: repository
OA_type: green
_id: '9197'
abstract:
- lang: eng
text: In this paper we introduce and study all-pay bidding games, a class of two
player, zero-sum games on graphs. The game proceeds as follows. We place a token
on some vertex in the graph and assign budgets to the two players. Each turn,
each player submits a sealed legal bid (non-negative and below their remaining
budget), which is deducted from their budget and the highest bidder moves the
token onto an adjacent vertex. The game ends once a sink is reached, and Player
1 pays Player 2 the outcome that is associated with the sink. The players attempt
to maximize their expected outcome. Our games model settings where effort (of
no inherent value) needs to be invested in an ongoing and stateful manner. On
the negative side, we show that even in simple games on DAGs, optimal strategies
may require a distribution over bids with infinite support. A central quantity
in bidding games is the ratio of the players budgets. On the positive side, we
show a simple FPTAS for DAGs, that, for each budget ratio, outputs an approximation
for the optimal strategy for that ratio. We also implement it, show that it performs
well, and suggests interesting properties of these games. Then, given an outcome
c, we show an algorithm for finding the necessary and sufficient initial ratio
for guaranteeing outcome c with probability 1 and a strategy ensuring such. Finally,
while the general case has not previously been studied, solving the specific game
in which Player 1 wins iff he wins the first two auctions, has been long stated
as an open question, which we solve.
acknowledgement: This research was supported by the Austrian Science Fund (FWF) under
grants S11402-N23 (RiSE/SHiNE), Z211-N23 (Wittgenstein Award), and M 2369-N33 (Meitner
fellowship).
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Guy
full_name: Avni, Guy
id: 463C8BC2-F248-11E8-B48F-1D18A9856A87
last_name: Avni
orcid: 0000-0001-5588-8287
- first_name: Rasmus
full_name: Ibsen-Jensen, Rasmus
id: 3B699956-F248-11E8-B48F-1D18A9856A87
last_name: Ibsen-Jensen
orcid: 0000-0003-4783-0389
- first_name: Josef
full_name: Tkadlec, Josef
id: 3F24CCC8-F248-11E8-B48F-1D18A9856A87
last_name: Tkadlec
orcid: 0000-0002-1097-9684
citation:
ama: Avni G, Ibsen-Jensen R, Tkadlec J. All-pay bidding games on graphs. Proceedings
of the AAAI Conference on Artificial Intelligence. 2020;34(02):1798-1805.
doi:10.1609/aaai.v34i02.5546
apa: 'Avni, G., Ibsen-Jensen, R., & Tkadlec, J. (2020). All-pay bidding games
on graphs. Proceedings of the AAAI Conference on Artificial Intelligence.
New York, NY, United States: Association for the Advancement of Artificial Intelligence.
https://doi.org/10.1609/aaai.v34i02.5546'
chicago: Avni, Guy, Rasmus Ibsen-Jensen, and Josef Tkadlec. “All-Pay Bidding Games
on Graphs.” Proceedings of the AAAI Conference on Artificial Intelligence.
Association for the Advancement of Artificial Intelligence, 2020. https://doi.org/10.1609/aaai.v34i02.5546.
ieee: G. Avni, R. Ibsen-Jensen, and J. Tkadlec, “All-pay bidding games on graphs,”
Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34,
no. 02. Association for the Advancement of Artificial Intelligence, pp. 1798–1805,
2020.
ista: Avni G, Ibsen-Jensen R, Tkadlec J. 2020. All-pay bidding games on graphs.
Proceedings of the AAAI Conference on Artificial Intelligence. 34(02), 1798–1805.
mla: Avni, Guy, et al. “All-Pay Bidding Games on Graphs.” Proceedings of the
AAAI Conference on Artificial Intelligence, vol. 34, no. 02, Association for
the Advancement of Artificial Intelligence, 2020, pp. 1798–805, doi:10.1609/aaai.v34i02.5546.
short: G. Avni, R. Ibsen-Jensen, J. Tkadlec, Proceedings of the AAAI Conference
on Artificial Intelligence 34 (2020) 1798–1805.
conference:
end_date: 2020-02-12
location: New York, NY, United States
name: 'AAAI: Conference on Artificial Intelligence'
start_date: 2020-02-07
date_created: 2021-02-25T09:05:18Z
date_published: 2020-04-03T00:00:00Z
date_updated: 2025-07-03T11:44:58Z
day: '03'
department:
- _id: ToHe
- _id: KrCh
doi: 10.1609/aaai.v34i02.5546
external_id:
arxiv:
- '1911.08360'
intvolume: ' 34'
issue: '02'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1911.08360
month: '04'
oa: 1
oa_version: Preprint
page: 1798-1805
project:
- _id: 25F2ACDE-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: S11402-N23
name: Rigorous Systems Engineering
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: Formal methods for the design and analysis of complex systems
- _id: 264B3912-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: M02369
name: Formal Methods meets Algorithmic Game Theory
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'
scopus_import: '1'
status: public
title: All-pay bidding games on graphs
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 34
year: '2020'
...