Yuhuai(Tony) Wu
Cited by
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Grandmaster level in StarCraft II using multi-agent reinforcement learning
O Vinyals, I Babuschkin, WM Czarnecki, M Mathieu, A Dudzik, J Chung, ...
Nature 575 (7782), 350-354, 2019
Openai baselines
P Dhariwal, C Hesse, O Klimov, A Nichol, M Plappert, A Radford, ...
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
Stable baselines
A Hill, A Raffin, M Ernestus, A Gleave, R Traore, P Dhariwal, C Hesse, ...
GitHub repository, 2018
Alphastar: Mastering the real-time strategy game starcraft ii
O Vinyals, I Babuschkin, J Chung, M Mathieu, M Jaderberg, ...
DeepMind blog 2, 2019
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
Backpropagation through the void: Optimizing control variates for black-box gradient estimation
W Grathwohl, D Choi, Y Wu, G Roeder, D Duvenaud
ICLR2018, 2017
On multiplicative integration with recurrent neural networks
Y Wu, S Zhang, Y Zhang, Y Bengio, R Salakhutdinov
arXiv preprint arXiv:1606.06630, 2016
Sticking the landing: Simple, lower-variance gradient estimators for variational inference
G Roeder, Y Wu, D Duvenaud
arXiv preprint arXiv:1703.09194, 2017
Architectural complexity measures of recurrent neural networks
S Zhang, Y Wu, T Che, Z Lin, R Memisevic, R Salakhutdinov, Y Bengio
arXiv preprint arXiv:1602.08210, 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
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
Y Wu, B Neyshabur, 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
ACTRCE: Augmenting Experience via Teacher’s Advice
Y Wu, H Chan, J Kiros, S Fidler, J Ba
OPtions as REsponses: Grounding Behavioural Hierarchies in Multi-Agent Reinforcement Learning
Y Wu, A Vezhnevets, M Eckstein, R Leblond, JZ Leibo
ICML2020, 2020
IsarStep: a Benchmark for High-level Mathematical Reasoning
W Li, L Yu, Y Wu, LC Paulson
International Conference on Learning Representations, 2021
INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving
Y Wu, A Jiang, J Ba, R Grosse
arXiv preprint arXiv:2007.02924, 2020
The scattering compositional learner: Discovering objects, attributes, relationships in analogical reasoning
Y Wu, H Dong, R Grosse, J Ba
arXiv preprint arXiv:2007.04212, 2020
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