A New Variation of Hat Guessing Games. - Tengyu Ma 001; Xiaoming Sun 001; Huacheng Yu (COCOON, 2011)

On a conjecture of Butler and Graham. - Tengyu Ma 001; Xiaoming Sun 001; Huacheng Yu (Des. Codes Cryptogr., 2013)

Provable Bounds for Learning Some Deep Representations. - Sanjeev Arora; Aditya Bhaskara; Rong Ge 001; Tengyu Ma 001 (ICML, 2014)

On Communication Cost of Distributed Statistical Estimation and Dimensionality. - Ankit Garg; Tengyu Ma 001; Huy L. Nguyen (NIPS, 2014)

More Algorithms for Provable Dictionary Learning. - Sanjeev Arora; Aditya Bhaskara; Rong Ge 001; Tengyu Ma 001 (CoRR, 2014)

Lower Bound for High-Dimensional Statistical Learning Problem via Direct-Sum Theorem. - Ankit Garg; Tengyu Ma 001; Huy L. Nguyen (CoRR, 2014)

Decomposing Overcomplete 3rd Order Tensors using Sum-of-Squares Algorithms. - Rong Ge 001; Tengyu Ma 001 (APPROX-RANDOM, 2015)

Simple, Efficient, and Neural Algorithms for Sparse Coding. - Sanjeev Arora; Rong Ge 001; Tengyu Ma 001; Ankur Moitra (COLT, 2015)

Online Learning of Eigenvectors. - Dan Garber; Elad Hazan; Tengyu Ma 001 (ICML, 2015)

Sum-of-Squares Lower Bounds for Sparse PCA. - Tengyu Ma 001; Avi Wigderson (NIPS, 2015)

Random Walks on Context Spaces: Towards an Explanation of the Mysteries of Semantic Word Embeddings. - Sanjeev Arora; Yuanzhi Li; Yingyu Liang; Tengyu Ma 001; Andrej Risteski (CoRR, 2015)

Why are deep nets reversible: A simple theory, with implications for training. - Sanjeev Arora; Yingyu Liang; Tengyu Ma 001 (CoRR, 2015)

Distributed Stochastic Variance Reduced Gradient Methods. - Jason D. Lee; Tengyu Ma 001; Qihang Lin (CoRR, 2015)

Polynomial-Time Tensor Decompositions with Sum-of-Squares. - Tengyu Ma 001; Jonathan Shi; David Steurer (FOCS, 2016)

Provable Algorithms for Inference in Topic Models. - Sanjeev Arora; Rong Ge 001; Frederic Koehler; Tengyu Ma 001; Ankur Moitra (ICML, 2016)

Matrix Completion has No Spurious Local Minimum. - Rong Ge 001; Jason D. Lee; Tengyu Ma 001 (NIPS, 2016)

A Non-generative Framework and Convex Relaxations for Unsupervised Learning. - Elad Hazan; Tengyu Ma 001 (NIPS, 2016)

Communication lower bounds for statistical estimation problems via a distributed data processing inequality. - Mark Braverman; Ankit Garg; Tengyu Ma 001; Huy L. Nguyen; David P. Woodruff (STOC, 2016)

Finding Approximate Local Minima for Nonconvex Optimization in Linear Time. - Naman Agarwal; Zeyuan Allen Zhu; Brian Bullins; Elad Hazan; Tengyu Ma 001 (CoRR, 2016)

The Simulated Greedy Algorithm for Several Submodular Matroid Secretary Problems. - Tengyu Ma 001; Bo Tang 003; Yajun Wang 001 (Theory Comput. Syst., 2016)

A Latent Variable Model Approach to PMI-based Word Embeddings. - Sanjeev Arora; Yuanzhi Li; Yingyu Liang; Tengyu Ma 001; Andrej Risteski (Trans. Assoc. Comput. Linguistics, 2016)

On the Ability of Neural Nets to Express Distributions. - Holden Lee; Rong Ge 001; Tengyu Ma 001; Andrej Risteski; Sanjeev Arora (COLT, 2017)

