---
title: "Tengyu Ma — all works — Elamur Research"
description: "All 33 works of Tengyu Ma in the Elamur Research library, the most cited first: our summaries where we have them, else the original."
canonical: "https://research.elamur.ai/free/en/authors/tengyu-ma/works/"
---
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# Tengyu Ma — all works

33 works · [← Tengyu Ma](https://md.research.elamur.ai/free/en/authors/tengyu-ma/)

1. **[Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling ↗](https://doi.org/10.1609/aaai.v35i16.17717) Proceedings of the AAAI Conference on Artificial Intelligence · no summary yet**Wenxuan Zhou et al.*2021 · 407 citations*
2. **[A Latent Variable Model Approach to PMI-based Word Embeddings](https://md.research.elamur.ai/free/en/papers/arora-2016-latent-variable-model-approach-pmi/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Sanjeev Arora, Yuanzhi Li, Yingyu Liang, et al.*2016 · 372 citations*
3. [**An Explanation of In-context Learning as Implicit Bayesian Inference arXiv:2111.02080**Sang Michael Xie, Aditi Raghunathan, Percy Liang, et al.*2021 · 286 citations*](https://md.research.elamur.ai/free/en/papers/xie-2021-explanation-context-learning-implicit-bayesian/)
4. [**Larger language models do in-context learning differently arXiv:2303.03846**Jerry Wei, Jason Wei, Yi Tay, et al.*2023 · 285 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-larger-language-models-context-learning/)
5. **[MOPO: Model-based Offline Policy Optimization ↗](https://arxiv.org/abs/2005.13239) arXiv (Cornell University) · no summary yet**Tianhe Yu et al.*2020 · 260 citations*
6. **[Linear Algebraic Structure of Word Senses, with Applications to Polysemy](https://md.research.elamur.ai/free/en/papers/arora-2018-linear-algebraic-structure-word-senses/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Sanjeev Arora, Yuanzhi Li, Yingyu Liang, et al.*2018 · 254 citations*
7. **[Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution ↗](https://arxiv.org/abs/2202.10054) arXiv (Cornell University) · no summary yet**Ananya Kumar et al.*2022 · 248 citations*
8. [**What learning algorithm is in-context learning? Investigations with linear models arXiv:2211.15661**Ekin Akyürek, Dale Schuurmans, Jacob Andreas, et al.*2022 · 236 citations*](https://md.research.elamur.ai/free/en/papers/akyurek-2022-learning-algorithm-context-learning-investigations/)
9. **[Verified Uncertainty Calibration ↗](https://arxiv.org/abs/1909.10155) arXiv:1909.10155 · no summary yet**Ananya Kumar, Percy Liang, Tengyu Ma*2019 · 172 citations*
10. **[DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining ↗](https://arxiv.org/abs/2305.10429) arXiv:2305.10429 · no summary yet**Sang Michael Xie et al.*2023 · 156 citations*
11. **[Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks ↗](https://arxiv.org/abs/1907.04595) arXiv (Cornell University) · no summary yet**Yuanzhi Li, Colin Wei, Tengyu Ma*2019 · 138 citations*
12. **[Data Selection for Language Models via Importance Resampling ↗](https://arxiv.org/abs/2302.03169) arXiv:2302.03169 · no summary yet**Sang Michael Xie et al.*2023 · 137 citations*
13. **[A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors ↗](https://aclanthology.org/P18-1002/) Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yet**Mikhail Khodak et al.*2018 · 128 citations*
14. **[Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees ↗](https://arxiv.org/abs/1807.03858) arXiv (Cornell University) · no summary yet**Yuping Luo et al.*2018 · 125 citations*
15. **[Large Language Models as Tool Makers ↗](https://arxiv.org/abs/2305.17126) arXiv:2305.17126 · no summary yet**Tianle Cai et al.*2023 · 123 citations*
