Yuhuai Wu

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 84

Top Topics

Theorem Proving

Reinforcement Learning

Recurrent Neural Networks

Logical Reasoning

Top Venues

Publications

Jin Peng Zhou, Charles Staats, Wenda Li, Christian Szegedy, Kilian Q. Weinberger, Yuhuai Wu
Don't Trust: Verify - Grounding LLM Quantitative Reasoning with Autoformalization. ICLR (2024)
Jin Peng Zhou, Yuhuai Wu, Qiyang Li, Roger B. Grosse
REFACTOR: Learning to Extract Theorems from Proofs. CoRR (2024)
Jin Peng Zhou, Charles Staats, Wenda Li, Christian Szegedy, Kilian Q. Weinberger, Yuhuai Wu
Don't Trust: Verify - Grounding LLM Quantitative Reasoning with Autoformalization. CoRR (2024)
Maciej Mikula, Szymon Tworkowski, Szymon Antoniak, Bartosz Piotrowski, Albert Q. Jiang, Jin Peng Zhou, Christian Szegedy, Lukasz Kucinski, Piotr Milos, Yuhuai Wu
Magnushammer: A Transformer-Based Approach to Premise Selection. ICLR (2024)
Trieu H. Trinh, Yuhuai Wu, Quoc V. Le, He He, Thang Luong
Solving olympiad geometry without human demonstrations. Nat. 625 (7995) (2024)
Sören Arlt, Haonan Duan, Felix Li, Sang Michael Xie, Yuhuai Wu, Mario Krenn
Meta-Designing Quantum Experiments with Language Models. CoRR (2024)
Jin Peng Zhou, Yuhuai Wu, Qiyang Li, Roger Baker Grosse
REFACTOR: Learning to Extract Theorems from Proofs. ICLR (2024)
Qian Huang, Eric Zelikman, Sarah Chen, Yuhuai Wu, Gregory Valiant, Percy Liang
Lexinvariant Language Models. NeurIPS (2023)
Szymon Tworkowski, Konrad Staniszewski, Mikolaj Pacek, Yuhuai Wu, Henryk Michalewski, Piotr Milos
Focused Transformer: Contrastive Training for Context Scaling. CoRR (2023)
Qian Huang, Eric Zelikman, Sarah Li Chen, Yuhuai Wu, Gregory Valiant, Percy Liang
Lexinvariant Language Models. CoRR (2023)
Samy Jelassi, Stéphane d'Ascoli, Carles Domingo-Enrich, Yuhuai Wu, Yuanzhi Li, François Charton
Length Generalization in Arithmetic Transformers. CoRR (2023)
Katherine M. Collins, Albert Q. Jiang, Simon Frieder, Lionel Wong, Miri Zilka, Umang Bhatt, Thomas Lukasiewicz, Yuhuai Wu, Joshua B. Tenenbaum, William Hart, Timothy Gowers, Wenda Li, Adrian Weller, Mateja Jamnik
Evaluating Language Models for Mathematics through Interactions. CoRR (2023)
Szymon Tworkowski, Konrad Staniszewski, Mikolaj Pacek, Yuhuai Wu, Henryk Michalewski, Piotr Milos
Focused Transformer: Contrastive Training for Context Scaling. NeurIPS (2023)
Michal Zawalski, Michal Tyrolski, Konrad Czechowski, Tomasz Odrzygózdz, Damian Stachura, Piotr Piekos, Yuhuai Wu, Lukasz Kucinski, Piotr Milos
Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Search. ICLR (2023)
Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel J. Orr, Lucia Zheng, Mert Yüksekgönül, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri S. Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, Yuta Koreeda
Holistic Evaluation of Language Models. Trans. Mach. Learn. Res. 2023 (2023)
Maciej Mikula, Szymon Antoniak, Szymon Tworkowski, Albert Qiaochu Jiang, Jin Peng Zhou, Christian Szegedy, Lukasz Kucinski, Piotr Milos, Yuhuai Wu
Magnushammer: A Transformer-based Approach to Premise Selection. CoRR (2023)
Albert Qiaochu Jiang, Sean Welleck, Jin Peng Zhou, Timothée Lacroix, Jiacheng Liu, Wenda Li, Mateja Jamnik, Guillaume Lample, Yuhuai Wu
Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs. ICLR (2023)
Yuhuai Wu, Albert Q. Jiang, Wenda Li, Markus N. Rabe, Charles Staats, Mateja Jamnik, Christian Szegedy
Autoformalization with Large Language Models. CoRR (2022)
Eric Zelikman, Yuhuai Wu, Jesse Mu, Noah D. Goodman
