Tengyu Ma
Assistant Professor of Computer Science
Studies deep learning theory, optimization and pre-training of foundation models.
Tengyu Ma's lab at Stanford works on learning theory & optimization, generative & foundation models and large language models. 23 papers since 2023, 23 at top venues, mostly ICLR and NeurIPS. Applications close .
At a glance
- papers, 2023–2026
- 6/ year
- at T1 venues
- 100%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom DBLP, 2023–2026
- 1 papers2026 · T1 1 · collecting
- 3 papers2025 · T1 3
- 5 papers2024 · T1 5
- 14 papers2023 · T1 14
T1 venuesOtherVenues: ICLR, NeurIPS, ICML, COLT, EMNLP
Research focuspaper titles that use each topic's words
- Large language models11 papers
- Generative & foundation models7 papers
- Learning theory & optimization4 papers
- studies deep0 papers
- foundation models0 papers
Paper list
From DBLP.
20261 paper
20253 papers
20245 papers
- ICLR (T1)Chain of Thought Empowers Transformers to Solve Inherently Serial Problems (opens in a new tab)
- ICLR (T1)Large Language Models as Tool Makers (opens in a new tab)
- ICML (T1)Linguistic Calibration of Long-Form Generations (opens in a new tab)
- ICLR (T1)One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention (opens in a new tab)
- ICLR (T1)Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training (opens in a new tab)
202314 papers
- ICLR (T1)A theoretical study of inductive biases in contrastive learning (opens in a new tab)
- ICLR (T1)Asymptotic Instance-Optimal Algorithms for Interactive Decision Making (opens in a new tab)
- NeurIPS (T1)Beyond NTK with Vanilla Gradient Descent: A Mean-Field Analysis of Neural Networks with Polynomial Width, Samples, and Time (opens in a new tab)
- NeurIPS (T1)Data Selection for Language Models via Importance Resampling (opens in a new tab)
- NeurIPS (T1)DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining (opens in a new tab)
- ICLR (T1)First Steps Toward Understanding the Extrapolation of Nonlinear Models to Unseen Domains (opens in a new tab)
- ICLR (T1)How Sharpness-Aware Minimization Minimizes Sharpness? (opens in a new tab)
- ICLR (T1)Max-Margin Works while Large Margin Fails: Generalization without Uniform Convergence (opens in a new tab)
- ICML (T1)Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models (opens in a new tab)
- NeurIPS (T1)Sharpness Minimization Algorithms Do Not Only Minimize Sharpness To Achieve Better Generalization (opens in a new tab)
- EMNLP (T1)Symbol tuning improves in-context learning in language models (opens in a new tab)
- COLT (T1)Toward L_∞Recovery of Nonlinear Functions: A Polynomial Sample Complexity Bound for Gaussian Random Fields (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to StanfordFall 2027
Stanford Computer Science PhD
- Round
- Single round
- Opens
- GRE
- See program page
- English
- TOEFL/IELTS
- Fee
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Confirmed on the program page.
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Data
Some numbers could not be verified:
- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
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