---
_id: '12467'
abstract:
- lang: eng
text: Safety and liveness are elementary concepts of computation, and the foundation
of many verification paradigms. The safety-liveness classification of boolean
properties characterizes whether a given property can be falsified by observing
a finite prefix of an infinite computation trace (always for safety, never for
liveness). In quantitative specification and verification, properties assign not
truth values, but quantitative values to infinite traces (e.g., a cost, or the
distance to a boolean property). We introduce quantitative safety and liveness,
and we prove that our definitions induce conservative quantitative generalizations
of both (1)~the safety-progress hierarchy of boolean properties and (2)~the safety-liveness
decomposition of boolean properties. In particular, we show that every quantitative
property can be written as the pointwise minimum of a quantitative safety property
and a quantitative liveness property. Consequently, like boolean properties, also
quantitative properties can be min-decomposed into safety and liveness parts,
or alternatively, max-decomposed into co-safety and co-liveness parts. Moreover,
quantitative properties can be approximated naturally. We prove that every quantitative
property that has both safe and co-safe approximations can be monitored arbitrarily
precisely by a monitor that uses only a finite number of states.
acknowledgement: We thank the anonymous reviewers for their helpful comments. This
work was supported in part by the ERC-2020-AdG 101020093.
alternative_title:
- LNCS
article_processing_charge: No
author:
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
- first_name: Nicolas Adrien
full_name: Mazzocchi, Nicolas Adrien
id: b26baa86-3308-11ec-87b0-8990f34baa85
last_name: Mazzocchi
- first_name: Naci E
full_name: Sarac, Naci E
id: 8C6B42F8-C8E6-11E9-A03A-F2DCE5697425
last_name: Sarac
citation:
ama: 'Henzinger TA, Mazzocchi NA, Sarac NE. Quantitative safety and liveness. In:
26th International Conference Foundations of Software Science and Computation
Structures. Vol 13992. Springer Nature; 2023:349-370. doi:10.1007/978-3-031-30829-1_17'
apa: 'Henzinger, T. A., Mazzocchi, N. A., & Sarac, N. E. (2023). Quantitative
safety and liveness. In 26th International Conference Foundations of Software
Science and Computation Structures (Vol. 13992, pp. 349–370). Paris, France:
Springer Nature. https://doi.org/10.1007/978-3-031-30829-1_17'
chicago: Henzinger, Thomas A, Nicolas Adrien Mazzocchi, and Naci E Sarac. “Quantitative
Safety and Liveness.” In 26th International Conference Foundations of Software
Science and Computation Structures, 13992:349–70. Springer Nature, 2023. https://doi.org/10.1007/978-3-031-30829-1_17.
ieee: T. A. Henzinger, N. A. Mazzocchi, and N. E. Sarac, “Quantitative safety and
liveness,” in 26th International Conference Foundations of Software Science
and Computation Structures, Paris, France, 2023, vol. 13992, pp. 349–370.
ista: 'Henzinger TA, Mazzocchi NA, Sarac NE. 2023. Quantitative safety and liveness.
26th International Conference Foundations of Software Science and Computation
Structures. FOSSACS: Foundations of Software Science and Computation Structures,
LNCS, vol. 13992, 349–370.'
mla: Henzinger, Thomas A., et al. “Quantitative Safety and Liveness.” 26th International
Conference Foundations of Software Science and Computation Structures, vol.
13992, Springer Nature, 2023, pp. 349–70, doi:10.1007/978-3-031-30829-1_17.
short: T.A. Henzinger, N.A. Mazzocchi, N.E. Sarac, in:, 26th International Conference
Foundations of Software Science and Computation Structures, Springer Nature, 2023,
pp. 349–370.
conference:
end_date: 2023-04-27
location: Paris, France
name: 'FOSSACS: Foundations of Software Science and Computation Structures'
start_date: 2023-04-22
date_created: 2023-01-31T07:23:56Z
date_published: 2023-04-21T00:00:00Z
date_updated: 2023-07-14T11:20:27Z
day: '21'
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
doi: 10.1007/978-3-031-30829-1_17
ec_funded: 1
external_id:
arxiv:
- '2301.11175'
file:
- access_level: open_access
checksum: 981025aed580b6b27c426cb8856cf63e
content_type: application/pdf
creator: esarac
date_created: 2023-01-31T07:22:21Z
date_updated: 2023-01-31T07:22:21Z
file_id: '12468'
file_name: qsl.pdf
file_size: 449027
relation: main_file
success: 1
- access_level: open_access
checksum: f16e2af1e0eb243158ab0f0fe74e7d5a
content_type: application/pdf
creator: dernst
date_created: 2023-06-19T10:28:09Z
date_updated: 2023-06-19T10:28:09Z
file_id: '13153'
file_name: 2023_LNCS_HenzingerT.pdf
file_size: 1048171
relation: main_file
success: 1
file_date_updated: 2023-06-19T10:28:09Z
has_accepted_license: '1'
intvolume: ' 13992'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '04'
oa: 1
oa_version: Published Version
page: 349-370
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
publication: 26th International Conference Foundations of Software Science and Computation
Structures
publication_identifier:
eissn:
- 1611-3349
isbn:
- '9783031308284'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Quantitative safety and liveness
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
volume: 13992
year: '2023'
...
---
_id: '13292'
abstract:
- lang: eng
text: The operator precedence languages (OPLs) represent the largest known subclass
of the context-free languages which enjoys all desirable closure and decidability
properties. This includes the decidability of language inclusion, which is the
ultimate verification problem. Operator precedence grammars, automata, and logics
have been investigated and used, for example, to verify programs with arithmetic
expressions and exceptions (both of which are deterministic pushdown but lie outside
the scope of the visibly pushdown languages). In this paper, we complete the picture
and give, for the first time, an algebraic characterization of the class of OPLs
in the form of a syntactic congruence that has finitely many equivalence classes
exactly for the operator precedence languages. This is a generalization of the
celebrated Myhill-Nerode theorem for the regular languages to OPLs. As one of
the consequences, we show that universality and language inclusion for nondeterministic
operator precedence automata can be solved by an antichain algorithm. Antichain
algorithms avoid determinization and complementation through an explicit subset
construction, by leveraging a quasi-order on words, which allows the pruning of
the search space for counterexample words without sacrificing completeness. Antichain
algorithms can be implemented symbolically, and these implementations are today
the best-performing algorithms in practice for the inclusion of finite automata.
