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Niladri Chatterji
I am currently on the Llama team at Meta Gen AI.
Previously, I was a postdoctoral researcher at Stanford University working with Tatsu Hashimoto and Percy Liang. Before that I completed my PhD at UC Berkeley advised by Peter Bartlett, and graduated from IIT Bombay in 2015.
My research interests lie at the intersection of Machine Learning and Statistics. My current research interests center around building more robust language models. In the past, I have worked on interpolating models, optimization theory, online learning, and MCMC algorithms.
Education
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PhD at UC Berkeley, 2021
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BTech and MTech at IIT Bombay, 2015
Publications
Deep linear networks can benignly overfit when shallow ones do
Journal of Machine Learning Research
Undersampling is a minimax optimal robustness intervention in nonparametric classification
Transactions of Machine Learning Research
Random feature amplification: Feature learning and generalization in neural networks
arXiv pre-print
Is importance weighting incompatible with interpolating classifiers?
ICLR 2022; also presented as a spotlight talk in the Workshop on Distribution Shifts, NeurIPS 2021
Foolish crowds support benign overfitting
Journal of Machine Learning Research; also presented at NeurIPS 2022
The interplay between implicit bias and benign overfitting in two-layer linear networks
Journal of Machine Learning Research
On the theory of reinforcement learning with once-per-episode feedback
NeurIPS 2021; also presented as an oral talk in the Workshop on Reinforcement Learning Theory, ICML 2021
When does gradient descent with logistic loss find interpolating two-layer networks?
Journal of Machine Learning Research
Finite-sample analysis of interpolating linear classifiers in the overparameterized regime
Journal of Machine Learning Research
The intriguing role of module criticality in the generalization of deep networks
ICLR 2020 (Spotlight Talk); also appeared at Workshops on ML with Guarantees & on Science of Deep Learning, NeurIPS 2019
Langevin Monte Carlo without smoothness
AISTATS 2020
Online learning with kernel losses
ICML 2019 (Long Talk)