---
_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
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
- 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: <i>Proceedings of the 37th AAAI Conference on Artificial Intelligence</i>.
    Vol 37. Association for the Advancement of Artificial Intelligence; 2023:14964-14973.
    doi:<a href="https://doi.org/10.1609/aaai.v37i12.26747">10.1609/aaai.v37i12.26747</a>'
  apa: 'Lechner, M., Zikelic, D., Chatterjee, K., Henzinger, T. A., &#38; Rus, D.
    (2023). Quantization-aware interval bound propagation for training certifiably
    robust quantized neural networks. In <i>Proceedings of the 37th AAAI Conference
    on Artificial Intelligence</i> (Vol. 37, pp. 14964–14973). Washington, DC, United
    States: Association for the Advancement of Artificial Intelligence. <a href="https://doi.org/10.1609/aaai.v37i12.26747">https://doi.org/10.1609/aaai.v37i12.26747</a>'
  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 <i>Proceedings of the 37th AAAI Conference
    on Artificial Intelligence</i>, 37:14964–73. Association for the Advancement of
    Artificial Intelligence, 2023. <a href="https://doi.org/10.1609/aaai.v37i12.26747">https://doi.org/10.1609/aaai.v37i12.26747</a>.
  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 <i>Proceedings of the 37th AAAI Conference on Artificial Intelligence</i>,
    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.” <i>Proceedings of the
    37th AAAI Conference on Artificial Intelligence</i>, vol. 37, no. 12, Association
    for the Advancement of Artificial Intelligence, 2023, pp. 14964–73, doi:<a href="https://doi.org/10.1609/aaai.v37i12.26747">10.1609/aaai.v37i12.26747</a>.
  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: 2025-07-14T09:09:56Z
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
arxiv: 1
author:
- first_name: Guy
  full_name: Avni, Guy
  id: 463C8BC2-F248-11E8-B48F-1D18A9856A87
  last_name: Avni
  orcid: 0000-0001-5588-8287
- first_name: Ismael R
  full_name: Jecker, Ismael R
  id: 85D7C63E-7D5D-11E9-9C0F-98C4E5697425
  last_name: Jecker
- first_name: Dorde
  full_name: Zikelic, Dorde
  id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
  last_name: Zikelic
  orcid: 0000-0002-4681-1699
citation:
  ama: 'Avni G, Jecker IR, Zikelic D. Bidding graph games with partially-observable
    budgets. In: <i>Proceedings of the 37th AAAI Conference on Artificial Intelligence</i>.
    Vol 37. ; 2023:5464-5471. doi:<a href="https://doi.org/10.1609/aaai.v37i5.25679">10.1609/aaai.v37i5.25679</a>'
  apa: Avni, G., Jecker, I. R., &#38; Zikelic, D. (2023). Bidding graph games with
    partially-observable budgets. In <i>Proceedings of the 37th AAAI Conference on
    Artificial Intelligence</i> (Vol. 37, pp. 5464–5471). Washington, DC, United States.
    <a href="https://doi.org/10.1609/aaai.v37i5.25679">https://doi.org/10.1609/aaai.v37i5.25679</a>
  chicago: Avni, Guy, Ismael R Jecker, and Dorde Zikelic. “Bidding Graph Games with
    Partially-Observable Budgets.” In <i>Proceedings of the 37th AAAI Conference on
    Artificial Intelligence</i>, 37:5464–71, 2023. <a href="https://doi.org/10.1609/aaai.v37i5.25679">https://doi.org/10.1609/aaai.v37i5.25679</a>.
  ieee: G. Avni, I. R. Jecker, and D. Zikelic, “Bidding graph games with partially-observable
    budgets,” in <i>Proceedings of the 37th AAAI Conference on Artificial Intelligence</i>,
    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.”
    <i>Proceedings of the 37th AAAI Conference on Artificial Intelligence</i>, vol.
    37, no. 5, 2023, pp. 5464–71, doi:<a href="https://doi.org/10.1609/aaai.v37i5.25679">10.1609/aaai.v37i5.25679</a>.
  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: 2025-07-14T09:09:56Z
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'
...