A Simple but Tough-to-Beat Baseline for Sentence Embeddings. - Sanjeev Arora; Yingyu Liang; Tengyu Ma 001 (ICLR (Poster), 2017)

Identity Matters in Deep Learning. - Moritz Hardt; Tengyu Ma 001 (ICLR (Poster), 2017)

Generalization and Equilibrium in Generative Adversarial Nets (GANs). - Sanjeev Arora; Rong Ge 001; Yingyu Liang; Tengyu Ma 001; Yi Zhang 074 (ICML, 2017)

Finding approximate local minima faster than gradient descent. - Naman Agarwal; Zeyuan Allen Zhu; Brian Bullins; Elad Hazan; Tengyu Ma 001 (STOC, 2017)

Provable learning of noisy-OR networks. - Sanjeev Arora; Rong Ge 001; Tengyu Ma 001; Andrej Risteski (STOC, 2017)

Algorithmic Regularization in Over-parameterized Matrix Recovery. - Yuanzhi Li; Tengyu Ma 001; Hongyang Zhang 005 (CoRR, 2017)

Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement. - Jason D. Lee; Qihang Lin; Tengyu Ma 001; Tianbao Yang (J. Mach. Learn. Res., 2017)

Non-convex Optimization for Machine Learning: Design, Analysis, and Understanding - Tengyu Ma 001 (2017)

A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors. - Mikhail Khodak; Nikunj Saunshi; Yingyu Liang; Tengyu Ma 001; Brandon Stewart; Sanjeev Arora (ACL (1), 2018)

Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations. - Yuanzhi Li; Tengyu Ma 001; Hongyang Zhang 005 (COLT, 2018)

Learning One-hidden-layer Neural Networks with Landscape Design. - Rong Ge 001; Jason D. Lee; Tengyu Ma 001 (ICLR (Poster), 2018)

Generalization and equilibrium in generative adversarial nets (GANs) (invited talk). - Tengyu Ma 001 (STOC, 2018)

Algorithmic Framework for Model-based Reinforcement Learning with Theoretical Guarantees. - Huazhe Xu; Yuanzhi Li; Yuandong Tian; Trevor Darrell; Tengyu Ma 001 (CoRR, 2018)

On the Margin Theory of Feedforward Neural Networks. - Colin Wei; Jason D. Lee; Qiang Liu 001; Tengyu Ma 001 (CoRR, 2018)

Gradient Descent Learns Linear Dynamical Systems. - Moritz Hardt; Tengyu Ma 001; Benjamin Recht (J. Mach. Learn. Res., 2018)

Linear Algebraic Structure of Word Senses, with Applications to Polysemy. - Sanjeev Arora; Yuanzhi Li; Yingyu Liang; Tengyu Ma 001; Andrej Risteski (Trans. Assoc. Comput. Linguistics, 2018)

On the Performance of Thompson Sampling on Logistic Bandits. - Shi Dong 003; Tengyu Ma 001; Benjamin Van Roy (COLT, 2019)

Approximability of Discriminators Implies Diversity in GANs. - Yu Bai 017; Tengyu Ma 001; Andrej Risteski (ICLR (Poster), 2019)

Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees. - Yuping Luo; Huazhe Xu; Yuanzhi Li; Yuandong Tian; Trevor Darrell; Tengyu Ma 001 (ICLR (Poster), 2019)

Fixup Initialization: Residual Learning Without Normalization. - Hongyi Zhang; Yann N. Dauphin; Tengyu Ma 001 (ICLR (Poster), 2019)

Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss. - Kaidi Cao; Colin Wei; Adrien Gaidon; Nikos Aréchiga; Tengyu Ma 001 (NeurIPS, 2019)

Verified Uncertainty Calibration. - Ananya Kumar; Percy Liang; Tengyu Ma 001 (NeurIPS, 2019)

Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks. - Yuanzhi Li; Colin Wei; Tengyu Ma 001 (NeurIPS, 2019)

Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel. - Colin Wei; Jason D. Lee; Qiang Liu 001; Tengyu Ma 001 (NeurIPS, 2019)

Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation. - Colin Wei; Tengyu Ma 001 (NeurIPS, 2019)

