Tengyu MA

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Colin WeiStanford UniversityVerified email at stanford.edu
Sanjeev AroraProfessor of Computer Science, Princeton UniversityVerified email at cs.princeton.edu
Rong GeDuke UniversityVerified email at cs.duke.edu
Percy LiangAssociate Professor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
Yingyu LiangUW-MadisonVerified email at cs.wisc.edu
Yuanzhi LiAssistant Professor at CMUVerified email at andrew.cmu.edu
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityVerified email at princeton.edu
Ananya KumarResearch Scientist, OpenAIVerified email at cs.stanford.edu
Denny ZhouResearch Scientist, Google DeepMindVerified email at google.com
Sang Michael XieMeta GenAIVerified email at cs.stanford.edu
Andrej RisteskiCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Adrien GaidonAdjunct Professor, StanfordVerified email at stanford.edu
Haochen ZhangPhD Student, Stanford UniversityVerified email at stanford.edu
Aditi RaghunathanAssistant professor, Carnegie Mellon UniversityVerified email at cmu.edu
Yining ChenOpenAIVerified email at openai.com
Zhiyuan LiAssistant Professor, Toyota Technological Institute at ChicagoVerified email at ttic.edu
Kaidi CaoStanford UniversityVerified email at cs.stanford.edu
Garrett ThomasStanford UniversityVerified email at stanford.edu
Yuping LuoComputer Science Department, Princeton UniversityVerified email at cs.princeton.edu
Nikos ArechigaToyota Research InstituteVerified email at tri.global

Tengyu MA

Stanford University

Verified email at stanford.edu - Homepage

Machine Learning Deep Learning Theory Machine Learning Theory


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
On the opportunities and risks of foundation models R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2021	4534	2021
Learning imbalanced datasets with label-distribution-aware margin loss K Cao, C Wei, A Gaidon, N Arechiga, T Ma NeurIPS 2019; arXiv preprint arXiv:1906.07413, 2019	1860	2019
A simple but tough-to-beat baseline for sentence embeddings S Arora, Y Liang, T Ma ICLR 2017, 2016	1744	2016
Mopo: Model-based offline policy optimization T Yu, G Thomas, L Yu, S Ermon, JY Zou, S Levine, C Finn, T Ma Advances in Neural Information Processing Systems 33, 14129-14142, 2020	889	2020
Generalization and Equilibrium in Generative Adversarial Nets (GANs) S Arora, R Ge, Y Liang, T Ma, Y Zhang ICML 2017;arXiv preprint arXiv:1703.00573, 2017, 2017	843	2017
Matrix Completion has No Spurious Local Minimum R Ge, JD Lee, T Ma NIPS 2016 (best student paper). arXiv preprint arXiv:1605.07272, 2016	732	2016
Fine-tuning can distort pretrained features and underperform out-of-distribution A Kumar, A Raghunathan, R Jones, T Ma, P Liang arXiv preprint arXiv:2202.10054, 2022	703	2022
A latent variable model approach to pmi-based word embeddings S Arora, Y Li, Y Liang, T Ma, A Risteski Transactions of the Association for Computational Linguistics 4, 385-399, 2016	678	2016
An explanation of in-context learning as implicit bayesian inference SM Xie, A Raghunathan, P Liang, T Ma arXiv preprint arXiv:2111.02080, 2021	664	2021
Provable bounds for learning some deep representations S Arora, A Bhaskara, R Ge, T Ma International conference on machine learning, 584-592, 2014	456	2014
Identity Matters in Deep Learning M Hardt, T Ma ICLR 2017, 2016	448	2016
What learning algorithm is in-context learning? investigations with linear models E Akyürek, D Schuurmans, J Andreas, T Ma, D Zhou arXiv preprint arXiv:2211.15661, 2022	444	2022
Verified uncertainty calibration A Kumar, PS Liang, T Ma Advances in Neural Information Processing Systems 32, 2019	400	2019
Fixup initialization: Residual learning without normalization H Zhang, YN Dauphin, T Ma arXiv preprint arXiv:1901.09321, 2019	391	2019
Algorithmic Regularization in Over-parameterized Matrix Recovery and Neural Networks with Quadratic Activations Y Li, T Ma, H Zhang COLT 2018 (best paper); arXiv preprint arXiv:1712.09203, 2017	373*	2017
Towards explaining the regularization effect of initial large learning rate in training neural networks Y Li, C Wei, T Ma Advances in neural information processing systems 32, 2019	367	2019
Finding Approximate Local Minima for Nonconvex Optimization in Linear Time N Agarwal, Z Allen-Zhu, B Bullins, E Hazan, T Ma STOC 2017, 2016	363*	2016
Gradient descent learns linear dynamical systems M Hardt, T Ma, B Recht Journal of Machine Learning Research 19 (29), 1-44, 2018	362	2018
Provable guarantees for self-supervised deep learning with spectral contrastive loss JZ HaoChen, C Wei, A Gaidon, T Ma Advances in Neural Information Processing Systems 34, 5000-5011, 2021	322	2021
Learning one-hidden-layer neural networks with landscape design R Ge, JD Lee, T Ma ICLR 2017; arXiv preprint arXiv:1711.00501, 2017	309	2017

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