| Out-of-distribution generalization via risk extrapolation (rex) D Krueger, E Caballero, JH Jacobsen, A Zhang, J Binas, D Zhang, ... ICML 2021; arXiv preprint arXiv:2003.00688, 2020 | 163 | 2020 |
| Improving sample efficiency in model-free reinforcement learning from images D Yarats, A Zhang, I Kostrikov, B Amos, J Pineau, R Fergus AAAI 2021, 2019 | 135 | 2019 |
| A dissection of overfitting and generalization in continuous reinforcement learning A Zhang, N Ballas, J Pineau arXiv preprint arXiv:1806.07937, 2018 | 122 | 2018 |
| Learning invariant representations for reinforcement learning without reconstruction A Zhang, R McAllister, R Calandra, Y Gal, S Levine ICLR 2021; arXiv:2006.10742, 2020 | 121 | 2020 |
| Mapping the world population one building at a time TG Tiecke, X Liu, A Zhang, A Gros, N Li, G Yetman, T Kilic, S Murray, ... arXiv preprint arXiv:1712.05839, 2017 | 78 | 2017 |
| Guess who rated this movie: Identifying users through subspace clustering A Zhang, N Fawaz, S Ioannidis, A Montanari Uncertainty in Artificial Intelligence, 2012 | 71 | 2012 |
| Managing your private and public data: Bringing down inference attacks against your privacy S Salamatian, A Zhang, F du Pin Calmon, S Bhamidipati, N Fawaz, ... IEEE Journal of Selected Topics in Signal Processing 9 (7), 1240-1255, 2015 | 67 | 2015 |
| How to hide the elephant-or the donkey-in the room: Practical privacy against statistical inference for large data S Salamatian, A Zhang, F du Pin Calmon, S Bhamidipati, N Fawaz, ... 2013 IEEE Global Conference on Signal and Information Processing, 269-272, 2013 | 67 | 2013 |
| Composable planning with attributes A Zhang, S Sukhbaatar, A Lerer, A Szlam, R Fergus International Conference on Machine Learning, 5842-5851, 2018 | 62 | 2018 |
| Decoupling dynamics and reward for transfer learning A Zhang, H Satija, J Pineau arXiv preprint arXiv:1804.10689, 2018 | 54 | 2018 |
| Natural environment benchmarks for reinforcement learning A Zhang, Y Wu, J Pineau arXiv preprint arXiv:1811.06032, 2018 | 42 | 2018 |
| Invariant causal prediction for block mdps A Zhang, C Lyle, S Sodhani, A Filos, M Kwiatkowska, J Pineau, Y Gal, ... ICML 2020; arXiv preprint arXiv:2003.06016, 2020 | 34 | 2020 |
| Learning causal state representations of partially observable environments A Zhang, ZC Lipton, L Pineda, K Azizzadenesheli, A Anandkumar, L Itti, ... arXiv preprint arXiv:1906.10437, 2019 | 28 | 2019 |
| Multi-Task Reinforcement Learning with Context-based Representations S Sodhani, A Zhang, J Pineau ICML 2021; arXiv preprint arXiv:2102.06177, 2021 | 25 | 2021 |
| Building detection from satellite images on a global scale A Zhang, X Liu, A Gros, T Tiecke arXiv preprint arXiv:1707.08952, 2017 | 25 | 2017 |
| A survey of generalisation in deep reinforcement learning R Kirk, A Zhang, E Grefenstette, T Rocktäschel arXiv preprint arXiv:2111.09794, 2021 | 18 | 2021 |
| Plan2Vec: Unsupervised Representation Learning by Latent Plans G Yang, A Zhang, AS Morcos, J Pineau, P Abbeel, R Calandra L4DC 2020; arXiv preprint arXiv:2005.03648, 2020 | 14 | 2020 |
| Learning Robust State Abstractions for Hidden-Parameter Block MDPs A Zhang, S Sodhani, K Khetarpal, J Pineau ICLR 2021; arXiv:2007.07206, 2020 | 13* | 2020 |
| Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability D Ghosh, J Rahme, A Kumar, A Zhang, RP Adams, S Levine NeurIPS 2021, 2021 | 10 | 2021 |
| Feedback neural network for weakly supervised geo-semantic segmentation X Liu, A Zhang, T Tiecke, A Gros, TS Huang arXiv preprint arXiv:1612.02766, 2016 | 10 | 2016 |
Joelle PineauSchool of Computer Science, McGill UniversityVerified email at cs.mcgill.ca
