@inproceedings{14242,
  abstract     = {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.},
  author       = {Lechner, Mathias and Zikelic, Dorde and Chatterjee, Krishnendu and Henzinger, Thomas A and Rus, Daniela},
  booktitle    = {Proceedings of the 37th AAAI Conference on Artificial Intelligence},
  isbn         = {9781577358800},
  location     = {Washington, DC, United States},
  number       = {12},
  pages        = {14964--14973},
  publisher    = {Association for the Advancement of Artificial Intelligence},
  title        = {{Quantization-aware interval bound propagation for training certifiably robust quantized neural networks}},
  doi          = {10.1609/aaai.v37i12.26747},
  volume       = {37},
  year         = {2023},
}

@inproceedings{14243,
  abstract     = {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.},
  author       = {Avni, Guy and Jecker, Ismael R and Zikelic, Dorde},
  booktitle    = {Proceedings of the 37th AAAI Conference on Artificial Intelligence},
  isbn         = {9781577358800},
  location     = {Washington, DC, United States},
  number       = {5},
  pages        = {5464--5471},
  title        = {{Bidding graph games with partially-observable budgets}},
  doi          = {10.1609/aaai.v37i5.25679},
  volume       = {37},
  year         = {2023},
}