| Discriminative embeddings of latent variable models for structured data H Dai, B Dai, L Song International conference on machine learning, 2702-2711, 2016 | 533 | 2016 |
| Syntax-directed variational autoencoder for structured data H Dai, Y Tian, B Dai, S Skiena, L Song arXiv preprint arXiv:1802.08786, 2018 | 270* | 2018 |
| Scalable kernel methods via doubly stochastic gradients B Dai, B Xie, N He, Y Liang, A Raj, MFF Balcan, L Song Advances in neural information processing systems 27, 2014 | 214 | 2014 |
| 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 | 196 | 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 | 147 | 2019 |
| 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 | 118 | 2018 |
| Learning from Conditional Distributions via Dual Embeddings B Dai, N He, Y Pan, B Boots, L Song arXiv preprint arXiv:1607.04579, 2016 | 111 | 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 | 108 | 2017 |
| 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 | 97 | 2017 |
| Information-theoretic semi-supervised metric learning via entropy regularization G Niu, B Dai, M Yamada, M Sugiyama Neural computation 26 (8), 1717-1762, 2014 | 96 | 2014 |
| 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 | 90 | 2018 |
| GenDICE: Generalized Offline Estimation of Stationary Values R Zhang, B Dai, L Li, D Schuurmans International Conference on Learning Representations, 2020 | 88 | 2020 |
| AlgaeDICE: Policy gradient from arbitrary experience O Nachum, B Dai, I Kostrikov, Y Chow, L Li, D Schuurmans arXiv preprint arXiv:1912.02074, 2019 | 86 | 2019 |
| Stochastic generative hashing B Dai, R Guo, S Kumar, N He, L Song International Conference on Machine Learning, 913-922, 2017 | 82 | 2017 |
| 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 | 64 | 2019 |
| Squared-loss mutual information regularization: A novel information-theoretic approach to semi-supervised learning G Niu, W Jitkrittum, B Dai, H Hachiya, M Sugiyama International Conference on Machine Learning, 10-18, 2013 | 60 | 2013 |
| Provable Bayesian inference via particle mirror descent B Dai, N He, H Dai, L Song Artificial Intelligence and Statistics, 985-994, 2016 | 57* | 2016 |
| Nonparametric estimation of multi-view latent variable models L Song, A Anandkumar, B Dai, B Xie International Conference on Machine Learning, 640-648, 2014 | 47 | 2014 |
| Off-Policy Evaluation via the Regularized Lagrangian M Yang, O Nachum, B Dai, L Li, D Schuurmans arXiv preprint arXiv:2007.03438, 2020 | 42 | 2020 |
| Towards black-box iterative machine teaching W Liu, B Dai, X Li, Z Liu, J Rehg, L Song International Conference on Machine Learning, 3141-3149, 2018 | 41 | 2018 |
Le SongGeorgia Institute of TechnologyVerified email at cc.gatech.edu