STaR: Bootstrapping Reasoning With Reasoning. NeurIPS (2022)
Albert Qiaochu Jiang, Wenda Li, Szymon Tworkowski, Konrad Czechowski, Tomasz Odrzygózdz, Piotr Milos, Yuhuai Wu, Mateja Jamnik
Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers. NeurIPS (2022)
Yuhuai Wu, Felix Li, Percy Liang
Insights into Pre-training via Simpler Synthetic Tasks. CoRR (2022)
Eric Zelikman, Yuhuai Wu, Noah D. Goodman
STaR: Bootstrapping Reasoning With Reasoning. CoRR (2022)
DeLesley Hutchins, Imanol Schlag, Yuhuai Wu, Ethan Dyer, Behnam Neyshabur
Block-Recurrent Transformers. CoRR (2022)
Chaochao Lu, Yuhuai Wu, José Miguel Hernández-Lobato, Bernhard Schölkopf
Invariant Causal Representation Learning for Out-of-Distribution Generalization. ICLR (2022)
Yuhuai Wu, Markus N. Rabe, DeLesley Hutchins, Christian Szegedy
Memorizing Transformers. CoRR (2022)
Cem Anil, Yuhuai Wu, Anders Andreassen, Aitor Lewkowycz, Vedant Misra, Vinay V. Ramasesh, Ambrose Slone, Guy Gur-Ari, Ethan Dyer, Behnam Neyshabur
Exploring Length Generalization in Large Language Models. NeurIPS (2022)
Yuhuai Wu, Albert Qiaochu Jiang, Wenda Li, Markus N. Rabe, Charles Staats, Mateja Jamnik, Christian Szegedy
Autoformalization with Large Language Models. NeurIPS (2022)
Yuhuai Wu, Felix Li, Percy Liang
Insights into Pre-training via Simpler Synthetic Tasks. NeurIPS (2022)
Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay V. Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur, Guy Gur-Ari, Vedant Misra
Solving Quantitative Reasoning Problems with Language Models. NeurIPS (2022)
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
Language Model Cascades. CoRR (2022)
Albert Q. Jiang, Wenda Li, Szymon Tworkowski, Konrad Czechowski, Tomasz Odrzygózdz, Piotr Milos, Yuhuai Wu, Mateja Jamnik
Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers. CoRR (2022)
Michal Zawalski, Michal Tyrolski, Konrad Czechowski, Damian Stachura, Piotr Piekos, Tomasz Odrzygózdz, Yuhuai Wu, Lukasz Kucinski, Piotr Milos
Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Search. CoRR (2022)
DeLesley Hutchins, Imanol Schlag, Yuhuai Wu, Ethan Dyer, Behnam Neyshabur
Block-Recurrent Transformers. NeurIPS (2022)
Jesse Michael Han, Jason Rute, Yuhuai Wu, Edward W. Ayers, Stanislas Polu
Proof Artifact Co-Training for Theorem Proving with Language Models. ICLR (2022)
Piotr Nawrot, Szymon Tworkowski, Michal Tyrolski, Lukasz Kaiser, Yuhuai Wu, Christian Szegedy, Henryk Michalewski
Hierarchical Transformers Are More Efficient Language Models. NAACL-HLT (Findings) (2022)
Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel J. Orr, Lucia Zheng, Mert Yüksekgönül, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri S. Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, Yuta Koreeda
Holistic Evaluation of Language Models. CoRR (2022)
Albert Q. Jiang, Sean Welleck, Jin Peng Zhou, Wenda Li, Jiacheng Liu, Mateja Jamnik, Timothée Lacroix, Yuhuai Wu, Guillaume Lample
Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs. CoRR (2022)
Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay V. Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur, Guy Gur-Ari, Vedant Misra
Solving Quantitative Reasoning Problems with Language Models. CoRR (2022)
Cem Anil, Ashwini Pokle, Kaiqu Liang, Johannes Treutlein, Yuhuai Wu, Shaojie Bai, Zico Kolter, Roger B. Grosse
Path Independent Equilibrium Models Can Better Exploit Test-Time Computation. CoRR (2022)
Cem Anil, Ashwini Pokle, Kaiqu Liang, Johannes Treutlein, Yuhuai Wu, Shaojie Bai, J. Zico Kolter, Roger B. Grosse