We give a generic construction of the quasi-order needed for antichain algorithms
from a finite syntactic congruence. This yields the first antichain algorithm
for OPLs, an algorithm that solves the ExpTime-hard language inclusion problem
for OPLs in exponential time.
acknowledgement: "This work was supported in part by the ERC-2020-AdG 101020093.\r\nWe
thank Pierre Ganty for early discussions and the anonymous reviewers for their helpful
comments.\r\n"
alternative_title:
- LIPIcs
article_processing_charge: Yes
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: Pavol
full_name: Kebis, Pavol
last_name: Kebis
- first_name: Nicolas Adrien
full_name: Mazzocchi, Nicolas Adrien
id: b26baa86-3308-11ec-87b0-8990f34baa85
last_name: Mazzocchi
- first_name: Naci E
full_name: Sarac, Naci E
id: 8C6B42F8-C8E6-11E9-A03A-F2DCE5697425
last_name: Sarac
citation:
ama: 'Henzinger TA, Kebis P, Mazzocchi NA, Sarac NE. Regular methods for operator
precedence languages. In: 50th International Colloquium on Automata, Languages,
and Programming. Vol 261. Schloss Dagstuhl - Leibniz-Zentrum für Informatik;
2023:129:1--129:20. doi:10.4230/LIPIcs.ICALP.2023.129'
apa: 'Henzinger, T. A., Kebis, P., Mazzocchi, N. A., & Sarac, N. E. (2023).
Regular methods for operator precedence languages. In 50th International Colloquium
on Automata, Languages, and Programming (Vol. 261, p. 129:1--129:20). Paderborn,
Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.ICALP.2023.129'
chicago: Henzinger, Thomas A, Pavol Kebis, Nicolas Adrien Mazzocchi, and Naci E
Sarac. “Regular Methods for Operator Precedence Languages.” In 50th International
Colloquium on Automata, Languages, and Programming, 261:129:1--129:20. Schloss
Dagstuhl - Leibniz-Zentrum für Informatik, 2023. https://doi.org/10.4230/LIPIcs.ICALP.2023.129.
ieee: T. A. Henzinger, P. Kebis, N. A. Mazzocchi, and N. E. Sarac, “Regular methods
for operator precedence languages,” in 50th International Colloquium on Automata,
Languages, and Programming, Paderborn, Germany, 2023, vol. 261, p. 129:1--129:20.
ista: 'Henzinger TA, Kebis P, Mazzocchi NA, Sarac NE. 2023. Regular methods for
operator precedence languages. 50th International Colloquium on Automata, Languages,
and Programming. ICALP: International Colloquium on Automata, Languages, and Programming,
LIPIcs, vol. 261, 129:1--129:20.'
mla: Henzinger, Thomas A., et al. “Regular Methods for Operator Precedence Languages.”
50th International Colloquium on Automata, Languages, and Programming,
vol. 261, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023, p. 129:1--129:20,
doi:10.4230/LIPIcs.ICALP.2023.129.
short: T.A. Henzinger, P. Kebis, N.A. Mazzocchi, N.E. Sarac, in:, 50th International
Colloquium on Automata, Languages, and Programming, Schloss Dagstuhl - Leibniz-Zentrum
für Informatik, 2023, p. 129:1--129:20.
conference:
end_date: 2023-07-14
location: Paderborn, Germany
name: 'ICALP: International Colloquium on Automata, Languages, and Programming'
start_date: 2023-07-10
date_created: 2023-07-24T15:11:41Z
date_published: 2023-07-05T00:00:00Z
date_updated: 2023-07-31T08:38:38Z
day: '05'
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
doi: 10.4230/LIPIcs.ICALP.2023.129
ec_funded: 1
external_id:
arxiv:
- '2305.03447'
file:
- access_level: open_access
checksum: 5d4c8932ef3450615a53b9bb15d92eb2
content_type: application/pdf
creator: esarac
date_created: 2023-07-24T15:11:05Z
date_updated: 2023-07-24T15:11:05Z
file_id: '13293'
file_name: icalp23.pdf
file_size: 859379
relation: main_file
success: 1
file_date_updated: 2023-07-24T15:11:05Z
has_accepted_license: '1'
intvolume: ' 261'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: 129:1--129:20
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
publication: 50th International Colloquium on Automata, Languages, and Programming
publication_identifier:
eissn:
- 1868-8969
isbn:
- '9783959772785'
publication_status: published
publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik
quality_controlled: '1'
status: public
title: Regular methods for operator precedence languages
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 261
year: '2023'
...
---
_id: '12704'
abstract:
- lang: eng
text: Adversarial training (i.e., training on adversarially perturbed input data)
is a well-studied method for making neural networks robust to potential adversarial
attacks during inference. However, the improved robustness does not come for free
but rather is accompanied by a decrease in overall model accuracy and performance.
Recent work has shown that, in practical robot learning applications, the effects
of adversarial training do not pose a fair trade-off but inflict a net loss when
measured in holistic robot performance. This work revisits the robustness-accuracy
trade-off in robot learning by systematically analyzing if recent advances in
robust training methods and theory in conjunction with adversarial robot learning,
are capable of making adversarial training suitable for real-world robot applications.
