Követés
Bo Dai
Bo Dai
Google Brain & Georgia Tech
E-mail megerősítve itt: google.com - Kezdőlap
Cím
Hivatkozott rá
Hivatkozott rá
Év
Discriminative embeddings of latent variable models for structured data
H Dai, B Dai, L Song
International conference on machine learning, 2702-2711, 2016
9042016
Syntax-directed variational autoencoder for structured data
H Dai, Y Tian, B Dai, S Skiena, L Song
arXiv preprint arXiv:1802.08786, 2018
485*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
3932019
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
3422018
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
2832018
AlgaeDICE: Policy gradient from arbitrary experience
O Nachum, B Dai, I Kostrikov, Y Chow, L Li, D Schuurmans
arXiv preprint arXiv:1912.02074, 2019
2722019
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
2612014
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
2302019
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
2212023
GenDICE: Generalized Offline Estimation of Stationary Values
R Zhang, B Dai, L Li, D Schuurmans
International Conference on Learning Representations, 2020
2042020
Learning from Conditional Distributions via Dual Embeddings
B Dai, N He, Y Pan, B Boots, L Song
arXiv preprint arXiv:1607.04579, 2016
1752016
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
1732018
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
1692017
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
1642017
Stochastic generative hashing
B Dai, R Guo, S Kumar, N He, L Song
International Conference on Machine Learning, 913-922, 2017
1412017
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
1402020
Off-Policy Evaluation via the Regularized Lagrangian
M Yang, O Nachum, B Dai, L Li, D Schuurmans
arXiv preprint arXiv:2007.03438, 2020
1282020
Reinforcement learning via fenchel-rockafellar duality
O Nachum, B Dai
arXiv preprint arXiv:2001.01866, 2020
1232020
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
112*2023
Information-theoretic semi-supervised metric learning via entropy regularization
G Niu, B Dai, M Yamada, M Sugiyama
Neural computation 26 (8), 1717-1762, 2014
1092014
A rendszer jelenleg nem tudja elvégezni a műveletet. Próbálkozzon újra később.
Cikkek 1–20