Path Independent Equilibrium Models Can Better Exploit Test-Time Computation. NeurIPS (2022)
Yuhuai Wu, Markus Norman Rabe, DeLesley Hutchins, Christian Szegedy
Memorizing Transformers. ICLR (2022)
Cem Anil, Yuhuai Wu, Anders Andreassen, Aitor Lewkowycz, Vedant Misra, Vinay V. Ramasesh, Ambrose Slone, Guy Gur-Ari, Ethan Dyer, Behnam Neyshabur
Exploring Length Generalization in Large Language Models. CoRR (2022)
Konrad Czechowski, Tomasz Odrzygózdz, Marek Zbysinski, Michal Zawalski, Krzysztof Olejnik, Yuhuai Wu, Lukasz Kucinski, Piotr Milos
Subgoal Search For Complex Reasoning Tasks. NeurIPS (2021)
Yuhuai Wu, Markus N. Rabe, Wenda Li, Jimmy Ba, Roger B. Grosse, Christian Szegedy
LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning. CoRR (2021)
Piotr Nawrot, Szymon Tworkowski, Michal Tyrolski, Lukasz Kaiser, Yuhuai Wu, Christian Szegedy, Henryk Michalewski
Hierarchical Transformers Are More Efficient Language Models. CoRR (2021)
Paxton Turner, Yuhuai Wu
Discrete Equidecomposability and Ehrhart Theory of Polygons. Discret. Comput. Geom. 65 (1) (2021)
Jesse Michael Han, Jason Rute, Yuhuai Wu, Edward W. Ayers, Stanislas Polu
Proof Artifact Co-training for Theorem Proving with Language Models. CoRR (2021)
Cem Anil, Guodong Zhang, Yuhuai Wu, Roger B. Grosse
Learning to Give Checkable Answers with Prover-Verifier Games. CoRR (2021)
Konrad Czechowski, Tomasz Odrzygózdz, Marek Zbysinski, Michal Zawalski, Krzysztof Olejnik, Yuhuai Wu, Lukasz Kucinski, Piotr Milos
Subgoal Search For Complex Reasoning Tasks. CoRR (2021)
Chaochao Lu, Yuhuai Wu, José Miguel Hernández-Lobato, Bernhard Schölkopf
Nonlinear Invariant Risk Minimization: A Causal Approach. CoRR (2021)
Sheng Jia, Ehsan Nezhadarya, Yuhuai Wu, Jimmy Ba
Efficient Statistical Tests: A Neural Tangent Kernel Approach. ICML (2021)
Wenda Li, Lei Yu, Yuhuai Wu, Lawrence C. Paulson
IsarStep: a Benchmark for High-level Mathematical Reasoning. ICLR (2021)
Pashootan Vaezipoor, Gil Lederman, Yuhuai Wu, Chris J. Maddison, Roger B. Grosse, Sanjit A. Seshia, Fahiem Bacchus
Learning Branching Heuristics for Propositional Model Counting. AAAI (2021)
Yuhuai Wu, Albert Jiang, Jimmy Ba, Roger Baker Grosse
INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving. ICLR (2021)
Yuhuai Wu, Markus N. Rabe, Wenda Li, Jimmy Ba, Roger B. Grosse, Christian Szegedy
LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning. ICML (2021)
Alexander Vezhnevets, Yuhuai Wu, Maria K. Eckstein, Rémi Leblond, Joel Z. Leibo
OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning. ICML (2020)
Yuhuai Wu, Honghua Dong, Roger B. Grosse, Jimmy Ba
The Scattering Compositional Learner: Discovering Objects, Attributes, Relationships in Analogical Reasoning. CoRR (2020)
Pashootan Vaezipoor, Gil Lederman, Yuhuai Wu, Chris J. Maddison, Roger B. Grosse, Edward A. Lee, Sanjit A. Seshia, Fahiem Bacchus
Learning Branching Heuristics for Propositional Model Counting. CoRR (2020)
Yuhuai Wu, Albert Jiang, Jimmy Ba, Roger B. Grosse
INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving. CoRR (2020)
Wenda Li, Lei Yu, Yuhuai Wu, Lawrence C. Paulson
Modelling High-Level Mathematical Reasoning in Mechanised Declarative Proofs. CoRR (2020)
Harris Chan, Yuhuai Wu, Jamie Kiros, Sanja Fidler, Jimmy Ba
ACTRCE: Augmenting Experience via Teacher's Advice For Multi-Goal Reinforcement Learning. CoRR (2019)
Emilio Parisotto, Soham Ghosh, Sai Bhargav Yalamanchi, Varsha Chinnaobireddy, Yuhuai Wu, Ruslan Salakhutdinov
Concurrent Meta Reinforcement Learning. CoRR (2019)
Alexander Sasha Vezhnevets, Yuhuai Wu, Rémi Leblond, Joel Z. Leibo