A Model-based Approach for Sample-efficient Multi-task Reinforcement Learning. - Nicholas C. Landolfi; Garrett Thomas; Tengyu Ma 001 (CoRR, 2019)

Improved Sample Complexities for Deep Networks and Robust Classification via an All-Layer Margin. - Colin Wei; Tengyu Ma 001 (CoRR, 2019)

Bootstrapping the Expressivity with Model-based Planning. - Kefan Dong; Yuping Luo; Tengyu Ma 001 (CoRR, 2019)

Optimal Design of Process Flexibility for General Production Systems. - Xi Chen 010; Tengyu Ma 001; Jiawei Zhang 006; Yuan Zhou 007 (Oper. Res., 2019)

Why Do Local Methods Solve Nonconvex Problems? - Tengyu Ma 001 (Beyond the Worst-Case Analysis of Algorithms, 2020)

Learning Over-Parametrized Two-Layer Neural Networks beyond NTK. - Yuanzhi Li; Tengyu Ma 001; Hongyang R. Zhang (COLT, 2020)

Robust and On-the-Fly Dataset Denoising for Image Classification. - Jiaming Song; Yann N. Dauphin; Michael Auli; Tengyu Ma 001 (ECCV (29), 2020)

Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling. - Yuping Luo; Huazhe Xu; Tengyu Ma 001 (ICLR, 2020)

Improved Sample Complexities for Deep Neural Networks and Robust Classification via an All-Layer Margin. - Colin Wei; Tengyu Ma 001 (ICLR, 2020)

On the Expressivity of Neural Networks for Deep Reinforcement Learning. - Kefan Dong; Yuping Luo; Tianhe Yu; Chelsea Finn; Tengyu Ma 001 (ICML, 2020)

Understanding Self-Training for Gradual Domain Adaptation. - Ananya Kumar; Tengyu Ma 001; Percy Liang (ICML, 2020)

The Implicit and Explicit Regularization Effects of Dropout. - Colin Wei; Sham M. Kakade; Tengyu Ma 001 (ICML, 2020)

Individual Calibration with Randomized Forecasting. - Shengjia Zhao; Tengyu Ma 001; Stefano Ermon (ICML, 2020)

Self-training Avoids Using Spurious Features Under Domain Shift. - Yining Chen; Colin Wei; Ananya Kumar; Tengyu Ma 001 (NeurIPS, 2020)

Model-based Adversarial Meta-Reinforcement Learning. - Zichuan Lin; Garrett Thomas; Guangwen Yang; Tengyu Ma 001 (NeurIPS, 2020)

Beyond Lazy Training for Over-parameterized Tensor Decomposition. - Xiang Wang 011; Chenwei Wu 002; Jason D. Lee; Tengyu Ma 001; Rong Ge 001 (NeurIPS, 2020)

MOPO: Model-based Offline Policy Optimization. - Tianhe Yu; Garrett Thomas; Lantao Yu; Stefano Ermon; James Y. Zou; Sergey Levine; Chelsea Finn; Tengyu Ma 001 (NeurIPS, 2020)

Federated Accelerated Stochastic Gradient Descent. - Honglin Yuan; Tengyu Ma 001 (NeurIPS, 2020)

Robust and On-the-fly Dataset Denoising for Image Classification. - Jiaming Song; Lunjia Hu; Yann N. Dauphin; Michael Auli; Tengyu Ma 001 (CoRR, 2020)

Active Online Domain Adaptation. - Yining Chen; Haipeng Luo; Tengyu Ma 001; Chicheng Zhang (CoRR, 2020)

Simplifying Models with Unlabeled Output Data. - Sang Michael Xie; Tengyu Ma 001; Percy Liang (CoRR, 2020)

Learning Over-Parametrized Two-Layer ReLU Neural Networks beyond NTK. - Yuanzhi Li; Tengyu Ma 001; Hongyang R. Zhang (CoRR, 2020)

Entity and Evidence Guided Relation Extraction for DocRED. - Kevin Huang 002; Guangtao Wang; Tengyu Ma 001; Jing Huang 019 (CoRR, 2020)