We evaluate three different robot learning tasks ranging from autonomous driving
in a high-fidelity environment amenable to sim-to-real deployment to mobile robot
navigation and gesture recognition. Our results demonstrate that, while these
techniques make incremental improvements on the trade-off on a relative scale,
the negative impact on the nominal accuracy caused by adversarial training still
outweighs the improved robustness by an order of magnitude. We conclude that although
progress is happening, further advances in robust learning methods are necessary
before they can benefit robot learning tasks in practice.
acknowledgement: "We thank Christoph Lampert for inspiring this work. The\r\nviews
and conclusions contained in this document are those of\r\nthe authors and should
not be interpreted as representing the\r\nofficial policies, either expressed or
implied, of the United States\r\nAir Force or the U.S. Government. The U.S. Government
is\r\nauthorized to reproduce and distribute reprints for Government\r\npurposes
notwithstanding any copyright notation herein."
article_processing_charge: No
article_type: original
author:
- 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: 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, Amini A, Rus D, Henzinger TA. Revisiting the adversarial robustness-accuracy
tradeoff in robot learning. IEEE Robotics and Automation Letters. 2023;8(3):1595-1602.
doi:10.1109/LRA.2023.3240930
apa: Lechner, M., Amini, A., Rus, D., & Henzinger, T. A. (2023). Revisiting
the adversarial robustness-accuracy tradeoff in robot learning. IEEE Robotics
and Automation Letters. Institute of Electrical and Electronics Engineers.
https://doi.org/10.1109/LRA.2023.3240930
chicago: Lechner, Mathias, Alexander Amini, Daniela Rus, and Thomas A Henzinger.
“Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning.” IEEE
Robotics and Automation Letters. Institute of Electrical and Electronics Engineers,
2023. https://doi.org/10.1109/LRA.2023.3240930.
ieee: M. Lechner, A. Amini, D. Rus, and T. A. Henzinger, “Revisiting the adversarial
robustness-accuracy tradeoff in robot learning,” IEEE Robotics and Automation
Letters, vol. 8, no. 3. Institute of Electrical and Electronics Engineers,
pp. 1595–1602, 2023.
ista: Lechner M, Amini A, Rus D, Henzinger TA. 2023. Revisiting the adversarial
robustness-accuracy tradeoff in robot learning. IEEE Robotics and Automation Letters.
8(3), 1595–1602.
mla: Lechner, Mathias, et al. “Revisiting the Adversarial Robustness-Accuracy Tradeoff
in Robot Learning.” IEEE Robotics and Automation Letters, vol. 8, no. 3,
Institute of Electrical and Electronics Engineers, 2023, pp. 1595–602, doi:10.1109/LRA.2023.3240930.
short: M. Lechner, A. Amini, D. Rus, T.A. Henzinger, IEEE Robotics and Automation
Letters 8 (2023) 1595–1602.
date_created: 2023-03-05T23:01:04Z
date_published: 2023-03-01T00:00:00Z
date_updated: 2023-08-01T13:36:50Z
day: '01'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.1109/LRA.2023.3240930
external_id:
arxiv:
- '2204.07373'
isi:
- '000936534100012'
file:
- access_level: open_access
checksum: 5a75dcd326ea66685de2b1aaec259e85
content_type: application/pdf
creator: cchlebak
date_created: 2023-03-07T12:22:23Z
date_updated: 2023-03-07T12:22:23Z
file_id: '12714'
file_name: 2023_IEEERobAutLetters_Lechner.pdf
file_size: 944052
relation: main_file
success: 1
file_date_updated: 2023-03-07T12:22:23Z
has_accepted_license: '1'
intvolume: ' 8'
isi: 1
issue: '3'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
page: 1595-1602
publication: IEEE Robotics and Automation Letters
publication_identifier:
eissn:
- 2377-3766
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
related_material:
record:
- id: '11366'
relation: earlier_version
status: public
scopus_import: '1'
status: public
title: Revisiting the adversarial robustness-accuracy tradeoff in robot learning
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 8
year: '2023'
...
---
_id: '12876'
abstract:
- lang: eng
text: "Motivation: The problem of model inference is of fundamental importance to
systems biology. Logical models (e.g. Boolean networks; BNs) represent a computationally
attractive approach capable of handling large biological networks. The models
are typically inferred from experimental data. However, even with a substantial
amount of experimental data supported by some prior knowledge, existing inference
methods often focus on a small sample of admissible candidate models only.\r\n\r\nResults:
We propose Boolean network sketches as a new formal instrument for the inference
of Boolean networks. A sketch integrates (typically partial) knowledge about the
network’s topology and the update logic (obtained through, e.g. a biological knowledge
base or a literature search), as well as further assumptions about the properties
of the network’s transitions (e.g. the form of its attractor landscape), and additional
restrictions on the model dynamics given by the measured experimental data. Our
new BNs inference algorithm starts with an ‘initial’ sketch, which is extended
by adding restrictions representing experimental data to a ‘data-informed’ sketch
and subsequently computes all BNs consistent with the data-informed sketch. Our
algorithm is based on a symbolic representation and coloured model-checking. Our
approach is unique in its ability to cover a broad spectrum of knowledge and efficiently
produce a compact representation of all inferred BNs. We evaluate the method on
a non-trivial collection of real-world and simulated data."
acknowledgement: This work was partially supported by GACR [grant No. GA22-10845S];
and Grant Agency of Masaryk University [grant No. MUNI/G/1771/2020]. This work was
partially supported by European Union’s Horizon 2020 research and innovation programme
under the Marie Skłodowska-Curie [Grant Agreement No. 101034413 to S.P.].
article_number: btad158
article_processing_charge: No
article_type: original
author:
- first_name: Nikola
full_name: Beneš, Nikola
last_name: Beneš
- first_name: Luboš
full_name: Brim, Luboš
last_name: Brim
- first_name: Ondřej
full_name: Huvar, Ondřej
last_name: Huvar
- first_name: Samuel
full_name: Pastva, Samuel
id: 07c5ea74-f61c-11ec-a664-aa7c5d957b2b
last_name: Pastva
- first_name: David
full_name: Šafránek, David
last_name: Šafránek
citation:
ama: 'Beneš N, Brim L, Huvar O, Pastva S, Šafránek D. Boolean network sketches:
A unifying framework for logical model inference. Bioinformatics. 2023;39(4).
doi:10.1093/bioinformatics/btad158'
apa: 'Beneš, N., Brim, L., Huvar, O., Pastva, S., & Šafránek, D. (2023). Boolean
network sketches: A unifying framework for logical model inference. Bioinformatics.
Oxford Academic. https://doi.org/10.1093/bioinformatics/btad158'
chicago: 'Beneš, Nikola, Luboš Brim, Ondřej Huvar, Samuel Pastva, and David Šafránek.
“Boolean Network Sketches: A Unifying Framework for Logical Model Inference.”
