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Yuhuai Wu

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

Achievements

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+12 more ↓ πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (8) 🌍 Conference Polyglot (5) 🌈 Renaissance Researcher (9) 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