| Discriminative embeddings of latent variable models for structured data H Dai, B Dai, L Song International conference on machine learning, 2702-2711, 2016 | 979 | 2016 |
| Syntax-directed variational autoencoder for structured data H Dai, Y Tian, B Dai, S Skiena, L Song arXiv preprint arXiv:1802.08786, 2018 | 531* | 2018 |
| DualDICE: Behavior-agnostic estimation of discounted stationary distribution corrections O Nachum, Y Chow, B Dai, L Li Advances in Neural Information Processing Systems, 2315-2325, 2019 | 440 | 2019 |
| Learning universal policies via text-guided video generation Y Du, S Yang, B Dai, H Dai, O Nachum, J Tenenbaum, D Schuurmans, ... Advances in Neural Information Processing Systems 36, 2023 | 391 | 2023 |
| SBEED: Convergent reinforcement learning with nonlinear function approximation B Dai, A Shaw, L Li, L Xiao, N He, Z Liu, J Chen, L Song International conference on machine learning, 1125-1134, 2018 | 373 | 2018 |
| Learning steady-states of iterative algorithms over graphs H Dai, Z Kozareva, B Dai, A Smola, L Song International conference on machine learning, 1106-1114, 2018 | 328 | 2018 |
| Retrosynthesis prediction with conditional graph logic network H Dai, C Li, C Coley, B Dai, L Song Advances in Neural Information Processing Systems 32, 2019 | 309 | 2019 |
| AlgaeDICE: Policy gradient from arbitrary experience O Nachum, B Dai, I Kostrikov, Y Chow, L Li, D Schuurmans arXiv preprint arXiv:1912.02074, 2019 | 299 | 2019 |
| Scalable kernel methods via doubly stochastic gradients B Dai, B Xie, N He, Y Liang, A Raj, MF Balcan, L Song Advances in neural information processing systems 27, 2014 | 274 | 2014 |
| GenDICE: Generalized Offline Estimation of Stationary Values R Zhang, B Dai, L Li, D Schuurmans International Conference on Learning Representations, 2020 | 229 | 2020 |
| Adaplanner: Adaptive planning from feedback with language models H Sun, Y Zhuang, L Kong, B Dai, C Zhang Advances in Neural Information Processing Systems 36, 2023 | 203* | 2023 |
| Learning towards minimum hyperspherical energy W Liu, R Lin, Z Liu, L Liu, Z Yu, B Dai, L Song Advances in neural information processing systems 31, 2018 | 194 | 2018 |
| Deep hyperspherical learning W Liu, YM Zhang, X Li, Z Yu, B Dai, T Zhao, L Song Advances in neural information processing systems 30, 2017 | 184 | 2017 |
| Learning from Conditional Distributions via Dual Embeddings B Dai, N He, Y Pan, B Boots, L Song arXiv preprint arXiv:1607.04579, 2016 | 184 | 2016 |
| Iterative machine teaching W Liu, B Dai, A Humayun, C Tay, C Yu, LB Smith, JM Rehg, L Song International Conference on Machine Learning, 2149-2158, 2017 | 182 | 2017 |
| Differentiable top-k with optimal transport Y Xie, H Dai, M Chen, B Dai, T Zhao, H Zha, W Wei, T Pfister Advances in neural information processing systems 33, 20520-20531, 2020 | 162 | 2020 |
| Stochastic generative hashing B Dai, R Guo, S Kumar, N He, L Song International Conference on Machine Learning, 913-922, 2017 | 147 | 2017 |
| Score-based continuous-time discrete diffusion models H Sun, L Yu, B Dai, D Schuurmans, H Dai arXiv preprint arXiv:2211.16750, 2022 | 145 | 2022 |
| Off-Policy Evaluation via the Regularized Lagrangian M Yang, O Nachum, B Dai, L Li, D Schuurmans arXiv preprint arXiv:2007.03438, 2020 | 145 | 2020 |
| Reinforcement learning via fenchel-rockafellar duality O Nachum, B Dai arXiv preprint arXiv:2001.01866, 2020 | 143 | 2020 |
Dale SchuurmansGoogle DeepMind & University of AlbertaVerified email at ualberta.ca
