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Niladri S. Chatterji
Niladri S. Chatterji
Postdoctoral Researcher, Department of Computer Science, Stanford University
Verified email at cs.stanford.edu - Homepage
Title
Cited by
Cited by
Year
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
6792021
Underdamped Langevin MCMC: A non-asymptotic analysis
X Cheng, NS Chatterji, PL Bartlett, MI Jordan
Conference on Learning Theory 75, 300--323, 2018
2222018
Sharp convergence rates for Langevin dynamics in the nonconvex setting
X Cheng, NS Chatterji, Y Abbasi-Yadkori, PL Bartlett, MI Jordan
arXiv preprint arXiv:1805.01648, 2018
1372018
Is there an analog of Nesterov acceleration for gradient-based MCMC?
YA Ma, NS Chatterji, X Cheng, N Flammarion, PL Bartlett, MI Jordan
Bernoulli 27 (3), 1942--1992, 2021
1032021
On the theory of variance reduction for stochastic gradient Monte Carlo
NS Chatterji, N Flammarion, YA Ma, PL Bartlett, MI Jordan
International Conference on Machine Learning 80, 764--773, 2018
862018
Finite-sample analysis of interpolating linear classifiers in the overparameterized regime
NS Chatterji, PM Long
Journal of Machine Learning Reseach 22 (129), 1--30, 2021
652021
OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits
NS Chatterji, V Muthukumar, PL Bartlett
International Conference on Artificial Intelligence and Statistics 108, 1844 …, 2020
342020
The intriguing role of module criticality in the generalization of deep networks
NS Chatterji, B Neyshabur, H Sedghi
International Conference on Learning Representations, 2020
332020
Alternating minimization for dictionary learning with random initialization
NS Chatterji, PL Bartlett
Advances in Neural Information Processing Systems 30, 2017
33*2017
Enhancement of spin-transfer torque switching via resonant tunneling
NS Chatterji, AA Tulapurkar, B Muralidharan
Applied Physics Letters 105 (23), 232410, 2014
302014
Langevin Monte Carlo without smoothness
NS Chatterji, J Diakonikolas, MI Jordan, PL Bartlett
International Conference on Artificial Intelligence and Statistics 108, 1716 …, 2020
292020
Benign overfitting without linearity: Neural network classifiers trained by gradient descent for noisy linear data
S Frei, NS Chatterji, PL Bartlett
Conference on Learning Theory 178, 2668--2703, 2022
252022
Online learning with kernel losses
NS Chatterji, A Pacchiano, PL Bartlett
International Conference on Machine Learning 97, 971--980, 2019
16*2019
Holistic evaluation of language models
P Liang, R Bommasani, T Lee, D Tsipras, D Soylu, M Yasunaga, Y Zhang, ...
arXiv preprint arXiv:2211.09110, 2022
122022
On the theory of reinforcement learning with once-per-episode feedback
NS Chatterji, A Pacchiano, PL Bartlett, MI Jordan
Advances in Neural Information Processing Systems 34, 3401--3412, 2021
102021
The interplay between implicit bias and benign overfitting in two-layer linear networks
NS Chatterji, PM Long, PL Bartlett
Journal of Machine Learning Research 23 (263), 1--48, 2021
102021
When does gradient descent with logistic loss find interpolating two-layer networks?
NS Chatterji, PM Long, PL Bartlett
Journal of Machine Learning Research 22, 1--48, 2021
102021
Is importance weighting incompatible with interpolating classifiers?
KA Wang, NS Chatterji, S Haque, T Hashimoto
International Conference on Learning Representations, 2022
92022
Foolish crowds support benign overfitting
NS Chatterji, PM Long
Journal of Machine Learning Research 23 (125), 1--12, 2022
92022
Random feature amplification: Feature learning and generalization in neural networks
S Frei, NS Chatterji, PL Bartlett
arXiv preprint arXiv:2202.07626, 2022
62022
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