Tengyu MA

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Colin WeiStanford UniversityPatvirtintas el. paštas stanford.edu
Sanjeev AroraProfessor of Computer Science, Princeton UniversityPatvirtintas el. paštas princeton.edu
Percy LiangAssociate Professor of Computer Science, Stanford UniversityPatvirtintas el. paštas cs.stanford.edu
Rong GeDuke UniversityPatvirtintas el. paštas cs.duke.edu
Yingyu LiangUW-MadisonPatvirtintas el. paštas cs.wisc.edu
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityPatvirtintas el. paštas princeton.edu
Ananya KumarResearch Scientist, OpenAIPatvirtintas el. paštas cs.stanford.edu
Denny ZhouResearch Scientist, Google DeepMindPatvirtintas el. paštas google.com
Sang Michael XieMeta GenAIPatvirtintas el. paštas cs.stanford.edu
Andrej RisteskiCarnegie Mellon UniversityPatvirtintas el. paštas andrew.cmu.edu
Yuanzhi LiAssistant Professor at CMUPatvirtintas el. paštas andrew.cmu.edu
Adrien GaidonAdjunct Professor, StanfordPatvirtintas el. paštas stanford.edu
Haochen ZhangPhD Student, Stanford UniversityPatvirtintas el. paštas stanford.edu
Aditi RaghunathanAssistant professor, Carnegie Mellon UniversityPatvirtintas el. paštas cmu.edu
Yining ChenOpenAIPatvirtintas el. paštas openai.com
Zhiyuan LiAssistant Professor, Toyota Technological Institute at ChicagoPatvirtintas el. paštas ttic.edu
Kaidi CaoStanford UniversityPatvirtintas el. paštas cs.stanford.edu
Garrett ThomasStanford UniversityPatvirtintas el. paštas stanford.edu
Yuping LuoComputer Science Department, Princeton UniversityPatvirtintas el. paštas cs.princeton.edu
Ekin AkyürekPhD Candidate @ MITPatvirtintas el. paštas mit.edu

Stebėti

Tengyu MA

Stanford University

Patvirtintas el. paštas stanford.edu - Pagrindinis puslapis

Machine Learning Deep Learning Theory Machine Learning Theory


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
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	4839	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	1954	2019
A simple but tough-to-beat baseline for sentence embeddings S Arora, Y Liang, T Ma ICLR 2017, 2016	1769	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	925	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	858	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	748	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	739	2022
An explanation of in-context learning as implicit bayesian inference SM Xie, A Raghunathan, P Liang, T Ma arXiv preprint arXiv:2111.02080, 2021	726	2021
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	688	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	514	2022
Provable bounds for learning some deep representations S Arora, A Bhaskara, R Ge, T Ma International conference on machine learning, 584-592, 2014	450	2014
Identity Matters in Deep Learning M Hardt, T Ma ICLR 2017, 2016	447	2016
Verified uncertainty calibration A Kumar, PS Liang, T Ma Advances in neural information processing systems 32, 2019	419	2019
Fixup initialization: Residual learning without normalization H Zhang, YN Dauphin, T Ma arXiv preprint arXiv:1901.09321, 2019	400	2019
Gradient descent learns linear dynamical systems M Hardt, T Ma, B Recht Journal of Machine Learning Research 19 (29), 1-44, 2018	381	2018
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	380*	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	375	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	370*	2016
Larger language models do in-context learning differently J Wei, J Wei, Y Tay, D Tran, A Webson, Y Lu, X Chen, H Liu, D Huang, ... arXiv preprint arXiv:2303.03846, 2023	336	2023
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	336	2021

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