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
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityVerified email at princeton.edu
Percy LiangAssociate Professor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
Andrej RisteskiCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Ananya KumarPhD Student, Stanford UniversityVerified email at cs.stanford.edu
Adrien GaidonHead of Machine Learning Research, Toyota Research Institute (TRI)Verified email at tri.global
Jeff Z. HaoChenPhD Student, Stanford UniversityVerified email at stanford.edu
Yining ChenStanford UniversityVerified email at stanford.edu
Elad HazanProfessor at Princeton University and Director Google AI PrincetonVerified email at princeton.edu
Kaidi CaoStanford UniversityVerified email at cs.stanford.edu
Sang Michael XiePhD Candidate, Stanford UniversityVerified email at cs.stanford.edu
Garrett ThomasStanford UniversityVerified email at stanford.edu
Yuping LuoComputer Science Department, Princeton UniversityVerified email at cs.princeton.edu
Yann N. DauphinGoogle AIVerified email at dauphin.io
Aditya BhaskaraUniversity of UtahVerified email at cs.utah.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
A simple but tough-to-beat baseline for sentence embeddings S Arora, Y Liang, T Ma ICLR 2017, 2016	1352	2016
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	821	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	819	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	659	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	638	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	460*	2016
Provable bounds for learning some deep representations S Arora, A Bhaskara, R Ge, T Ma International conference on machine learning, 584-592, 2014	425	2014
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	405	2020
Identity Matters in Deep Learning M Hardt, T Ma ICLR 2017, 2016	374	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	309*	2016
Gradient descent learns linear dynamical systems M Hardt, T Ma, B Recht arXiv preprint arXiv:1609.05191, 2016	284	2016
Fixup initialization: Residual learning without normalization H Zhang, YN Dauphin, T Ma arXiv preprint arXiv:1901.09321, 2019	265	2019
Learning one-hidden-layer neural networks with landscape design R Ge, JD Lee, T Ma ICLR 2017; arXiv preprint arXiv:1711.00501, 2017	254	2017
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	248*	2017
Verified uncertainty calibration A Kumar, PS Liang, T Ma Advances in Neural Information Processing Systems 32, 2019	222	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	219*	2019
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	209	2019
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	206	2016
Simple, efficient, and neural algorithms for sparse coding S Arora, R Ge, T Ma, A Moitra Conference on Learning Theory (COLT) 2015. arXiv preprint arXiv:1503.00778, 2015	201	2015
Algorithmic framework for model-based deep reinforcement learning with theoretical guarantees Y Luo, H Xu, Y Li, Y Tian, T Darrell, T Ma arXiv preprint arXiv:1807.03858, 2018	179	2018

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