Options as responses: Grounding behavioural hierarchies in multi-agent RL. CoRR (2019)
Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H. Choi, Richard Powell, Timo Ewalds, Petko Georgiev, Junhyuk Oh, Dan Horgan, Manuel Kroiss, Ivo Danihelka, Aja Huang, Laurent Sifre, Trevor Cai, John P. Agapiou, Max Jaderberg, Alexander Sasha Vezhnevets, Rémi Leblond, Tobias Pohlen, Valentin Dalibard, David Budden, Yury Sulsky, James Molloy, Tom Le Paine, Çaglar Gülçehre, Ziyu Wang, Tobias Pfaff, Yuhuai Wu, Roman Ring, Dani Yogatama, Dario Wünsch, Katrina McKinney, Oliver Smith, Tom Schaul, Timothy P. Lillicrap, Koray Kavukcuoglu, Demis Hassabis, Chris Apps, David Silver
Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nat. 575 (7782) (2019)
Yuhuai Wu, Mengye Ren, Renjie Liao, Roger B. Grosse
Understanding Short-Horizon Bias in Stochastic Meta-Optimization. ICLR (Poster) (2018)
Bradly C. Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever
Some Considerations on Learning to Explore via Meta-Reinforcement Learning. CoRR (2018)
Yuhuai Wu, Mengye Ren, Renjie Liao, Roger B. Grosse
Understanding Short-Horizon Bias in Stochastic Meta-Optimization. CoRR (2018)
Bradly C. Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever
The Importance of Sampling inMeta-Reinforcement Learning. NeurIPS (2018)
Jiaming Song, Yuhuai Wu
An Empirical Analysis of Proximal Policy Optimization with Kronecker-factored Natural Gradients. CoRR (2018)
Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation. ICLR (Poster) (2018)
Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, Roger B. Grosse
On the Quantitative Analysis of Decoder-Based Generative Models. ICLR (Poster) (2017)
Yoshua Bengio, Thomas Mesnard, Asja Fischer, Saizheng Zhang, Yuhuai Wu
STDP-Compatible Approximation of Backpropagation in an Energy-Based Model. Neural Comput. 29 (3) (2017)
Geoffrey Roeder, Yuhuai Wu, David Duvenaud
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference. NIPS (2017)
Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation. CoRR (2017)
Yuhuai Wu, Elman Mansimov, Roger B. Grosse, Shun Liao, Jimmy Ba
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation NIPS (2017)
Geoffrey Roeder, Yuhuai Wu, David Duvenaud
Sticking the Landing: An Asymptotically Zero-Variance Gradient Estimator for Variational Inference. CoRR (2017)
Yuhuai Wu, Elman Mansimov, Shun Liao, Roger B. Grosse, Jimmy Ba
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation. CoRR (2017)
Behnam Neyshabur, Yuhuai Wu, Ruslan Salakhutdinov, Nathan Srebro
Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations. CoRR (2016)
Saizheng Zhang, Yuhuai Wu, Tong Che, Zhouhan Lin, Roland Memisevic, Ruslan Salakhutdinov, Yoshua Bengio
Architectural Complexity Measures of Recurrent Neural Networks. NIPS (2016)
Yuhuai Wu, Saizheng Zhang, Ying Zhang, Yoshua Bengio, Ruslan Salakhutdinov
On Multiplicative Integration with Recurrent Neural Networks. NIPS (2016)
Behnam Neyshabur, Yuhuai Wu, Ruslan Salakhutdinov, Nati Srebro
Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations. NIPS (2016)
Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, Roger B. Grosse
On the Quantitative Analysis of Decoder-Based Generative Models. CoRR (2016)
Yuhuai Wu, Saizheng Zhang, Ying Zhang, Yoshua Bengio, Ruslan Salakhutdinov
On Multiplicative Integration with Recurrent Neural Networks. CoRR (2016)
Saizheng Zhang, Yuhuai Wu, Tong Che, Zhouhan Lin, Roland Memisevic, Ruslan Salakhutdinov, Yoshua Bengio
Architectural Complexity Measures of Recurrent Neural Networks. CoRR (2016)