16. **[Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training ↗](https://arxiv.org/abs/2305.14342) arXiv:2305.14342 · no summary yet**Hong Liu et al.*2023 · 70 citations*
17. **[Symbol tuning improves in-context learning in language models ↗](https://aclanthology.org/2023.emnlp-main.61/) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · no summary yet**Jerry Wei et al.*2023 · 68 citations*
18. **[STP: Self-play LLM Theorem Provers with Iterative Conjecturing and Proving ↗](https://arxiv.org/abs/2502.00212) arXiv:2502.00212 · no summary yet**Kefan Dong, Tengyu Ma*2025 · 43 citations*
19. **[Understanding Self-Training for Gradual Domain Adaptation ↗](https://arxiv.org/abs/2002.11361) arXiv (Cornell University) · no summary yet**Ananya Kumar, Tengyu Ma, Percy Liang*2020 · 39 citations*
20. **[Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations ↗](https://arxiv.org/abs/1712.09203) arXiv (Cornell University) · no summary yet**Yuanzhi Li, Tengyu Ma, Hongyang Zhang*2017 · 38 citations*
21. **[Learning Over-Parametrized Two-Layer ReLU Neural Networks beyond NTK ↗](https://arxiv.org/abs/2007.04596) arXiv (Cornell University) · no summary yet**Yuanzhi Li, Tengyu Ma, Hongyang Zhang*2020 · 33 citations*
22. **[Individual Calibration with Randomized Forecasting ↗](https://arxiv.org/abs/2006.10288) arXiv (Cornell University) · no summary yet**Shengjia Zhao, Tengyu Ma, Stefano Ermon*2020 · 24 citations*
23. **[Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation ↗](https://arxiv.org/abs/2204.00570) arXiv (Cornell University) · no summary yet**Kendrick Shen et al.*2022 · 23 citations*
24. **[One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention ↗](https://arxiv.org/abs/2307.03576) arXiv:2307.03576 · no summary yet**Arvind Mahankali, Tatsunori B. Hashimoto, Tengyu Ma*2023 · 22 citations*
25. **[RAND-WALK: A Latent Variable Model Approach to Word Embeddings ↗](https://arxiv.org/abs/1502.03520) arXiv (Cornell University) · no summary yet**Sanjeev Arora et al.*2015 · 21 citations*
26. **[Understanding Warmup-Stable-Decay Learning Rates: A River Valley Loss Landscape Perspective ↗](https://arxiv.org/abs/2410.05192) arXiv (Cornell University) · no summary yet**Kaiyue Wen et al.*2024 · 18 citations*
27. **[In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness ↗](https://arxiv.org/abs/2012.04550) arXiv (Cornell University) · no summary yet**Sang Michael Xie et al.*2020 · 18 citations*
28. **[Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration ↗](https://arxiv.org/abs/2107.05719) arXiv (Cornell University) · no summary yet**Shengjia Zhao et al.*2021 · 12 citations*
29. **[Fantastic Pretraining Optimizers and Where to Find Them ↗](https://arxiv.org/abs/2509.02046) arXiv (Cornell University) · no summary yet**Kaiyue Wen et al.*2025 · 11 citations*
30. **[Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift ↗](https://arxiv.org/abs/2207.08977) arXiv (Cornell University) · no summary yet**Ananya Kumar et al.*2022 · 10 citations*
31. **[Fantastic Pretraining Optimizers and Where to Find Them II: Hyperball Optimization ↗](https://arxiv.org/abs/2606.16899) arXiv (Cornell University) · no summary yet**Kaiyue Wen et al.*2026 · 2 citations*
32. **[Simplifying Models with Unlabeled Output Data ↗](https://arxiv.org/abs/2006.16205) arXiv (Cornell University) · no summary yet**Sang Michael Xie, Tengyu Ma, Percy Liang*2021 · 1 citation*
33. **[Priced Guidance: Can Language Models Generate Future Research Ideas? ↗](https://arxiv.org/abs/2610.04976) arXiv (Cornell University) · no summary yet**Kaiyue Wen, Tengyu Ma, Percy Liang*2026*