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Niladri S. Chatterji
Niladri S. Chatterji
Postdoctoral Researcher, Department of Computer Science, Stanford University
Geverifieerd e-mailadres voor cs.stanford.edu - Homepage
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Underdamped Langevin MCMC: A non-asymptotic analysis
X Cheng, NS Chatterji, PL Bartlett, MI Jordan
arXiv preprint arXiv:1707.03663, 2017
1762017
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
1362021
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
1102018
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
79*2021
On the theory of variance reduction for stochastic gradient Monte Carlo
N Chatterji, N Flammarion, Y Ma, P Bartlett, M Jordan
International Conference on Machine Learning, 764-773, 2018
792018
Finite-sample analysis of interpolating linear classifiers in the overparameterized regime
NS Chatterji, M Long, Philip
arXiv preprint arXiv:2004.12019 16, 2020
462020
Osom: A simultaneously optimal algorithm for multi-armed and linear contextual bandits
N Chatterji, V Muthukumar, P Bartlett
International Conference on Artificial Intelligence and Statistics, 1844-1854, 2020
312020
Alternating minimization for dictionary learning: Local convergence guarantees
NS Chatterji, PL Bartlett
arXiv preprint arXiv:1711.03634, 2017
29*2017
Enhancement of Spin-transfer torque switching via resonant tunneling
N Chatterji, AA Tulapurkar, B Muralidharan
Applied Physics Letters 105 (23), 232410, 2014
292014
Langevin monte carlo without smoothness
N Chatterji, J Diakonikolas, MI Jordan, P Bartlett
International Conference on Artificial Intelligence and Statistics, 1716-1726, 2020
252020
The intriguing role of module criticality in the generalization of deep networks
NS Chatterji, B Neyshabur, H Sedghi
arXiv preprint arXiv:1912.00528, 2019
212019
Online learning with kernel losses
N Chatterji, A Pacchiano, P Bartlett
International Conference on Machine Learning, 971-980, 2019
13*2019
When does gradient descent with logistic loss find interpolating two-layer networks?
NS Chatterji, PM Long, PL Bartlett
Journal of Machine Learning Research 22 (159), 1-48, 2021
82021
On the theory of reinforcement learning with once-per-episode feedback
N Chatterji, A Pacchiano, P Bartlett, M Jordan
Advances in Neural Information Processing Systems 34, 2021
62021
Foolish Crowds Support Benign Overfitting
NS Chatterji, PM Long
arXiv preprint arXiv:2110.02914, 2021
42021
Oracle lower bounds for stochastic gradient sampling algorithms
NS Chatterji, PL Bartlett, PM Long
arXiv preprint arXiv:2002.00291, 2020
42020
When does gradient descent with logistic loss interpolate using deep networks with smoothed ReLU activations?
NS Chatterji, PM Long, P Bartlett
Conference on Learning Theory, 927-1027, 2021
32021
Is Importance Weighting Incompatible with Interpolating Classifiers?
KA Wang, NS Chatterji, S Haque, T Hashimoto
arXiv preprint arXiv:2112.12986, 2021
22021
The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks
NS Chatterji, PM Long, PL Bartlett
arXiv preprint arXiv:2108.11489, 2021
22021
Random Feature Amplification: Feature Learning and Generalization in Neural Networks
S Frei, NS Chatterji, PL Bartlett
arXiv preprint arXiv:2202.07626, 2022
12022
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