| Out-of-distribution generalization via risk extrapolation (rex) D Krueger, E Caballero, JH Jacobsen, A Zhang, J Binas, D Zhang, ... ICML 2021 Long talk, 2020 | 935 | 2020 |
| You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle D Zhang, T Zhang, Y Lu, Z Zhu, B Dong NeurIPS 2019; arXiv preprint arXiv:1905.00877, 2019 | 484 | 2019 |
| Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization K Ahuja, E Caballero, D Zhang, JC Gagnon-Audet, Y Bengio, I Mitliagkas, ... NeurIPS 2021 spotlight; arXiv preprint arXiv:2106.06607, 2021 | 265 | 2021 |
| Biological Sequence Design with GFlowNets M Jain, E Bengio, AH Garcia, J Rector-Brooks, BFP Dossou, C Ekbote, ... ICML 2022; arXiv preprint arXiv:2203.04115, 2022 | 159 | 2022 |
| Informative Dropout for Robust Representation Learning: A Shape-bias Perspective B Shi, D Zhang, Q Dai, Z Zhu, Y Mu, J Wang ICML 2020, 2020 | 122 | 2020 |
| Can Subnetwork Structure be the Key to Out-of-Distribution Generalization? D Zhang, K Ahuja, Y Xu, Y Wang, A Courville ICML 2021 long talk; arXiv preprint arXiv:2106.02890, 2021 | 101 | 2021 |
| Generative Flow Networks for Discrete Probabilistic Modeling D Zhang, N Malkin, Z Liu, A Volokhova, A Courville, Y Bengio ICML 2022; arXiv preprint arXiv:2202.01361, 2022 | 90 | 2022 |
| Black-box certification with randomized smoothing: A functional optimization based framework D Zhang, M Ye, C Gong, Z Zhu, Q Liu NeurIPS 2020, 2020 | 74 | 2020 |
| GFlowNets and variational inference N Malkin, S Lahlou, T Deleu, X Ji, E Hu, K Everett, D Zhang, Y Bengio ICLR 2023; arXiv preprint arXiv:2210.00580, 2022 | 69 | 2022 |
| Neural Approximate Sufficient Statistics for Implicit Models Y Chen*, D Zhang*, M Gutmann, A Courville, Z Zhu ICLR 2021 spotlight; arXiv preprint arXiv:2010.10079, 2020 | 64 | 2020 |
| A theory of continuous generative flow networks S Lahlou, T Deleu, P Lemos, D Zhang, A Volokhova, A Hernández-García, ... ICML 2023; arXiv preprint arXiv:2301.12594, 2023 | 63 | 2023 |
| Better training of gflownets with local credit and incomplete trajectories L Pan, N Malkin, D Zhang, Y Bengio ICML 2023; arXiv preprint arXiv:2302.01687, 2023 | 47 | 2023 |
| Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets D Zhang, H Dai, N Malkin, A Courville, Y Bengio, L Pan NeurIPS 2023 spotlight; arXiv preprint arXiv:2305.17010, 2023 | 45* | 2023 |
| Generative Augmented Flow Networks L Pan, D Zhang, A Courville, L Huang, Y Bengio ICLR 2023 spotlight; arXiv preprint arXiv:2210.03308, 2022 | 32 | 2022 |
| Unifying Generative Models with GFlowNets and Beyond D Zhang, RTQ Chen, N Malkin, Y Bengio ICML 2022 Beyond Bayes workshop; arXiv preprint arXiv:2209.02606, 2022 | 29 | 2022 |
| Stochastic Generative Flow Networks L Pan, D Zhang, M Jain, L Huang, Y Bengio UAI 2023 spotlight; arXiv preprint arXiv:2302.09465, 2023 | 23 | 2023 |
| Unifying Likelihood-free Inference with Black-box Optimization and Beyond D Zhang, J Fu, Y Bengio, A Courville ICLR 2022 spotlight; arXiv preprint arXiv:2110.03372, 2021 | 23 | 2021 |
| Predictive Inference with Feature Conformal Prediction J Teng, C Wen, D Zhang, Y Bengio, Y Gao, Y Yuan ICLR 2023; arXiv preprint arXiv:2210.00173, 2022 | 21 | 2022 |
| Local Search GFlowNets M Kim, T Yun, E Bengio, D Zhang, Y Bengio, S Ahn, J Park ICLR 2024 spotlight; arXiv preprint arXiv:2310.02710, 2023 | 20 | 2023 |
| Diffusion generative flow samplers: Improving learning signals through partial trajectory optimization D Zhang, RTQ Chen, CH Liu, A Courville, Y Bengio ICLR 2024; arXiv preprint arXiv:2310.02679, 2023 | 18 | 2023 |
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
