Wu, Yuhuai

39 publications

ICLR 2024 Don't Trust: Verify -- Grounding LLM Quantitative Reasoning with Autoformalization Jin Peng Zhou, Charles E Staats, Wenda Li, Christian Szegedy, Kilian Q Weinberger, Yuhuai Wu

ICLR 2024 Magnushammer: A Transformer-Based Approach to Premise Selection Maciej Mikuła, Szymon Tworkowski, Szymon Antoniak, Bartosz Piotrowski, Albert Q. Jiang, Jin Peng Zhou, Christian Szegedy, Łukasz Kuciński, Piotr Miłoś, Yuhuai Wu

ICMLW 2024 Meta-Designing Quantum Experiments with Language Models Sören Arlt, Haonan Duan, Felix Li, Sang Michael Xie, Yuhuai Wu, Mario Krenn

ICLR 2024 REFACTOR: Learning to Extract Theorems from Proofs Jin Peng Zhou, Yuhuai Wu, Qiyang Li, Roger Baker Grosse

ICLR 2023 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

ICLR 2023 Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Search Michał Zawalski, Michał Tyrolski, Konrad Czechowski, Tomasz Odrzygóźdź, Damian Stachura, Piotr Piękos, Yuhuai Wu, Łukasz Kuciński, Piotr Miłoś

NeurIPS 2023 Focused Transformer: Contrastive Training for Context Scaling Szymon Tworkowski, Konrad Staniszewski, Mikołaj Pacek, Yuhuai Wu, Henryk Michalewski, Piotr Miłoś

TMLR 2023 Holistic Evaluation of Language Models 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 Re, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel Orr, Lucia Zheng, Mert Yuksekgonul, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri S. Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Andrew 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

NeurIPS 2023 Lexinvariant Language Models Qian Huang, Eric Zelikman, Sarah Chen, Yuhuai Wu, Gregory Valiant, Percy Liang

ICMLW 2023 Lexinvariant Language Models Qian Huang, Eric Zelikman, Sarah Li Chen, Yuhuai Wu, Gregory Valiant, Percy Liang

NeurIPSW 2023 Magnushammer: A Transformer-Based Approach to Premise Selection Maciej Mikuła, Szymon Antoniak, Szymon Tworkowski, Bartosz Piotrowski, Albert Jiang, Jin Peng Zhou, Christian Szegedy, Łukasz Kuciński, Piotr Miłoś, Yuhuai Wu

NeurIPS 2022 Autoformalization with Large Language Models Yuhuai Wu, Albert Qiaochu Jiang, Wenda Li, Markus Rabe, Charles Staats, Mateja Jamnik, Christian Szegedy

NeurIPS 2022 Block-Recurrent Transformers DeLesley Hutchins, Imanol Schlag, Yuhuai Wu, Ethan Dyer, Behnam Neyshabur

NeurIPS 2022 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

NeurIPSW 2022 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

NeurIPS 2022 Insights into Pre-Training via Simpler Synthetic Tasks Yuhuai Wu, Felix Li, Percy Liang

ICLR 2022 Invariant Causal Representation Learning for Out-of-Distribution Generalization Chaochao Lu, Yuhuai Wu, José Miguel Hernández-Lobato, Bernhard Schölkopf

ICLR 2022 Memorizing Transformers Yuhuai Wu, Markus Norman Rabe, DeLesley Hutchins, Christian Szegedy

NeurIPS 2022 Path Independent Equilibrium Models Can Better Exploit Test-Time Computation Cem Anil, Ashwini Pokle, Kaiqu Liang, Johannes Treutlein, Yuhuai Wu, Shaojie Bai, J. Zico Kolter, Roger B Grosse

ICLR 2022 Proof Artifact Co-Training for Theorem Proving with Language Models Jesse Michael Han, Jason Rute, Yuhuai Wu, Edward Ayers, Stanislas Polu

NeurIPS 2022 STaR: Bootstrapping Reasoning with Reasoning Eric Zelikman, Yuhuai Wu, Jesse Mu, Noah Goodman

NeurIPS 2022 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

NeurIPS 2022 Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers Albert Qiaochu Jiang, Wenda Li, Szymon Tworkowski, Konrad Czechowski, Tomasz Odrzygóźdź, Piotr Miłoś, Yuhuai Wu, Mateja Jamnik

ICML 2021 Efficient Statistical Tests: A Neural Tangent Kernel Approach Sheng Jia, Ehsan Nezhadarya, Yuhuai Wu, Jimmy Ba

ICLR 2021 INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving Yuhuai Wu, Albert Jiang, Jimmy Ba, Roger Baker Grosse

ICLR 2021 IsarStep: A Benchmark for High-Level Mathematical Reasoning Wenda Li, Lei Yu, Yuhuai Wu, Lawrence C. Paulson

ICML 2021 LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning Yuhuai Wu, Markus N Rabe, Wenda Li, Jimmy Ba, Roger B Grosse, Christian Szegedy

AAAI 2021 Learning Branching Heuristics for Propositional Model Counting Pashootan Vaezipoor, Gil Lederman, Yuhuai Wu, Chris J. Maddison, Roger B. Grosse, Sanjit A. Seshia, Fahiem Bacchus

NeurIPS 2021 Subgoal Search for Complex Reasoning Tasks Konrad Czechowski, Tomasz Odrzygóźdź, Marek Zbysiński, Michał Zawalski, Krzysztof Olejnik, Yuhuai Wu, Łukasz Kuciński, Piotr Miłoś

ICML 2020 OPtions as REsponses: Grounding Behavioural Hierarchies in Multi-Agent Reinforcement Learning Alexander Vezhnevets, Yuhuai Wu, Maria Eckstein, Rémi Leblond, Joel Z Leibo

ICLR 2018 Backpropagation Through the Void: Optimizing Control Variates for Black-Box Gradient Estimation Will Grathwohl, Dami Choi, Yuhuai Wu, Geoff Roeder, David Duvenaud

NeurIPS 2018 The Importance of Sampling inMeta-Reinforcement Learning Bradly Stadie, Ge Yang, Rein Houthooft, Peter Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever

ICLR 2018 Understanding Short-Horizon Bias in Stochastic Meta-Optimization Yuhuai Wu, Mengye Ren, Renjie Liao, Roger Grosse.

ICLR 2017 On the Quantitative Analysis of Decoder-Based Generative Models Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, Roger B. Grosse

NeurIPS 2017 Scalable Trust-Region Method for Deep Reinforcement Learning Using Kronecker-Factored Approximation Yuhuai Wu, Elman Mansimov, Roger B Grosse, Shun Liao, Jimmy Ba

NeurIPS 2017 Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference Geoffrey Roeder, Yuhuai Wu, David K. Duvenaud

NeurIPS 2016 Architectural Complexity Measures of Recurrent Neural Networks Saizheng Zhang, Yuhuai Wu, Tong Che, Zhouhan Lin, Roland Memisevic, Ruslan Salakhutdinov, Yoshua Bengio

NeurIPS 2016 On Multiplicative Integration with Recurrent Neural Networks Yuhuai Wu, Saizheng Zhang, Ying Zhang, Yoshua Bengio, Ruslan Salakhutdinov

NeurIPS 2016 Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations Behnam Neyshabur, Yuhuai Wu, Ruslan Salakhutdinov, Nati Srebro