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Prior work found that superhuman Go AIs can be defeated by simple adversarial strategies, especially "cyclic" attacks.
Stochastic Games
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Approximate exploitability: Learning a best response in large games
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Mastering the game of Go with deep neural networks and tree search
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A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
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A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
Silver, D.; Hubert, T.; Schrittwieser, J.; Antonoglou, I.; Lai, M.; Guez, A.; Lanctot, M.; Sifre, L.; Kumaran, D.; Graepel, T.; Lillicrap, T.; Simonyan, K.; and Hassabis, D. 2018 · 2018
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Regret Minimization in Games with Incomplete Information
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SAI a Sensible Artificial Intelligence that plays Go
Morandin, F.; Amato, G.; Gini, R.; Metta, C.; Parton, M.; and Pascutto, G.-C. 2019 · 2019
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ELF OpenGo: an analysis and open reimplementation of AlphaZero
Tian, Y.; Ma, J.; Gong, Q.; Sengupta, S.; Chen, Z.; Pinkerton, J.; and Zitnick, L. 2019 · 2019
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Robustness May Be at Odds with Accuracy
Tsipras, D.; Santurkar, S.; Engstrom, L.; Turner, A.; and Madry, A. 2019 · 2019
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Grandmaster level in StarCraft II using multi-agent reinforcement learning
Vinyals, O.; Babuschkin, I.; Czarnecki, W. M.; Mathieu, M.; Dudzik, A.; Chung, J.; Choi, D. H.; Powell, R.; Ewalds, T.; Georgiev, P.; et al. 2019 · 2019
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Stateful detection of black-box adversarial attacks
Chen, S.; Carlini, N.; and Wagner, D. 2020 · 2020
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Adversarial Policies: Attacking Deep Reinforcement Learning
Gleave, A.; Dennis, M.; Wild, C.; Kant, N.; Levine, S.; and Russell, S. 2020 · 2020
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Mastering Atari, Go, chess and shogi by planning with a learned model
Schrittwieser, J.; Antonoglou, I.; Hubert, T.; Simonyan, K.; Sifre, L.; Schmitt, S.; Guez, A.; Lockhart, E.; Hassabis, D.; Graepel, T.; Lillicrap, T.; and Silver, D. 2020 · 2020
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Are transformers more robust than CNNs?
Bai, Y.; Mei, J.; Yuille, A. L.; and Xie, C. 2021 · 2021
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Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs
Benz, P.; Ham, S.; Zhang, C.; Karjauv, A.; and Kweon, I. S. 2021 · 2021
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Understanding robustness of transformers for image classification
Bhojanapalli, S.; Chakrabarti, A.; Glasner, D.; Li, D.; Unterthiner, T.; and Veit, A. 2021 · 2021
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RobustBench: a standardized adversarial robustness benchmark
Croce, F.; Andriushchenko, M.; Sehwag, V.; Debenedetti, E.; Flammarion, N.; Chiang, M.; Mittal, P.; and Hein, M. 2021 · 2021
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On the Robustness of Vision Transformers to Adversarial Examples
Mahmood, K.; Mahmood, R.; and van Dijk, M. 2021 · 2021
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From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization
Perolat, J.; Munos, R.; Lespiau, J.-B.; Omidshafiei, S.; Rowland, M.; Ortega, P.; Burch, N.; Anthony, T.; Balduzzi, D.; De Vylder, B.; Piliouras, G.; Lanctot, M.; and Tuyls, K. 2021 · 2021
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A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking
Liu, C.; Dong, Y.; Xiang, W.; Yang, X.; Su, H.; Zhu, J.; Chen, Y.; He, Y.; Xue, H.; and Zheng, S. 2023 · 2023
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Subtle adversarial image manipulations influence both human and machine perception
Veerabadran, V.; Goldman, J.; Shankar, S.; Cheung, B.; Papernot, N.; Kurakin, A.; Goodfellow, I.; Shlens, J.; Sohl-Dickstein, J.; Mozer, M. C.; et al. 2023 · 2023
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Defending Against Unforeseen Failure Modes with Latent Adversarial Training
Casper, S.; Schulze, L.; Patel, O.; and Hadfield-Menell, D. 2024 · 2024
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Worst-case guarantees
Christiano, P. 2019 · 2024
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Comment on Even Superhuman Go AIs Have Surprising Failure Modes
Gleave, A. 2023 · 2024
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Reducing Exploitability with Population Based Training
Czempin, P.; and Gleave, A. 2022 · 2022
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Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?
Fu, Y.; Zhang, S.; Wu, S.; Wan, C.; and Lin, Y. 2022 · 2022
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EfficientFormer: Vision transformers at MobileNet speed
Li, Y.; Yuan, G.; Wen, Y.; Hu, J.; Evangelidis, G.; Tulyakov, S.; Wang, Y.; and Ren, J. 2022 · 2022
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Leela Zero
Pascutto, G.-C. 2019b · 2022
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Mastering the game of Stratego with model-free multiagent reinforcement learning
Perolat, J.; De Vylder, B.; Hennes, D.; Tarassov, E.; Strub, F.; de Boer, V.; Muller, P.; Connor, J. T.; Burch, N.; Anthony, T.; et al. 2022 · 2022
Cited alongside, same era.
An impartial take to the CNN vs transformer robustness contest
Pinto, F.; Torr, P. H.; and K. Dokania, P. 2022 · 2022
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On the Adversarial Robustness of Vision Transformers
Shao, R.; Shi, Z.; Yi, J.; Chen, P.-Y.; and Hsieh, C.-J. 2022 · 2022
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Relaxed adversarial training for inner alignment
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