Yamini Bansal
Yamini Bansal
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Deep double descent: Where bigger models and more data hurt
P Nakkiran, G Kaplun, Y Bansal, T Yang, B Barak, I Sutskever
Journal of Statistical Mechanics: Theory and Experiment 2021 (12), 124003, 2021
On the information bottleneck theory of deep learning
AM Saxe, Y Bansal, J Dapello, M Advani, A Kolchinsky, BD Tracey, ...
Journal of Statistical Mechanics: Theory and Experiment 2019 (12), 124020, 2019
Minnorm training: an algorithm for training over-parameterized deep neural networks
Y Bansal, M Advani, DD Cox, AM Saxe
arXiv preprint arXiv:1806.00730, 2018
For self-supervised learning, Rationality implies generalization, provably
Y Bansal, G Kaplun, B Barak
arXiv preprint arXiv:2010.08508, 2020
Distributional Generalization: A New Kind of Generalization
P Nakkiran, Y Bansal
arXiv preprint arXiv:2009.08092, 2020
Revisiting model stitching to compare neural representations
Y Bansal, P Nakkiran, B Barak
Advances in Neural Information Processing Systems 34, 225-236, 2021
Data Scaling Laws in NMT: The Effect of Noise and Architecture
Y Bansal, B Ghorbani, A Garg, B Zhang, C Cherry, B Neyshabur, O Firat
International Conference on Machine Learning, 1466-1482, 2022
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling
A Srivastava, Y Bansal, Y Ding, C Hurwitz, K Xu, B Egger, P Sattigeri, ...
arXiv preprint arXiv:2010.13187, 2020
Limitations of the NTK for Understanding Generalization in Deep Learning
N Vyas, Y Bansal, P Nakkiran
arXiv preprint arXiv:2206.10012, 2022
Building the Theoretical Foundations of Deep Learning: An Empirical Approach
Y Bansal
Harvard University, 2022
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