| Adversarial policies: Attacking deep reinforcement learning A Gleave, M Dennis, C Wild, N Kant, S Levine, S Russell International Conference on Learning Representations 2020, 2020 | 462 | 2020 |
| A deep learning approach to fast, format-agnostic detection of malicious web content J Saxe, R Harang, C Wild, H Sanders 2018 IEEE Security and Privacy Workshops (SPW), 8-14, 2018 | 45 | 2018 |
| Clusterability in neural networks D Filan, S Casper, S Hod, C Wild, A Critch, S Russell arXiv preprint arXiv:2103.03386, 2021 | 37 | 2021 |
| An Empirical Investigation of Representation Learning for Imitation X Chen, S Toyer, C Wild, S Emmons, I Fischer, KH Lee, N Alex, SH Wang, ... NeurIPS 2021, Datasets and Benchmarks Track, 2021 | 33 | 2021 |
| {ALOHA}: Auxiliary loss optimization for hypothesis augmentation EM Rudd, FN Ducau, C Wild, K Berlin, R Harang 28th USENIX Security Symposium (USENIX Security 19), 303-320, 2019 | 31 | 2019 |
| The MineRL BASALT competition on learning from human feedback R Shah, C Wild, SH Wang, N Alex, B Houghton, W Guss, S Mohanty, ... NeurIPS Competitions 2021, 2021 | 28 | 2021 |
| Pruned neural networks are surprisingly modular D Filan, S Hod, C Wild, A Critch, S Russell arXiv preprint arXiv:2003.04881, 2020 | 20 | 2020 |
| Graphical clusterability and local specialization in deep neural networks S Casper, S Hod, D Filan, C Wild, A Critch, S Russell ICLR 2022 Workshop on PAIR {\textasciicircum} 2Struct: Privacy …, 2022 | 15 | 2022 |
| Detecting modularity in deep neural networks S Hod, S Casper, D Filan, C Wild, A Critch, S Russell | 12 | 2021 |
| Retrospective on the 2021 minerl BASALT competition on learning from human feedback R Shah, SH Wang, C Wild, S Milani, A Kanervisto, VG Goecks, ... NeurIPS 2021 Competitions and Demonstrations Track, 259-272, 2022 | 9 | 2022 |
| Adversarial policies: Attacking deep reinforcement learning. arXiv 2019 A Gleave, M Dennis, C Wild, N Kant, S Levine, S Russell arXiv preprint arXiv:1905.10615, 0 | 5 | |
| Retrospective on the 2021 BASALT competition on learning from human feedback R Shah, SH Wang, C Wild, S Milani, A Kanervisto, VG Goecks, ... arXiv preprint arXiv:2204.07123, 2022 | 2 | 2022 |
| Importance and Coherence: Methods for Evaluating Modularity in Neural Networks S Hod, S Casper, D Filan, C Wild, A Critch, S Russell | 2 | 2020 |
| Augmented security recognition tasks RE Harang, EMA Rudd, K Berlin, CM Wild, FN Ducau US Patent App. 18/323,607, 2024 | | 2024 |
| Augmented security recognition tasks RE Harang, EMA Rudd, K Berlin, CM Wild, FN Ducau US Patent 11,681,800, 2023 | | 2023 |
| Project Plan: Benchmarking Representation Learning for Imitation Learning X Chen, S Toyer, C Wild, N Alex, BS Emmons, S Wang, S Russell, ... | | 2020 |
| Using Variational Autoencoders to Learn Variations in Data EM Rudd, C Wild | | |
| SPW 2018 N Carlini, D Wagner, J Saxe, R Harang, C Wild, H Sanders, P Chong, ... | | |
Stuart RussellProfessor of Computer Science, University of California, BerkeleyVerified email at cs.berkeley.edu