Bioinformatics. Oxford Academic, 2023. https://doi.org/10.1093/bioinformatics/btad158.'
ieee: 'N. Beneš, L. Brim, O. Huvar, S. Pastva, and D. Šafránek, “Boolean network
sketches: A unifying framework for logical model inference,” Bioinformatics,
vol. 39, no. 4. Oxford Academic, 2023.'
ista: 'Beneš N, Brim L, Huvar O, Pastva S, Šafránek D. 2023. Boolean network sketches:
A unifying framework for logical model inference. Bioinformatics. 39(4), btad158.'
mla: 'Beneš, Nikola, et al. “Boolean Network Sketches: A Unifying Framework for
Logical Model Inference.” Bioinformatics, vol. 39, no. 4, btad158, Oxford
Academic, 2023, doi:10.1093/bioinformatics/btad158.'
short: N. Beneš, L. Brim, O. Huvar, S. Pastva, D. Šafránek, Bioinformatics 39 (2023).
date_created: 2023-04-30T22:01:05Z
date_published: 2023-04-03T00:00:00Z
date_updated: 2023-08-01T14:27:28Z
day: '03'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.1093/bioinformatics/btad158
ec_funded: 1
external_id:
isi:
- '000976610800001'
pmid:
- '37004199'
file:
- access_level: open_access
checksum: 2cb90ddf781baefddf47eac4b54e2a03
content_type: application/pdf
creator: dernst
date_created: 2023-05-02T07:39:04Z
date_updated: 2023-05-02T07:39:04Z
file_id: '12886'
file_name: 2023_Bioinformatics_Benes.pdf
file_size: 478740
relation: main_file
success: 1
file_date_updated: 2023-05-02T07:39:04Z
has_accepted_license: '1'
intvolume: ' 39'
isi: 1
issue: '4'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: Bioinformatics
publication_identifier:
eissn:
- 1367-4811
publication_status: published
publisher: Oxford Academic
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://doi.org/10.5281/zenodo.7688740
scopus_import: '1'
status: public
title: 'Boolean network sketches: A unifying framework for logical model inference'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 39
year: '2023'
...
---
_id: '14242'
abstract:
- lang: eng
text: We study the problem of training and certifying adversarially robust quantized
neural networks (QNNs). Quantization is a technique for making neural networks
more efficient by running them using low-bit integer arithmetic and is therefore
commonly adopted in industry. Recent work has shown that floating-point neural
networks that have been verified to be robust can become vulnerable to adversarial
attacks after quantization, and certification of the quantized representation
is necessary to guarantee robustness. In this work, we present quantization-aware
interval bound propagation (QA-IBP), a novel method for training robust QNNs.
Inspired by advances in robust learning of non-quantized networks, our training
algorithm computes the gradient of an abstract representation of the actual network.
Unlike existing approaches, our method can handle the discrete semantics of QNNs.
Based on QA-IBP, we also develop a complete verification procedure for verifying
the adversarial robustness of QNNs, which is guaranteed to terminate and produce
a correct answer. Compared to existing approaches, the key advantage of our verification
procedure is that it runs entirely on GPU or other accelerator devices. We demonstrate
experimentally that our approach significantly outperforms existing methods and
establish the new state-of-the-art for training and certifying the robustness
of QNNs.
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. Research
was sponsored by the United\r\nStates Air Force Research Laboratory and the United
States Air Force Artificial Intelligence Accelerator and was accomplished under
Cooperative Agreement Number FA8750-19-2-\r\n1000. The views and conclusions contained
in this document are those of the authors and should not be interpreted as representing
the official policies, either expressed or implied,\r\nof the United States Air
Force or the U.S. Government. The U.S. Government is authorized to reproduce and
distribute reprints for Government purposes notwithstanding any copyright\r\nnotation
herein. The research was also funded in part by the AI2050 program at Schmidt Futures
(Grant G-22-63172) and Capgemini SE."
article_processing_charge: No
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
- first_name: Daniela
full_name: Rus, Daniela
last_name: Rus
citation:
ama: 'Lechner M, Zikelic D, Chatterjee K, Henzinger TA, Rus D. Quantization-aware
interval bound propagation for training certifiably robust quantized neural networks.
In: Proceedings of the 37th AAAI Conference on Artificial Intelligence.
Vol 37. Association for the Advancement of Artificial Intelligence; 2023:14964-14973.
doi:10.1609/aaai.v37i12.26747'
apa: 'Lechner, M., Zikelic, D., Chatterjee, K., Henzinger, T. A., & Rus, D.
(2023). Quantization-aware interval bound propagation for training certifiably
robust quantized neural networks. In Proceedings of the 37th AAAI Conference
on Artificial Intelligence (Vol. 37, pp. 14964–14973). Washington, DC, United
States: Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v37i12.26747'
chicago: Lechner, Mathias, Dorde Zikelic, Krishnendu Chatterjee, Thomas A Henzinger,
and Daniela Rus. “Quantization-Aware Interval Bound Propagation for Training Certifiably
Robust Quantized Neural Networks.” In Proceedings of the 37th AAAI Conference
on Artificial Intelligence, 37:14964–73. Association for the Advancement of
Artificial Intelligence, 2023. https://doi.org/10.1609/aaai.v37i12.26747.
ieee: M. Lechner, D. Zikelic, K. Chatterjee, T. A. Henzinger, and D. Rus, “Quantization-aware
interval bound propagation for training certifiably robust quantized neural networks,”
in Proceedings of the 37th AAAI Conference on Artificial Intelligence,
Washington, DC, United States, 2023, vol. 37, no. 12, pp. 14964–14973.
ista: 'Lechner M, Zikelic D, Chatterjee K, Henzinger TA, Rus D. 2023. Quantization-aware
interval bound propagation for training certifiably robust quantized neural networks.
