Yuhuai(Tony) Wu
Yuhuai(Tony) Wu
Verified email at cs.toronto.edu - Homepage
TitleCited byYear
Openai baselines
P Dhariwal, C Hesse, M Plappert, A Radford, J Schulman, S Sidor, Y Wu
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
Y Wu, E Mansimov, RB Grosse, S Liao, J Ba
Advances in Neural Information Processing Systems, 5283-5292, 2017
On the quantitative analysis of decoder-based generative models
Y Wu, Y Burda, R Salakhutdinov, R Grosse
5th International Conference on Learning Representations (ICLR 2017), 2016
On multiplicative integration with recurrent neural networks
Y Wu, S Zhang, Y Zhang, Y Bengio, RR Salakhutdinov
Advances in neural information processing systems, 2856-2864, 2016
Backpropagation through the void: Optimizing control variates for black-box gradient estimation
W Grathwohl, D Choi, Y Wu, G Roeder, D Duvenaud
arXiv preprint arXiv:1711.00123, 2017
AlphaStar: Mastering the real-time strategy game StarCraft II
O Vinyals, I Babuschkin, J Chung, M Mathieu, M Jaderberg, ...
DeepMind Blog, 2019
Architectural complexity measures of recurrent neural networks
S Zhang, Y Wu, T Che, Z Lin, R Memisevic, RR Salakhutdinov, Y Bengio
Advances in neural information processing systems, 1822-1830, 2016
STDP-compatible approximation of backpropagation in an energy-based model
Y Bengio, T Mesnard, A Fischer, S Zhang, Y Wu
Neural computation 29 (3), 555-577, 2017
Sticking the landing: Simple, lower-variance gradient estimators for variational inference
G Roeder, Y Wu, DK Duvenaud
Advances in Neural Information Processing Systems, 6925-6934, 2017
Stable baselines
A Hill, A Raffin, M Ernestus, A Gleave, R Traore, P Dhariwal, C Hesse, ...
GitHub repository, 2018
The Importance of Sampling in Meta-Reinforcement Learning
B Stadie, G Yang, R Houthooft, P Chen, Y Duan, Y Wu, P Abbeel, ...
Advances in Neural Information Processing Systems, 9299-9309, 2018
Understanding Short-Horizon Bias in Stochastic Meta-Optimization
Y Wu, M Ren, R Liao, RB Grosse
Sixth International Conference on Learning Representations (ICLR 2018), 2018
Path-normalized optimization of recurrent neural networks with relu activations
B Neyshabur, Y Wu, RR Salakhutdinov, N Srebro
Advances in Neural Information Processing Systems, 3477-3485, 2016
Concurrent Meta Reinforcement Learning
E Parisotto, S Ghosh, SB Yalamanchi, V Chinnaobireddy, Y Wu, ...
arXiv preprint arXiv:1903.02710, 2019
Discrete Equidecomposability and Ehrhart Theory of Polygons
P Turner, Y Wu
arXiv preprint arXiv:1412.0196, 2014
Grandmaster level in StarCraft II using multi-agent reinforcement learning
O Vinyals, I Babuschkin, WM Czarnecki, M Mathieu, A Dudzik, J Chung, ...
Nature, 1-5, 2019
Conditions for Discrete Equidecomposability of Polygons
P Turner, Y Wu
arXiv preprint arXiv:1412.0191, 2014
Options as responses: Grounding behavioural hierarchies in multi-agent RL
Y Wu, AS Vezhnevets, R Leblond, J Leibo
arXiv preprint arXiv:1906.01470, 2019
ACTRCE: Augmenting Experience via Teacher’s Advice
Y Wu, H Chan, J Kiros, S Fidler, J Ba
An Empirical Analysis of Proximal Policy Optimization with Kronecker-factored Natural Gradients
J Song, Y Wu
arXiv preprint arXiv:1801.05566, 2018
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