Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs
Albert Qiaochu Jiang*, Sean Welleck*, Jin Peng Zhou*, Timothee Lacroix, Jiacheng Liu, Wenda Li,
Mateja Jamnik, Guillaume Lample, Yuhuai Wu
The 10th International Conference on Learning Representations, 2023.
PDF
Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Search.
Michał Zawalski, Michał Tyrolski, Konrad Czechowski, Damian Stachura, Piotr Piękos,
Tomasz Odrzygóźdź, Yuhuai Wu, Łukasz Kuciński,
Piotr Miłoś
The 10th International Conference on Learning Representations, 2023.
PDF
Minerva: Solving Quantitative Reasoning Problems with Language Models
Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski,
Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo,
Yuhuai Wu, Behnam Neyshabur, Guy Gur-Ari, Vedant Misra
The 36th Conference on Neural Information Processing Systems, 2022.
PDF Google AI Blog
Insights into Pre-training via Simpler Synthetic Tasks
Yuhuai Wu*,
Felix Li*,
Percy Liang
The 36th Conference on Neural Information Processing Systems, 2022.
PDF
Autoformalization with Large Language Models.
Yuhuai Wu,
Albert Q. Jiang,
Wenda Li,
Markus Rabe,
Charles Staats,
Mateja Jamnik,
Christian Szegedy
The 36th Conference on Neural Information Processing Systems, 2022.
PDF Interview with NewScientist
Exploring Length Generalization in Large Language Models
Cem Anil, Yuhuai Wu, Anders Andreassen, Aitor Lewkowycz, Vedant Misra,
Vinay Ramasesh, Ambrose Slone, Guy Gur-Ari, Ethan Dyer, Behnam Neyshabur
The 36th Conference on Neural Information Processing Systems, 2022.
PDF
Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers.
Albert Q. Jiang,
Wenda Li,
Szymon Tworkowski, Konrad Czechowski, Tomasz Odrzygóźdź,
Piotr Miłoś,
Yuhuai Wu,
Mateja Jamnik
The 36th Conference on Neural Information Processing Systems, 2022.
PDF
STaR: Bootstrapping Reasoning With Reasoning.
Eric Zelikman*, Yuhuai Wu*, Noah D. Goodman
The 36th Conference on Neural Information Processing Systems, 2022.
PDF
Block-Recurrent Transformers.
DeLesley Hutchins*, Imanol Schlag*, Yuhuai Wu, Ethan Dyer,
Behnam Neyshabur
The 36th Conference on Neural Information Processing Systems, 2022.
PDF
Language Model Cascades.
David Dohan, Winnie Xu, Aitor Lewkowycz, Jacob Austin, David Bieber,
Raphael Gontijo Lopes, Yuhuai Wu, Henryk Michalewski, Rif A. Saurous,
Jascha Sohl-dickstein, Kevin Murphy, Charles Sutton
Beyond Bayes: Paths Towards Universal Reasoning Systems, ICML, 2022.
PDF
Hierarchical Transformers Are More Efficient Language Models.
Piotr Nawrot, Szymon Tworkowski, Michał Tyrolski, Łukasz Kaiser,
Yuhuai Wu,
Christian Szegedy,
Henryk Michalewski
Memorizing Transformers.
Yuhuai Wu,
Markus Rabe,
DeLesley Hutchins,
Christian Szegedy
The 10th International Conference on Learning Representations, 2022.
PDF #4 on HackerNews
Proof Artifact Co-training for Theorem Proving with Language Model.
Jesse Michael Han,
Jason Rute, Yuhuai Wu, Edward W. Ayers, Stanislas Polu
The 10th International Conference on Learning Representations, 2022.
PDF
Nonlinear Invariant Risk Minimization: A Causal Approach.
Chaochao Lu,
Yuhuai Wu, Jose Miguel Hernandez-Lobato, Bernhard Scholkopf
The 10th International Conference on Learning Representations, 2022.
PDF
On the Opportunities and Risks of Foundation Models.
Rishi Bommasani, Drew A. Hudson,
Percy Liang et. al.
Subgoal Search For Complex Reasoning Tasks.
Konrad Czechowski, Tomasz Odrzygozdz, Marek Zbysinski, Michal Zawalski,
Krzysztof Olejnik, Yuhuai Wu, Lukasz Kucinski,
Piotr Miłoś
The 35th Conference on Neural Information Processing Systems, 2021.
PDF
Learning to Give Checkable Answers with Prover-Verifier Games.
Cem Anil,
Guodong Zhang,
Yuhuai Wu,
Roger Grosse
2021
PDF
Learning Neural Discrete Reaction Classes to Improve Retrosynthesis.
Théophile Gaudin, Yuhuai Wu, Robert Pollice, Animesh Garg, Alán Aspuru-Guzik
Machine Learning and the Physical Sciences, NeurIPS 2021
PDF
LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning.
Yuhuai Wu,
Markus Rabe,
Wenda Li,
Jimmy Ba,
Roger Grosse,
Christian Szegedy
The 38th International Conference on Machine Learning, 2021.
PDF
Out-of-Distribution Generalization with Deep Equilibrium Models.
Cem Anil, Kaiqu Liang,
Yuhuai Wu, Roger Grosse
Uncertainty and Robustness in Deep Learning, ICML 2021.
PDF
INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving.
Yuhuai Wu*,
Albert Q. Jiang*,
Jimmy Ba,
Roger Grosse
The 9th International Conference on Learning Representations, 2021.