Proceedings of the 37th AAAI Conference on Artificial Intelligence. AAAI: Conference
on Artificial Intelligence vol. 37, 14964–14973.'
mla: Lechner, Mathias, et al. “Quantization-Aware Interval Bound Propagation for
Training Certifiably Robust Quantized Neural Networks.” Proceedings of the
37th AAAI Conference on Artificial Intelligence, vol. 37, no. 12, Association
for the Advancement of Artificial Intelligence, 2023, pp. 14964–73, doi:10.1609/aaai.v37i12.26747.
short: M. Lechner, D. Zikelic, K. Chatterjee, T.A. Henzinger, D. Rus, in:, Proceedings
of the 37th AAAI Conference on Artificial Intelligence, Association for the Advancement
of Artificial Intelligence, 2023, pp. 14964–14973.
conference:
end_date: 2023-02-14
location: Washington, DC, United States
name: 'AAAI: Conference on Artificial Intelligence'
start_date: 2023-02-07
date_created: 2023-08-27T22:01:17Z
date_published: 2023-06-26T00:00:00Z
date_updated: 2023-09-05T07:06:14Z
day: '26'
department:
- _id: ToHe
- _id: KrCh
doi: 10.1609/aaai.v37i12.26747
ec_funded: 1
external_id:
arxiv:
- '2211.16187'
intvolume: ' 37'
issue: '12'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2211.16187
month: '06'
oa: 1
oa_version: Preprint
page: 14964-14973
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:
isbn:
- '9781577358800'
publication_status: published
publisher: Association for the Advancement of Artificial Intelligence
quality_controlled: '1'
scopus_import: '1'
status: public
title: Quantization-aware interval bound propagation for training certifiably robust
quantized neural networks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2023'
...
---
_id: '14243'
abstract:
- lang: eng
text: 'Two-player zero-sum "graph games" are central in logic, verification, and
multi-agent systems. The game proceeds by placing a token on a vertex of a graph,
and allowing the players to move it to produce an infinite path, which determines
the winner or payoff of the game. Traditionally, the players alternate turns in
moving the token. In "bidding games", however, the players have budgets and in
each turn, an auction (bidding) determines which player moves the token. So far,
bidding games have only been studied as full-information games. In this work we
initiate the study of partial-information bidding games: we study bidding games
in which a player''s initial budget is drawn from a known probability distribution.
We show that while for some bidding mechanisms and objectives, it is straightforward
to adapt the results from the full-information setting to the partial-information
setting, for others, the analysis is significantly more challenging, requires
new techniques, and gives rise to interesting results. Specifically, we study
games with "mean-payoff" objectives in combination with "poorman" bidding. We
construct optimal strategies for a partially-informed player who plays against
a fully-informed adversary. We show that, somewhat surprisingly, the "value" under
pure strategies does not necessarily exist in such games.'
acknowledgement: This research was supported in part by ISF grant no.1679/21, by the
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
author:
- first_name: Guy
full_name: Avni, Guy
id: 463C8BC2-F248-11E8-B48F-1D18A9856A87
last_name: Avni
orcid: 0000-0001-5588-8287
- first_name: Ismael R
full_name: Jecker, Ismael R
id: 85D7C63E-7D5D-11E9-9C0F-98C4E5697425
last_name: Jecker
- first_name: Dorde
full_name: Zikelic, Dorde
id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
last_name: Zikelic
orcid: 0000-0002-4681-1699
citation:
ama: 'Avni G, Jecker IR, Zikelic D. Bidding graph games with partially-observable
budgets. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence.
Vol 37. ; 2023:5464-5471. doi:10.1609/aaai.v37i5.25679'
apa: Avni, G., Jecker, I. R., & Zikelic, D. (2023). Bidding graph games with
partially-observable budgets. In Proceedings of the 37th AAAI Conference on
Artificial Intelligence (Vol. 37, pp. 5464–5471). Washington, DC, United States.
https://doi.org/10.1609/aaai.v37i5.25679
chicago: Avni, Guy, Ismael R Jecker, and Dorde Zikelic. “Bidding Graph Games with
Partially-Observable Budgets.” In Proceedings of the 37th AAAI Conference on
Artificial Intelligence, 37:5464–71, 2023. https://doi.org/10.1609/aaai.v37i5.25679.
ieee: G. Avni, I. R. Jecker, and D. Zikelic, “Bidding graph games with partially-observable
budgets,” in Proceedings of the 37th AAAI Conference on Artificial Intelligence,
Washington, DC, United States, 2023, vol. 37, no. 5, pp. 5464–5471.
ista: 'Avni G, Jecker IR, Zikelic D. 2023. Bidding graph games with partially-observable
budgets. Proceedings of the 37th AAAI Conference on Artificial Intelligence. AAAI:
Conference on Artificial Intelligence vol. 37, 5464–5471.'
mla: Avni, Guy, et al. “Bidding Graph Games with Partially-Observable Budgets.”
Proceedings of the 37th AAAI Conference on Artificial Intelligence, vol.
37, no. 5, 2023, pp. 5464–71, doi:10.1609/aaai.v37i5.25679.
short: G. Avni, I.R. Jecker, D. Zikelic, in:, Proceedings of the 37th AAAI Conference
on Artificial Intelligence, 2023, pp. 5464–5471.
conference:
end_date: 2023-02-14
location: Washington, DC, United States
name: 'AAAI: Conference on Artificial Intelligence'
start_date: 2023-02-07
date_created: 2023-08-27T22:01:18Z
date_published: 2023-06-27T00:00:00Z
date_updated: 2023-09-05T08:37:00Z
day: '27'
department:
- _id: ToHe
- _id: KrCh
doi: 10.1609/aaai.v37i5.25679
ec_funded: 1
external_id:
arxiv:
- '2211.13626'
intvolume: ' 37'
issue: '5'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1609/aaai.v37i5.25679
month: '06'
oa: 1
oa_version: Published Version
page: 5464-5471
project:
- _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:
isbn:
- '9781577358800'
publication_status: published
quality_controlled: '1'
scopus_import: '1'
status: public
title: Bidding graph games with partially-observable budgets
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2023'
...
---
_id: '13310'
abstract:
- lang: eng
text: Machine-learned systems are in widespread use for making decisions about humans,
and it is important that they are fair, i.e., not biased against individuals based
on sensitive attributes. We present runtime verification of algorithmic fairness
for systems whose models are unknown, but are assumed to have a Markov chain structure.
We introduce a specification language that can model many common algorithmic fairness
properties, such as demographic parity, equal opportunity, and social burden.