Meta-learning Transferable Representations with a Single Target Domain. - Hong Liu; Jeff Z. HaoChen; Colin Wei; Tengyu Ma 001 (CoRR, 2020)

Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling. - Wenxuan Zhou; Kevin Huang 002; Tengyu Ma 001; Jing Huang 019 (AAAI, 2021)

Active Online Learning with Hidden Shifting Domains. - Yining Chen; Haipeng Luo; Tengyu Ma 001; Chicheng Zhang (AISTATS, 2021)

Shape Matters: Understanding the Implicit Bias of the Noise Covariance. - Jeff Z. HaoChen; Colin Wei; Jason D. Lee; Tengyu Ma 001 (COLT, 2021)

Fine-Grained Gap-Dependent Bounds for Tabular MDPs via Adaptive Multi-Step Bootstrap. - Haike Xu; Tengyu Ma 001; Simon S. Du (COLT, 2021)

Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization. - Kaidi Cao; Yining Chen; Junwei Lu; Nikos Aréchiga; Adrien Gaidon; Tengyu Ma 001 (ICLR, 2021)

Optimal Regularization can Mitigate Double Descent. - Preetum Nakkiran; Prayaag Venkat; Sham M. Kakade; Tengyu Ma 001 (ICLR, 2021)

Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data. - Colin Wei; Kendrick Shen; Yining Chen; Tengyu Ma 001 (ICLR, 2021)

In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness. - Sang Michael Xie; Ananya Kumar; Robbie Jones; Fereshte Khani; Tengyu Ma 001; Percy Liang (ICLR, 2021)

Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved Generalization. - Sang Michael Xie; Tengyu Ma 001; Percy Liang (ICML, 2021)

Variance-reduced First-order Meta-learning for Natural Language Processing Tasks. - Lingxiao Wang 001; Kevin Huang 002; Tengyu Ma 001; Quanquan Gu; Jing Huang 019 (NAACL-HLT, 2021)

Label Noise SGD Provably Prefers Flat Global Minimizers. - Alex Damian; Tengyu Ma 001; Jason D. Lee (NeurIPS, 2021)

Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature. - Kefan Dong; Jiaqi Yang 001; Tengyu Ma 001 (NeurIPS, 2021)

Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss. - Jeff Z. HaoChen; Colin Wei; Adrien Gaidon; Tengyu Ma 001 (NeurIPS, 2021)

Learning Barrier Certificates: Towards Safe Reinforcement Learning with Zero Training-time Violations. - Yuping Luo; Tengyu Ma 001 (NeurIPS, 2021)

Safe Reinforcement Learning by Imagining the Near Future. - Garrett Thomas; Yuping Luo; Tengyu Ma 001 (NeurIPS, 2021)

Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning. - Colin Wei; Sang Michael Xie; Tengyu Ma 001 (NeurIPS, 2021)

Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration. - Shengjia Zhao; Michael P. Kim; Roshni Sahoo; Tengyu Ma 001; Stefano Ermon (NeurIPS, 2021)

Entity and Evidence Guided Document-Level Relation Extraction. - Kevin Huang 002; Peng Qi 003; Guangtao Wang; Tengyu Ma 001; Jing Huang 019 (RepL4NLP@ACL-IJCNLP, 2021)

Why Do Local Methods Solve Nonconvex Problems? - Tengyu Ma 001 (CoRR, 2021)

Joint System-Wise Optimization for Pipeline Goal-Oriented Dialog System. - Zichuan Lin; Jing Huang 019; Bowen Zhou 001; Xiaodong He 001; Tengyu Ma 001 (CoRR, 2021)

Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspective. - Margalit R. Glasgow; Honglin Yuan; Tengyu Ma 001 (AISTATS, 2022)

DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization. - Aviral Kumar; Rishabh Agarwal; Tengyu Ma 001; Aaron C. Courville; George Tucker; Sergey Levine (ICLR, 2022)

Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution. - Ananya Kumar; Aditi Raghunathan; Robbie Matthew Jones; Tengyu Ma 001; Percy Liang (ICLR, 2022)