PDF
REFACTOR: Learning to Extract Theorems from Proofs.
Jin Peng Zhou, Yuhuai Wu, Colin Li, Roger Grosse
The Role of Mathematical Reasoning in General Artificial Intelligence, ICLR 2021.
PDF
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.
The 35th AAAI Conference on Artificial Intelligence, 2021.
PDF
LISA: Language models of ISAbelle Proofs.
Albert Q. Jiang,
Wenda Li,
Jesse Michael Han, Yuhuai Wu
The 6th Conference on Artificial Intelligence and Theorem Proving, 2021.
PDF
Latent Action Space for Efficient Planning in Theorem Proving.
Minchao Wu*, Yuhuai Wu*
The 6th Conference on Artificial Intelligence and Theorem Proving, 2021.
PDF
The Scattering Compositional Learner: Discovering Objects, Attributes, Relationships in Analogical Reasoning.
Yuhuai Wu*, Honghua Dong*,
Roger Grosse,
Jimmy Ba
Options as REsponses: Grounding Behavioural Hierarchies in Multi-agent Reinforcement Learning.
Yuhuai Wu*, Alexander Sasha Vezhnevets*, Maria Eckstein, Remi Leblond, Joel Z. Leibo.
The 37th International Conference on Machine Learning, 2020.
PDF
Neural Theorem Proving on Inequality Problems.
Yuhuai Wu*, Albert Q. Jiang*,
Roger Grosse,
Jimmy Ba
The 5th Conference on Artificial Intelligence and Theorem Proving, 2020.
PDF
Learning Clause Deletion Heuristics with Reinforcement Learning.
Pashootan Vaezipoor, Gil Lederman,
Yuhuai Wu, Albert Q. Jiang,
Roger Grosse, Fahiem Bacchus
The 5th Conference on Artificial Intelligence and Theorem Proving, 2020.
PDF
Discrete Equidecomposability and Ehrhart Theory of Polygons.
Paxton Turner, Yuhuai Wu
Discrete & Computational Geometry, 2020.
PDF
Grandmaster Level in StarCraft II using Multi-gent Reinforcement Learning.
Vinyals, O., Babuschkin, I., Czarnecki, W.M. et al.
Nature, 2019.
PDF
Concurrent Meta Reinforcement Learning.
Emilio Parisotto, Soham Ghosh, Sai Bhargav Yalamanchi, Varsha Chinnaobireddy,Yuhuai Wu*,
Ruslan Salakhutdinov
An Empirical Analysis of Proximal Policy Optimization with Kronecker-factored Natural Gradients.
Jiaming Song,
Yuhuai Wu
The Importance of Sampling in Meta-Reinforcement Learning.
Bradly Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu,
Pieter Abbeel,
Ilya Sutskever
The 32nd Conference on Neural Information Processing Systems, 2018.
PDF
Understanding Short-Horizon Bias in Stochastic Meta-Optimization.
Yuhuai Wu*,
Mengye Ren*,
Renjie Liao,
Roger Grosse
The 6th International Conference on Learning Representations, 2018.
PDF
Backpropagation through the Void: Optimizing Control Variates for Black-Box Gradient Estimation.
Will Grathwohl,
Dami Choi,
Yuhuai Wu,
Geoffrey Roeder
David Duvenaud
The 6th International Conference on Learning Representations, 2018.
PDF
ACTRCE: Augmenting Experience via Teacher's Advice For Multi-Goal Reinforcement Learning.
Yuhuai Wu*,
Harris Chan*, Jamie Kiros, Sanja Fidler,
Jimmy Ba
Goal Specifications for Reinforcement Learning, ICML 2018.
PDF
Scalable Trust-Region Method for Deep Reinforcement Learning using Kronecker-Factored Approximation.
Yuhuai Wu*, Elman Mansimov*, Shun Liao,
Roger Grosse,
Jimmy Ba
The 31st Annual Conference on Neural Information Processing Systems, 2017
PDF
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference.
Geoffrey Roeder, Yuhuai Wu,
David Duvenaud
The 31st Annual Conference on Neural Information Processing Systems, 2017
PDF
On the Quantitative Analysis of Decoder-Based Generative Models
Yuhuai Wu,
Yuri Burda,
Ruslan Salakhutdinov,
Roger Grosse
The 5th International Conference on Learning Representations, 2017
PDF
STDP Based Approximation of Back-Propagation in an Energy Based Model
Yoshua Bengio,
Thomas Mesnard, Asja Fischer, Saizheng Zhang, Yuhuai Wu
Neural computation, 2017
PDF
On Multiplicative Integration with Recurrent Neural Networks.
Yuhuai Wu*, Saizheng Zhang*, Ying Zhang,
Yoshua Bengio,
Ruslan Salakhutdinov
The 30th Annual Conference on Neural Information Processing Systems, 2016
PDF
Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations.
Yuhuai Wu*,
Behnam Neyshabur*,
Ruslan Salakhutdinov,
Nathan Srebro
The 30th Annual Conference on Neural Information Processing Systems, 2016
PDF
Architectural Complexity Measures of Recurrent Neural Networks.
Saizheng Zhang*, Yuhuai Wu*, Tong Che, Zhouhan Lin, Roland Memisevic,
Ruslan Salakhutdinov,
Yoshua Bengio
The 30th Annual Conference on Neural Information Processing Systems, 2016
PDF
Conditions for Discrete Equidecomposability of Polygons.
Paxton Turner, Yuhuai Wu
PDF