We build monitors that observe a long sequence of events as generated by a given
system, and output, after each observation, a quantitative estimate of how fair
or biased the system was on that run until that point in time. The estimate is
proven to be correct modulo a variable error bound and a given confidence level,
where the error bound gets tighter as the observed sequence gets longer. Our monitors
are of two types, and use, respectively, frequentist and Bayesian statistical
inference techniques. While the frequentist monitors compute estimates that are
objectively correct with respect to the ground truth, the Bayesian monitors compute
estimates that are correct subject to a given prior belief about the system’s
model. Using a prototype implementation, we show how we can monitor if a bank
is fair in giving loans to applicants from different social backgrounds, and if
a college is fair in admitting students while maintaining a reasonable financial
burden on the society. Although they exhibit different theoretical complexities
in certain cases, in our experiments, both frequentist and Bayesian monitors took
less than a millisecond to update their verdicts after each observation.
acknowledgement: 'This work is supported by the European Research Council under Grant
No.: ERC-2020-AdG101020093.'
alternative_title:
- LNCS
article_processing_charge: Yes (in subscription journal)
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: Mahyar
full_name: Karimi, Mahyar
id: f1dedef5-2f78-11ee-989a-c4c97bccf506
last_name: Karimi
orcid: 0009-0005-0820-1696
- 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: 'Henzinger TA, Karimi M, Kueffner K, Mallik K. Monitoring algorithmic fairness.
In: Computer Aided Verification. Vol 13965. Springer Nature; 2023:358–382.
doi:10.1007/978-3-031-37703-7_17'
apa: 'Henzinger, T. A., Karimi, M., Kueffner, K., & Mallik, K. (2023). Monitoring
algorithmic fairness. In Computer Aided Verification (Vol. 13965, pp. 358–382).
Paris, France: Springer Nature. https://doi.org/10.1007/978-3-031-37703-7_17'
chicago: Henzinger, Thomas A, Mahyar Karimi, Konstantin Kueffner, and Kaushik Mallik.
“Monitoring Algorithmic Fairness.” In Computer Aided Verification, 13965:358–382.
Springer Nature, 2023. https://doi.org/10.1007/978-3-031-37703-7_17.
ieee: T. A. Henzinger, M. Karimi, K. Kueffner, and K. Mallik, “Monitoring algorithmic
fairness,” in Computer Aided Verification, Paris, France, 2023, vol. 13965,
pp. 358–382.
ista: 'Henzinger TA, Karimi M, Kueffner K, Mallik K. 2023. Monitoring algorithmic
fairness. Computer Aided Verification. CAV: Computer Aided Verification, LNCS,
vol. 13965, 358–382.'
mla: Henzinger, Thomas A., et al. “Monitoring Algorithmic Fairness.” Computer
Aided Verification, vol. 13965, Springer Nature, 2023, pp. 358–382, doi:10.1007/978-3-031-37703-7_17.
short: T.A. Henzinger, M. Karimi, K. Kueffner, K. Mallik, in:, Computer Aided Verification,
Springer Nature, 2023, pp. 358–382.
conference:
end_date: 2023-07-22
location: Paris, France
name: 'CAV: Computer Aided Verification'
start_date: 2023-07-17
date_created: 2023-07-25T18:32:40Z
date_published: 2023-07-18T00:00:00Z
date_updated: 2023-09-05T15:14:00Z
day: '18'
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
doi: 10.1007/978-3-031-37703-7_17
ec_funded: 1
external_id:
arxiv:
- '2305.15979'
file:
- access_level: open_access
checksum: ccaf94bf7d658ba012c016e11869b54c
content_type: application/pdf
creator: dernst
date_created: 2023-07-31T08:11:20Z
date_updated: 2023-07-31T08:11:20Z
file_id: '13327'
file_name: 2023_LNCS_CAV_HenzingerT.pdf
file_size: 647760
relation: main_file
success: 1
file_date_updated: 2023-07-31T08:11:20Z
has_accepted_license: '1'
intvolume: ' 13965'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: 358–382
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
publication: Computer Aided Verification
publication_identifier:
eisbn:
- '9783031377037'
eissn:
- 1611-3349
isbn:
- '9783031377020'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
status: public
title: Monitoring algorithmic fairness
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 13965
year: '2023'
...
---
_id: '13221'
abstract:
- lang: eng
text: The safety-liveness dichotomy is a fundamental concept in formal languages
which plays a key role in verification. Recently, this dichotomy has been lifted
to quantitative properties, which are arbitrary functions from infinite words
to partially-ordered domains. We look into harnessing the dichotomy for the specific
classes of quantitative properties expressed by quantitative automata. These automata
contain finitely many states and rational-valued transition weights, and their
common value functions Inf, Sup, LimInf, LimSup, LimInfAvg, LimSupAvg, and DSum
map infinite words into the totallyordered domain of real numbers. In this automata-theoretic
setting, we establish a connection between quantitative safety and topological
continuity and provide an alternative characterization of quantitative safety
and liveness in terms of their boolean counterparts. For all common value functions,
we show how the safety closure of a quantitative automaton can be constructed
in PTime, and we provide PSpace-complete checks of whether a given quantitative
automaton is safe or live, with the exception of LimInfAvg and LimSupAvg automata,
for which the safety check is in ExpSpace. Moreover, for deterministic Sup, LimInf,
and LimSup automata, we give PTime decompositions into safe and live automata.
These decompositions enable the separation of techniques for safety and liveness
verification for quantitative specifications.
acknowledgement: We thank Christof Löding for pointing us to some results on PSpace-hardess
of universality problems and the anonymous reviewers for their helpful comments.
This work was supported in part by the ERC-2020-AdG 101020093 and the Israel Science
Foundation grant 2410/22.
alternative_title:
- LIPIcs
article_number: '17'
article_processing_charge: No
author:
- first_name: Udi
full_name: Boker, Udi
id: 31E297B6-F248-11E8-B48F-1D18A9856A87
last_name: Boker
- 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: Nicolas Adrien
full_name: Mazzocchi, Nicolas Adrien
id: b26baa86-3308-11ec-87b0-8990f34baa85
last_name: Mazzocchi
- first_name: Naci E
full_name: Sarac, Naci E
id: 8C6B42F8-C8E6-11E9-A03A-F2DCE5697425
last_name: Sarac
citation:
ama: 'Boker U, Henzinger TA, Mazzocchi NA, Sarac NE. Safety and liveness of quantitative
automata. In: 34th International Conference on Concurrency Theory. Vol
279. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2023. doi:10.4230/LIPIcs.CONCUR.2023.17'
apa: 'Boker, U., Henzinger, T. A., Mazzocchi, N. A., & Sarac, N. E. (2023).
