Yuhuai(Tony) Wu
Yuhuai(Tony) Wu
Google / Stanford
Verified email at - Homepage
Cited by
Cited by
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, ...
On the opportunities and risks of foundation models
R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ...
arXiv preprint arXiv:2108.07258, 2021
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
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 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
Sticking the landing: Simple, lower-variance gradient estimators for variational inference
G Roeder, Y Wu, DK Duvenaud
Advances in Neural Information Processing Systems 30, 2017
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 29, 2016
On multiplicative integration with recurrent neural networks
Y Wu, S Zhang, Y Zhang, Y Bengio, RR Salakhutdinov
Advances in neural information processing systems 29, 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
Solving quantitative reasoning problems with language models
A Lewkowycz, A Andreassen, D Dohan, E Dyer, H Michalewski, ...
arXiv preprint arXiv:2206.14858, 2022
Invariant Causal Representation Learning for Out-of-Distribution Generalization
C Lu, Y Wu, JM Hernández-Lobato, B Schölkopf
International Conference on Learning Representations, 2022
STaR: Bootstrapping reasoning with reasoning
E Zelikman, Y Wu, ND Goodman
arXiv preprint arXiv:2203.14465, 2022
IsarStep: a Benchmark for High-level Mathematical Reasoning
W Li, L Yu, Y Wu, LC Paulson
ICLR 2021, 2021
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
OPtions as REsponses: Grounding Behavioural Hierarchies in Multi-Agent Reinforcement Learning
Y Wu, A Vezhnevets, M Eckstein, R Leblond, JZ Leibo
ICML2020, 2020
Memorizing Transformers
Y Wu, MN Rabe, DL Hutchins, C Szegedy
International Conference on Learning Representations 2022, 2022
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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