Self-supervised Learning is More Robust to Dataset Imbalance. - Hong Liu; Jeff Z. HaoChen; Adrien Gaidon; Tengyu Ma 001 (ICLR, 2022)

An Explanation of In-context Learning as Implicit Bayesian Inference. - Sang Michael Xie; Aditi Raghunathan; Percy Liang; Tengyu Ma 001 (ICLR, 2022)

Near-Optimal Algorithms for Autonomous Exploration and Multi-Goal Stochastic Shortest Path. - Haoyuan Cai; Tengyu Ma 001; Simon S. Du (ICML, 2022)

Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification. - Ling Pan; Longbo Huang; Tengyu Ma 001; Huazhe Xu (ICML, 2022)

Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation. - Kendrick Shen; Robbie M. Jones; Ananya Kumar; Sang Michael Xie; Jeff Z. HaoChen; Tengyu Ma 001; Percy Liang (ICML, 2022)

Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments. - Yining Chen; Elan Rosenfeld; Mark Sellke; Tengyu Ma 001; Andrej Risteski (NeurIPS, 2022)

Beyond Separability: Analyzing the Linear Transferability of Contrastive Representations to Related Subpopulations. - Jeff Z. HaoChen; Colin Wei; Ananya Kumar; Tengyu Ma 001 (NeurIPS, 2022)

Statistically Meaningful Approximation: a Case Study on Approximating Turing Machines with Transformers. - Colin Wei; Yining Chen; Tengyu Ma 001 (NeurIPS, 2022)

Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift. - Ananya Kumar; Tengyu Ma 001; Percy Liang; Aditi Raghunathan (UAI, 2022)

How Does Sharpness-Aware Minimization Minimize Sharpness? - Kaiyue Wen; Tengyu Ma 001; Zhiyuan Li 005 (CoRR, 2022)

On the optimization landscape of tensor decompositions. - Rong Ge 001; Tengyu Ma 001 (Math. Program., 2022)

Toward L_∞Recovery of Nonlinear Functions: A Polynomial Sample Complexity Bound for Gaussian Random Fields. - Kefan Dong; Tengyu Ma 001 (COLT, 2023)

Symbol tuning improves in-context learning in language models. - Jerry W. Wei; Le Hou; Andrew K. Lampinen; Xiangning Chen; Da Huang; Yi Tay; Xinyun Chen; Yifeng Lu; Denny Zhou; Tengyu Ma 001; Quoc V. Le (EMNLP, 2023)

What learning algorithm is in-context learning? Investigations with linear models. - Ekin Akyürek; Dale Schuurmans; Jacob Andreas; Tengyu Ma 001; Denny Zhou (ICLR, 2023)

First Steps Toward Understanding the Extrapolation of Nonlinear Models to Unseen Domains. - Kefan Dong; Tengyu Ma 001 (ICLR, 2023)

Asymptotic Instance-Optimal Algorithms for Interactive Decision Making. - Kefan Dong; Tengyu Ma 001 (ICLR, 2023)

Max-Margin Works while Large Margin Fails: Generalization without Uniform Convergence. - Margalit Glasgow; Colin Wei; Mary Wootters; Tengyu Ma 001 (ICLR, 2023)

A theoretical study of inductive biases in contrastive learning. - Jeff Z. HaoChen; Tengyu Ma 001 (ICLR, 2023)

How Sharpness-Aware Minimization Minimizes Sharpness? - Kaiyue Wen; Tengyu Ma 001; Zhiyuan Li 005 (ICLR, 2023)

Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models. - Hong Liu; Sang Michael Xie; Zhiyuan Li 005; Tengyu Ma 001 (ICML, 2023)

What is the Inductive Bias of Flatness Regularization? A Study of Deep Matrix Factorization Models. - Khashayar Gatmiry; Zhiyuan Li 005; Tengyu Ma 001; Sashank J. Reddi; Stefanie Jegelka; Ching-Yao Chuang (NeurIPS, 2023)

Beyond NTK with Vanilla Gradient Descent: A Mean-Field Analysis of Neural Networks with Polynomial Width, Samples, and Time. - Arvind V. Mahankali; Haochen Zhang; Kefan Dong; Margalit Glasgow; Tengyu Ma 001 (NeurIPS, 2023)

