Urmăriți
Tengyu MA
Titlu
Citat de
Citat de
Anul
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
48392021
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
19542019
A simple but tough-to-beat baseline for sentence embeddings
S Arora, Y Liang, T Ma
ICLR 2017, 2016
17692016
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
9252020
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
8582017
Matrix Completion has No Spurious Local Minimum
R Ge, JD Lee, T Ma
NIPS 2016 (best student paper). arXiv preprint arXiv:1605.07272, 2016
7482016
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
7392022
An explanation of in-context learning as implicit bayesian inference
SM Xie, A Raghunathan, P Liang, T Ma
arXiv preprint arXiv:2111.02080, 2021
7262021
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
6882016
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
5142022
Provable bounds for learning some deep representations
S Arora, A Bhaskara, R Ge, T Ma
International conference on machine learning, 584-592, 2014
4502014
Identity Matters in Deep Learning
M Hardt, T Ma
ICLR 2017, 2016
4472016
Verified uncertainty calibration
A Kumar, PS Liang, T Ma
Advances in neural information processing systems 32, 2019
4192019
Fixup initialization: Residual learning without normalization
H Zhang, YN Dauphin, T Ma
arXiv preprint arXiv:1901.09321, 2019
4002019
Gradient descent learns linear dynamical systems
M Hardt, T Ma, B Recht
Journal of Machine Learning Research 19 (29), 1-44, 2018
3812018
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
3752019
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
3362023
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
3362021
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