Machine reasoning, theorem proving, modularity, language, neuro-symbolic integration.
I would like to make machine do reasoning.
Releasing LIME: Designing inductive biases as a form of datasets for mathematical reasoniong! [arxiv]
Neuro# accepted to AAAI2021: A neural network #SAT solver that generalizes to problems of much larger sizes, achiving improvements over SOTA by orders of magnitude. [arxiv]
Releasing SCL: a neural architecture that discovers compositional structures in analogical reasoning, generalizing to novel analogies. [arxiv]
One poster in ICML 2020.
I'm organizing a seminar on Machine Reasoning, including reasoning in theorem proving, natural language understanding, program synthesis:
The meeting time is every Wednesday 3-4pm EST, Vector Institute.
Welcome to attend if you're around Toronto.
Releasing OPRE: a hierarchical agent that generalizes to novel opponent strategy! [arxiv]
I recently finished an internship at Deepmind from June 2018 - April 2019, working on hierarchical reinforcement learning and StarCraft 2.
One poster in NeurIPS 2018.
Two posters in ICLR 2018.
Two posters and two workshop in NIPS 2017.
I'm very honoured to receive the Google PhD fellowship in machine learning!
Our submission to ICLR: On the Quantitative Analysis of Decoder-Based Generative Models [arxiv] is accepted as a poster presentation. Now we are able to quantitatively measure performances of GANs!
One journal paper accepted to appear in Neural Computation!
3 (co)first-authored papers accepted to appear at NIPS 2016!
Proof Artifact Co-training for Theorem Proving with Language Model. Jesse Michael Han, Jason Rute, Yuhuai Wu, Edward W. Ayers, Stanislas Polu. 2021. [arxiv].
Nonlinear Invariant Risk Minimization: A Causal Approach. Chaochao Lu, Yuhuai Wu, Jose Miguel Hernandez-Lobato, Bernhard Scholkopf. 2021. [arxiv].
The Scattering Compositional Learner: Discovering Objects, Attributes, Relationships in Analogical Reasoning. Yuhuai Wu*, Honghua Dong*, Roger Grosse, Jimmy Ba. 2020. [arxiv].
Grandmaster level in StarCraft II using multi-agent reinforcement learning. Vinyals, O., Babuschkin, I., Czarnecki, W.M. et al. Nature. 2019. [journal]
LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning. Yuhuai Wu, Markus Rabe, Wenda Li, Jimmy Ba, Roger Grosse, Christian Szegedy. ICML 2021. [arxiv].
Efficient Statistical Tests: A Neural Tangent Kernel Approach. Sheng Jia, Ehsan Nezhadarya, Yuhuai Wu, Jimmy Ba. ICML 2021.
INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving.
Yuhuai Wu*, Albert Q. Jiang*, Jimmy Ba, Roger Grosse. ICLR 2021. [arxiv].
Modelling High-Level Mathematical Reasoning in Mechanised Declarative Proofs. Wenda Li, Lei Yu, Yuhuai Wu, Lawrence C. Paulson, ICLR 2021. [arxiv].
Learning Branching Heuristics for Propositional Model Counting. Pashootan Vaezipoor*, Gil Lederman*, Yuhuai Wu, Chris J. Maddison, Roger Grosse, Edward Lee, Sanjit A. Seshia, Fahiem Bacchus. AAAI 2021. [arxiv].
Neural Theorem Proving on Inequality Problems.
Yuhuai Wu*, Albert Q. Jiang*, Roger Grosse, Jimmy Ba. AITP 2020. [paper].
Learning Clause Deletion Heuristics with Reinforcement Learning. Pashootan Vaezipoor, Gil Lederman, Yuhuai Wu, Roger Grosse, Fahiem Bacchus. AITP 2020. [paper].
The Importance of Sampling in Meta-Reinforcement Learning. Bradly Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever. NeurIPS 2018. [paper].
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation. Will Grathwohl, Dami Choi, Yuhuai Wu, Geoff Roeder, David Duvenaud. ICLR 2018. [arxiv].
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
Yuhuai Wu*, Elman Mansimov*, Shun Liao, Roger Grosse, Jimmy Ba. NIPS, 2017. Spotlight.
Sticking the Landing: An Asymptotically Zero-Variance Gradient Estimator for Variational Inference. Geoffrey Roeder, Yuhuai Wu, David Duvenaud. NIPS, 2017. [arxiv]
On Multiplicative Integration with Recurrent Neural Networks. Yuhuai Wu*, Saizheng Zhang*, Ying Zhang, Yoshua Bengio, Ruslan Salakhutdinov. NIPS, 2016. [arxiv]
Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations. Behnam Neyshabur*, Yuhuai Wu*, Ruslan Salakhutdinov, Nathan Srebro. NIPS, 2016. [arxiv]
Architectural Complexity Measures of Recurrent Neural Networks. Saizheng Zhang*, Yuhuai Wu*, Tong Che, Zhouhan Lin, Roland Memisevic, Ruslan Salakhutdinov, Yoshua Bengio. NIPS, 2016. [arxiv]
Understanding Short-Horizon Bias in Stochastic Meta-Optimization. Yuhuai Wu*, Mengye Ren*, Renjie Liao, Roger B. Grosse. NIPS 2017 workshop in meta-learning. [workshop]
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation. Will Grathwohl, Dami Choi, Yuhuai Wu, Geoff Roeder, David Duvenaud. NIPS 2017 Deep RL symposium. Oral. [workshop]
Sticking the Landing: A Simple Reduced-Variance Gradient for ADVI. Geoffrey Roeder, Yuhuai Wu, David Duvenaud. NIPS 2016 workshop in Advances in Approximate Bayesian Inference. [workshop]
ACTRCE: Augmenting Experience via Teacher's Advice For Multi-Goal Reinforcement Learning. Yuhuai Wu*, Harris Chan*, Jamie Kiros, Sanja Fidler, Jimmy Ba. 2019. [arxiv].
Concurrent Meta Reinforcement Learning. Emilio Parisotto, Soham Ghosh, Sai Bhargav Yalamanchi, Varsha Chinnaobireddy, Yuhuai Wu, Ruslan Salakhutdinov. 2019. [arxiv].
An Empirical Analysis of Proximal Policy Optimization with Kronecker-factored Natural Gradients. Jiaming Song, Yuhuai Wu. 2018. [arxiv].
I am/was a reviewer for
NIPS2016, ICML2017, NIPS2017, AAAI2018, ICLR2018, 2018NIPS, 2019ICML, 2019NIPS, 2020ICLR, 2020NIPS.
I am/was a TA for
CSC 321 : Introduction to Neural Networks (2017 spring)
ECE 521 : Inference Algorithms and Machine Learning (2017 spring)
CSC 236: Introduction to the Theory of Computation (2016 summer)
CSC 148: Introduction to Computer Science (2016 spring)
CSC 165: Mathematical Expression and Reasoning for Computer Science (2015 fall)
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation. Vector Institute Endless Summer School. 2017/11.
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation. Microsoft Research Redmond. 2017/09.
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation. Apple. 2017/09.
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation. Google Brain. 2017/09.
On the Quantitative Analysis of Decoder-Based Generative Models. NIPS workshop in Adversarial training. 2016/12.
On the Quantitative Analysis of Decoder-Based Generative Models. OpenAI. 2016/11.
Architectural Complexity Measures & Multiplicative Integration of RNNs. U of Toronto. 2016/10.
Intro to Differential Geometry. U of Toronto. 2016/07.
Architectural Complexity Measures of Recurrent Neural Networks. Toyota Technological Institute at Chicago. 2016/04.