Sharpness Minimization Algorithms Do Not Only Minimize Sharpness To Achieve Better Generalization. - Kaiyue Wen; Zhiyuan Li 005; Tengyu Ma 001 (NeurIPS, 2023)

DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining. - Sang Michael Xie; Hieu Pham 001; Xuanyi Dong; Nan Du 002; Hanxiao Liu; Yifeng Lu; Percy Liang; Quoc V. Le; Tengyu Ma 001; Adams Wei Yu (NeurIPS, 2023)

Data Selection for Language Models via Importance Resampling. - Sang Michael Xie; Shibani Santurkar; Tengyu Ma 001; Percy Liang (NeurIPS, 2023)

Larger language models do in-context learning differently. - Jerry W. Wei; Jason Wei; Yi Tay; Dustin Tran; Albert Webson; Yifeng Lu; Xinyun Chen; Hanxiao Liu; Da Huang; Denny Zhou; Tengyu Ma 001 (CoRR, 2023)

Toward L∞-recovery of Nonlinear Functions: A Polynomial Sample Complexity Bound for Gaussian Random Fields. - Kefan Dong; Tengyu Ma 001 (CoRR, 2023)

The Inductive Bias of Flatness Regularization for Deep Matrix Factorization. - Khashayar Gatmiry; Zhiyuan Li 005; Ching-Yao Chuang; Sashank J. Reddi; Tengyu Ma 001; Stefanie Jegelka (CoRR, 2023)

Beyond NTK with Vanilla Gradient Descent: A Mean-Field Analysis of Neural Networks with Polynomial Width, Samples, and Time. - Arvind V. Mahankali; Jeff Z. HaoChen; Kefan Dong; Margalit Glasgow; Tengyu Ma 001 (CoRR, 2023)

Chain of Thought Empowers Transformers to Solve Inherently Serial Problems. - Zhiyuan Liu 001; Hong Liu; Denny Zhou; Tengyu Ma 001 (ICLR, 2024)

Large Language Models as Tool Makers. - Tianle Cai; Xuezhi Wang 002; Tengyu Ma 001; Xinyun Chen; Denny Zhou (ICLR, 2024)

Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training. - Hong Liu; Zhiyuan Li 005; David Leo Wright Hall; Percy Liang; Tengyu Ma 001 (ICLR, 2024)

One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention. - Arvind V. Mahankali; Tatsunori Hashimoto; Tengyu Ma 001 (ICLR, 2024)

Linguistic Calibration of Long-Form Generations. - Neil Band; Xuechen Li 005; Tengyu Ma 001; Tatsunori Hashimoto (ICML, 2024)

Chain of Thought Empowers Transformers to Solve Inherently Serial Problems. - Zhiyuan Li 005; Hong Liu; Denny Zhou; Tengyu Ma 001 (CoRR, 2024)

Linguistic Calibration of Language Models. - Neil Band; Xuechen Li 005; Tengyu Ma 001; Tatsunori Hashimoto (CoRR, 2024)

Understanding Warmup-Stable-Decay Learning Rates: A River Valley Loss Landscape Perspective. - Kaiyue Wen; Zhiyuan Li 005; Jason S. Wang; David Hall 006; Percy Liang; Tengyu Ma 001 (CoRR, 2024)

Formal Theorem Proving by Rewarding LLMs to Decompose Proofs Hierarchically. - Kefan Dong; Arvind V. Mahankali; Tengyu Ma 001 (CoRR, 2024)

Understanding Warmup-Stable-Decay Learning Rates: A River Valley Loss Landscape View. - Kaiyue Wen; Zhiyuan Li 005; Jason S. Wang; David Leo Wright Hall; Percy Liang; Tengyu Ma 001 (ICLR, 2025)

STP: Self-play LLM Theorem Provers with Iterative Conjecturing and Proving. - Kefan Dong; Tengyu Ma 001 (CoRR, 2025)

Non-Asymptotic Length Generalization. - Thomas Chen; Tengyu Ma 001; Zhiyuan Li 005 (CoRR, 2025)