Safety and liveness of quantitative automata. In 34th International Conference
on Concurrency Theory (Vol. 279). Antwerp, Belgium: Schloss Dagstuhl - Leibniz-Zentrum
für Informatik. https://doi.org/10.4230/LIPIcs.CONCUR.2023.17'
chicago: Boker, Udi, Thomas A Henzinger, Nicolas Adrien Mazzocchi, and Naci E Sarac.
“Safety and Liveness of Quantitative Automata.” In 34th International Conference
on Concurrency Theory, Vol. 279. Schloss Dagstuhl - Leibniz-Zentrum für Informatik,
2023. https://doi.org/10.4230/LIPIcs.CONCUR.2023.17.
ieee: U. Boker, T. A. Henzinger, N. A. Mazzocchi, and N. E. Sarac, “Safety and liveness
of quantitative automata,” in 34th International Conference on Concurrency
Theory, Antwerp, Belgium, 2023, vol. 279.
ista: 'Boker U, Henzinger TA, Mazzocchi NA, Sarac NE. 2023. Safety and liveness
of quantitative automata. 34th International Conference on Concurrency Theory.
CONCUR: Conference on Concurrency Theory, LIPIcs, vol. 279, 17.'
mla: Boker, Udi, et al. “Safety and Liveness of Quantitative Automata.” 34th
International Conference on Concurrency Theory, vol. 279, 17, Schloss Dagstuhl
- Leibniz-Zentrum für Informatik, 2023, doi:10.4230/LIPIcs.CONCUR.2023.17.
short: U. Boker, T.A. Henzinger, N.A. Mazzocchi, N.E. Sarac, in:, 34th International
Conference on Concurrency Theory, Schloss Dagstuhl - Leibniz-Zentrum für Informatik,
2023.
conference:
end_date: 2023-09-23
location: Antwerp, Belgium
name: 'CONCUR: Conference on Concurrency Theory'
start_date: 2023-09-18
date_created: 2023-07-14T10:00:15Z
date_published: 2023-09-01T00:00:00Z
date_updated: 2023-10-09T07:14:03Z
day: '01'
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
doi: 10.4230/LIPIcs.CONCUR.2023.17
ec_funded: 1
external_id:
arxiv:
- '2307.06016'
file:
- access_level: open_access
checksum: d40e57a04448ea5c77d7e1cfb9590a81
content_type: application/pdf
creator: esarac
date_created: 2023-07-14T12:03:48Z
date_updated: 2023-07-14T12:03:48Z
file_id: '13224'
file_name: CONCUR23.pdf
file_size: 755529
relation: main_file
success: 1
file_date_updated: 2023-07-14T12:03:48Z
has_accepted_license: '1'
intvolume: ' 279'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
publication: 34th International Conference on Concurrency Theory
publication_identifier:
eissn:
- 1868-8969
isbn:
- '9783959772990'
publication_status: published
publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik
quality_controlled: '1'
status: public
title: Safety and liveness of quantitative automata
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 279
year: '2023'
...
---
_id: '14405'
abstract:
- lang: eng
text: We introduce hypernode automata as a new specification formalism for hyperproperties
of concurrent systems. They are finite automata with nodes labeled with hypernode
logic formulas and transitions labeled with actions. A hypernode logic formula
specifies relations between sequences of variable values in different system executions.
Unlike HyperLTL, hypernode logic takes an asynchronous view on execution traces
by constraining the values and the order of value changes of each variable without
correlating the timing of the changes. Different execution traces are synchronized
solely through the transitions of hypernode automata. Hypernode automata naturally
combine asynchronicity at the node level with synchronicity at the transition
level. We show that the model-checking problem for hypernode automata is decidable
over action-labeled Kripke structures, whose actions induce transitions of the
specification automata. For this reason, hypernode automaton is a suitable formalism
for specifying and verifying asynchronous hyperproperties, such as declassifying
observational determinism in multi-threaded programs.
acknowledgement: "This work was supported in part by the Austrian Science Fund (FWF)
SFB project\r\nSpyCoDe F8502, by the FWF projects ZK-35 and W1255-N23, and by the
ERC Advanced Grant\r\nVAMOS 101020093."
alternative_title:
- LIPIcs
article_number: '21'
article_processing_charge: Yes
author:
- first_name: Ezio
full_name: Bartocci, Ezio
last_name: Bartocci
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
- first_name: Dejan
full_name: Nickovic, Dejan
id: 41BCEE5C-F248-11E8-B48F-1D18A9856A87
last_name: Nickovic
- first_name: Ana
full_name: Oliveira da Costa, Ana
id: f347ec37-6676-11ee-b395-a888cb7b4fb4
last_name: Oliveira da Costa
orcid: 0000-0002-8741-5799
citation:
ama: 'Bartocci E, Henzinger TA, Nickovic D, Oliveira da Costa A. Hypernode automata.
In: 34th International Conference on Concurrency Theory. Vol 279. Schloss
Dagstuhl - Leibniz-Zentrum für Informatik; 2023. doi:10.4230/LIPIcs.CONCUR.2023.21'
apa: 'Bartocci, E., Henzinger, T. A., Nickovic, D., & Oliveira da Costa, A.
