← Authors

Yuhuai Wu

34 papers · 2016–2024 · 5 conferences · across top CS/AI conferences

Achievements

🌈 Renaissance Researcher (9) 🐝 Cross-Pollinator (14) 🐣 Hot Topic Early Bird 🧭 Keyword Pioneer πŸ—ΊοΈ Taxonomy Completionist (45) 🌍 Conference Polyglot (5) πŸƒ Academic Marathon (8) πŸŒ‰ Interdisciplinary Bridge 🌟 Keyword Trendsetter Combo (4) 🧬 Topic Evolution πŸ† Grand Slam πŸ‘‘ Triple Crown πŸš€ Conference Pioneer ⚑ Prolific Year (12) πŸ—ƒοΈ Keyword Collector (104) πŸ’Ž Century Club (34) πŸ”₯ Unstoppable (5)

Conferences

NIPS (17) ICLR (12) ICML (3) AAAI (1) NAACL (1)

Top co-authors

Christian Szegedy (6) Wenda Li (6) Piotr MiΕ‚oΕ› (5) Szymon Tworkowski (4) Jin Peng Zhou (4) Roger B Grosse (4) Behnam Neyshabur (4) Jimmy Ba (4) Ruslan Salakhutdinov (3) Albert Qiaochu Jiang (3)

Keywords

recurrent neural network (4) language modeling (3) language model (3) large language model (3) in-context learning (2) mathematical reasoning (2) synthetic task (2) sequence modeling (2) few-shot learning (1) theorem proving (1) contrastive learning (1) attention mechanism (1) natural policy gradient (1) representation learning (1) puzzle solving (1) neural tangent kernel (1) out-of-distribution generalization (1) transfer learning (1) best-first search (1) chain-of-thought reasoning (1)

Papers

Don't Trust: Verify -- Grounding LLM Quantitative Reasoning with Autoformalization ICLR 2024
REFACTOR: Learning to Extract Theorems from Proofs ICLR 2024
Magnushammer: A Transformer-Based Approach to Premise Selection ICLR 2024
Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs ICLR 2023
Lexinvariant Language Models NIPS 2023
Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Search ICLR 2023
Focused Transformer: Contrastive Training for Context Scaling NIPS 2023
Insights into Pre-training via Simpler Synthetic Tasks NIPS 2022
Autoformalization with Large Language Models NIPS 2022
Block-Recurrent Transformers NIPS 2022
Exploring Length Generalization in Large Language Models NIPS 2022
Invariant Causal Representation Learning for Out-of-Distribution Generalization ICLR 2022
Memorizing Transformers ICLR 2022
Proof Artifact Co-Training for Theorem Proving with Language Models ICLR 2022
Hierarchical Transformers Are More Efficient Language Models NAACL 2022
Solving Quantitative Reasoning Problems with Language Models NIPS 2022
Path Independent Equilibrium Models Can Better Exploit Test-Time Computation NIPS 2022
Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers NIPS 2022
STaR: Bootstrapping Reasoning With Reasoning NIPS 2022
Learning Branching Heuristics for Propositional Model Counting AAAI 2021
Subgoal Search For Complex Reasoning Tasks NIPS 2021
IsarStep: a Benchmark for High-level Mathematical Reasoning ICLR 2021
INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving ICLR 2021
Efficient Statistical Tests: A Neural Tangent Kernel Approach ICML 2021
LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning ICML 2021
OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning ICML 2020
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation ICLR 2018
Understanding Short-Horizon Bias in Stochastic Meta-Optimization ICLR 2018
The Importance of Sampling inMeta-Reinforcement Learning NIPS 2018
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference NIPS 2017
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation NIPS 2017
On Multiplicative Integration with Recurrent Neural Networks NIPS 2016
Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations NIPS 2016
Architectural Complexity Measures of Recurrent Neural Networks NIPS 2016