Tengyu MA

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Sanjeev AroraProfessor of Computer Science, Princeton UniversityVerified email at cs.princeton.edu
Colin WeiStanford UniversityVerified email at stanford.edu
Rong GeDuke UniversityVerified email at cs.duke.edu
Yingyu LiangUniversity of Wisconsin-MadisonVerified email at cs.wisc.edu
Yuanzhi LiAssistant Professor at CMUVerified email at andrew.cmu.edu
Percy LiangAssociate Professor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityVerified email at princeton.edu
Andrej RisteskiCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Ananya KumarResearch Scientist, OpenAIVerified email at cs.stanford.edu
Adrien GaidonHead of Machine Learning Research, Toyota Research Institute (TRI)Verified email at tri.global
Haochen ZhangPhD Student, Stanford UniversityVerified email at stanford.edu
Sang Michael XiePhD Candidate, Stanford UniversityVerified email at cs.stanford.edu
Yining ChenOpenAIVerified email at openai.com
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
Elad HazanProfessor at Princeton University and Director Google AI PrincetonVerified email at princeton.edu
Aditi RaghunathanAssistant professor, Carnegie Mellon UniversityVerified email at cmu.edu
Nikos ArechigaToyota Research InstituteVerified email at tri.global
Stefano ErmonStanford UniversityVerified email at cs.stanford.edu

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	1920	2021
A simple but tough-to-beat baseline for sentence embeddings S Arora, Y Liang, T Ma ICLR 2017, 2016	1500	2016
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	1217	2019
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	747	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	679	2016
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	598*	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	567	2020
Provable bounds for learning some deep representations S Arora, A Bhaskara, R Ge, T Ma International conference on machine learning, 584-592, 2014	438	2014
Identity Matters in Deep Learning M Hardt, T Ma ICLR 2017, 2016	394	2016
Finding Approximate Local Minima for Nonconvex Optimization in Linear Time N Agarwal, Z Allen-Zhu, B Bullins, E Hazan, T Ma STOC 2017, 2016	331*	2016
Fixup initialization: Residual learning without normalization H Zhang, YN Dauphin, T Ma arXiv preprint arXiv:1901.09321, 2019	325	2019
Gradient descent learns linear dynamical systems M Hardt, T Ma, B Recht The Journal of Machine Learning Research 19 (1), 1025-1068, 2018	324	2018
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	301	2022
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	296*	2017
Verified uncertainty calibration A Kumar, PS Liang, T Ma Advances in Neural Information Processing Systems 32, 2019	284	2019
Learning one-hidden-layer neural networks with landscape design R Ge, JD Lee, T Ma ICLR 2017; arXiv preprint arXiv:1711.00501, 2017	284	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	266	2019
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel TM Colin Wei, Jason D. Lee, Qiang Liu arXiv preprint arXiv:1810.05369, 2019	252*	2019
An explanation of in-context learning as implicit bayesian inference SM Xie, A Raghunathan, P Liang, T Ma arXiv preprint arXiv:2111.02080, 2021	246	2021
Linear algebraic structure of word senses, with applications to polysemy S Arora, Y Li, Y Liang, T Ma, A Risteski arXiv preprint arXiv:1601.03764, 2016	235	2016

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