(2023). Hypernode automata. In 34th International Conference on Concurrency
Theory (Vol. 279). Antwerp, Belgium: Schloss Dagstuhl - Leibniz-Zentrum für
Informatik. https://doi.org/10.4230/LIPIcs.CONCUR.2023.21'
chicago: Bartocci, Ezio, Thomas A Henzinger, Dejan Nickovic, and Ana Oliveira da
Costa. “Hypernode Automata.” In 34th International Conference on Concurrency
Theory, Vol. 279. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023.
https://doi.org/10.4230/LIPIcs.CONCUR.2023.21.
ieee: E. Bartocci, T. A. Henzinger, D. Nickovic, and A. Oliveira da Costa, “Hypernode
automata,” in 34th International Conference on Concurrency Theory, Antwerp,
Belgium, 2023, vol. 279.
ista: 'Bartocci E, Henzinger TA, Nickovic D, Oliveira da Costa A. 2023. Hypernode
automata. 34th International Conference on Concurrency Theory. CONCUR: Conference
on Concurrency Theory, LIPIcs, vol. 279, 21.'
mla: Bartocci, Ezio, et al. “Hypernode Automata.” 34th International Conference
on Concurrency Theory, vol. 279, 21, Schloss Dagstuhl - Leibniz-Zentrum für
Informatik, 2023, doi:10.4230/LIPIcs.CONCUR.2023.21.
short: E. Bartocci, T.A. Henzinger, D. Nickovic, A. Oliveira da Costa, in:, 34th
International Conference on Concurrency Theory, Schloss Dagstuhl - Leibniz-Zentrum
für Informatik, 2023.
conference:
end_date: 2023-09-22
location: Antwerp, Belgium
name: 'CONCUR: Conference on Concurrency Theory'
start_date: 2023-09-19
date_created: 2023-10-08T22:01:16Z
date_published: 2023-09-01T00:00:00Z
date_updated: 2023-10-09T07:43:44Z
day: '01'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.4230/LIPIcs.CONCUR.2023.21
ec_funded: 1
external_id:
arxiv:
- '2305.02836'
file:
- access_level: open_access
checksum: 215765e40454d806174ac0a223e8d6fa
content_type: application/pdf
creator: dernst
date_created: 2023-10-09T07:42:45Z
date_updated: 2023-10-09T07:42:45Z
file_id: '14413'
file_name: 2023_LIPcs_Bartocci.pdf
file_size: 795790
relation: main_file
success: 1
file_date_updated: 2023-10-09T07:42:45Z
has_accepted_license: '1'
intvolume: ' 279'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
publication: 34th International Conference on Concurrency Theory
publication_identifier:
isbn:
- '9783959772990'
issn:
- '18688969'
publication_status: published
publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik
quality_controlled: '1'
scopus_import: '1'
status: public
title: Hypernode automata
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 279
year: '2023'
...
---
_id: '14454'
abstract:
- lang: eng
text: As AI and machine-learned software are used increasingly for making decisions
that affect humans, it is imperative that they remain fair and unbiased in their
decisions. To complement design-time bias mitigation measures, runtime verification
techniques have been introduced recently to monitor the algorithmic fairness of
deployed systems. Previous monitoring techniques assume full observability of
the states of the (unknown) monitored system. Moreover, they can monitor only
fairness properties that are specified as arithmetic expressions over the probabilities
of different events. In this work, we extend fairness monitoring to systems modeled
as partially observed Markov chains (POMC), and to specifications containing arithmetic
expressions over the expected values of numerical functions on event sequences.
The only assumptions we make are that the underlying POMC is aperiodic and starts
in the stationary distribution, with a bound on its mixing time being known. These
assumptions enable us to estimate a given property for the entire distribution
of possible executions of the monitored POMC, by observing only a single execution.
Our monitors observe a long run of the system and, after each new observation,
output updated PAC-estimates of how fair or biased the system is. The monitors
are computationally lightweight and, using a prototype implementation, we demonstrate
their effectiveness on several real-world examples.
acknowledgement: 'This work is supported by the European Research Council under Grant
No.: ERC-2020-AdG 101020093.'
alternative_title:
- LNCS
article_processing_charge: No
author:
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
- first_name: 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: 'Henzinger TA, Kueffner K, Mallik K. Monitoring algorithmic fairness under
partial observations. In: 23rd International Conference on Runtime Verification.
Vol 14245. Springer Nature; 2023:291-311. doi:10.1007/978-3-031-44267-4_15'
apa: 'Henzinger, T. A., Kueffner, K., & Mallik, K. (2023). Monitoring algorithmic
fairness under partial observations. In 23rd International Conference on Runtime
Verification (Vol. 14245, pp. 291–311). Thessaloniki, Greece: Springer Nature.
https://doi.org/10.1007/978-3-031-44267-4_15'
chicago: Henzinger, Thomas A, Konstantin Kueffner, and Kaushik Mallik. “Monitoring
Algorithmic Fairness under Partial Observations.” In 23rd International Conference
on Runtime Verification, 14245:291–311. Springer Nature, 2023. https://doi.org/10.1007/978-3-031-44267-4_15.
ieee: T. A. Henzinger, K. Kueffner, and K. Mallik, “Monitoring algorithmic fairness
under partial observations,” in 23rd International Conference on Runtime Verification,
Thessaloniki, Greece, 2023, vol. 14245, pp. 291–311.
ista: 'Henzinger TA, Kueffner K, Mallik K. 2023. Monitoring algorithmic fairness
under partial observations. 23rd International Conference on Runtime Verification.
RV: Conference on Runtime Verification, LNCS, vol. 14245, 291–311.'
mla: Henzinger, Thomas A., et al. “Monitoring Algorithmic Fairness under Partial
Observations.” 23rd International Conference on Runtime Verification, vol.
14245, Springer Nature, 2023, pp. 291–311, doi:10.1007/978-3-031-44267-4_15.
short: T.A. Henzinger, K. Kueffner, K. Mallik, in:, 23rd International Conference
on Runtime Verification, Springer Nature, 2023, pp. 291–311.
conference:
end_date: 2023-10-06
location: Thessaloniki, Greece
name: 'RV: Conference on Runtime Verification'
start_date: 2023-10-03
date_created: 2023-10-29T23:01:15Z
date_published: 2023-10-01T00:00:00Z
date_updated: 2023-10-31T11:48:20Z
day: '01'
department:
- _id: ToHe
doi: 10.1007/978-3-031-44267-4_15
ec_funded: 1
external_id:
arxiv:
- '2308.00341'
intvolume: ' 14245'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2308.00341
month: '10'
oa: 1
oa_version: Preprint
page: 291-311
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
publication: 23rd International Conference on Runtime Verification
publication_identifier:
eissn:
- 1611-3349
isbn:
- '9783031442667'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Monitoring algorithmic fairness under partial observations
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 14245
year: '2023'
...