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Niladri S. Chatterji
Niladri S. Chatterji
Postdoctoral Researcher, Department of Computer Science, Stanford University
Zweryfikowany adres z cs.stanford.edu - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
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
48952021
The llama 3 herd of models
A Dubey, A Jauhri, A Pandey, A Kadian, A Al-Dahle, A Letman, A Mathur, ...
arXiv preprint arXiv:2407.21783, 2024
27942024
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
12052022
Underdamped Langevin MCMC: A non-asymptotic analysis
X Cheng, NS Chatterji, PL Bartlett, MI Jordan
Conference on Learning Theory 75, 300--323, 2018
3682018
Convergence rates for Langevin Monte Carlo in the nonconvex setting
X Cheng, NS Chatterji, Y Abbasi-Yadkori, PL Bartlett, MI Jordan
arXiv preprint arXiv:1805.01648, 2018
188*2018
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
1772021
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
1442021
& Liang, P.(2021). 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, 0
121
Proving test set contamination in black-box language models
Y Oren, N Meister, NS Chatterji, F Ladhak, T Hashimoto
The Twelfth International Conference on Learning Representations, 2023
1172023
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
1082018
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
1042022
The llama 3 herd of models
A Grattafiori, A Dubey, A Jauhri, A Pandey, A Kadian, A Al-Dahle, ...
arXiv e-prints, arXiv: 2407.21783, 2024
912024
On the opportunities and risks of foundation models (arXiv: 2108.07258). arXiv
R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S Von Arx, ...
802022
The intriguing role of module criticality in the generalization of deep networks
NS Chatterji, B Neyshabur, H Sedghi
International Conference on Learning Representations, 2020
692020
Langevin Monte Carlo without smoothness
NS Chatterji, J Diakonikolas, MI Jordan, PL Bartlett
International Conference on Artificial Intelligence and Statistics 108, 1716 …, 2020
532020
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
452020
Alternating minimization for dictionary learning with random initialization
NS Chatterji, PL Bartlett
Advances in Neural Information Processing Systems 30, 2017
43*2017
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
382021
Random feature amplification: Feature learning and generalization in neural networks
S Frei, NS Chatterji, PL Bartlett
arXiv preprint arXiv:2202.07626, 2022
322022
Enhancement of spin-transfer torque switching via resonant tunneling
NS Chatterji, AA Tulapurkar, B Muralidharan
Applied Physics Letters 105 (23), 232410, 2014
322014
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