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ORCIDNiladri S. Chatterji
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2020 – today
- 2024
[c12]Yonatan Oren, Nicole Meister, Niladri S. Chatterji, Faisal Ladhak, Tatsunori Hashimoto:
Proving Test Set Contamination in Black-Box Language Models. ICLR 2024- 2023
- [j8]Niladri S. Chatterji, Philip M. Long:
Deep linear networks can benignly overfit when shallow ones do. J. Mach. Learn. Res. 24: 117:1-117:39 (2023) - [j7]Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett:
Random Feature Amplification: Feature Learning and Generalization in Neural Networks. J. Mach. Learn. Res. 24: 303:1-303:49 (2023) - [j6]Niladri S. Chatterji, Saminul Haque, Tatsunori Hashimoto:
Undersampling is a Minimax Optimal Robustness Intervention in Nonparametric Classification. Trans. Mach. Learn. Res. 2023 (2023) - [j5]Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel J. Orr, Lucia Zheng, Mert Yüksekgönül, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri S. Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, Yuta Koreeda:
Holistic Evaluation of Language Models. Trans. Mach. Learn. Res. 2023 (2023) - [i24]Yonatan Oren, Nicole Meister, Niladri S. Chatterji, Faisal Ladhak, Tatsunori B. Hashimoto:
Proving Test Set Contamination in Black Box Language Models. CoRR abs/2310.17623 (2023) - 2022
- [j4]Niladri S. Chatterji, Philip M. Long:
Foolish Crowds Support Benign Overfitting. J. Mach. Learn. Res. 23: 125:1-125:12 (2022) - [j3]Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett:
The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks. J. Mach. Learn. Res. 23: 263:1-263:48 (2022) - [c11]Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett:
Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data. COLT 2022: 2668-2703 - [c10]Ke Alexander Wang, Niladri Shekhar Chatterji, Saminul Haque, Tatsunori Hashimoto:
Is Importance Weighting Incompatible with Interpolating Classifiers? ICLR 2022 - [i23]Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett:
Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data. CoRR abs/2202.05928 (2022) - [i22]Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett:
Random Feature Amplification: Feature Learning and Generalization in Neural Networks. CoRR abs/2202.07626 (2022) - [i21]Niladri S. Chatterji, Saminul Haque, Tatsunori Hashimoto:
Undersampling is a Minimax Optimal Robustness Intervention in Nonparametric Classification. CoRR abs/2205.13094 (2022) - [i20]Niladri S. Chatterji, Philip M. Long:
Deep Linear Networks can Benignly Overfit when Shallow Ones Do. CoRR abs/2209.09315 (2022) - [i19]Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel J. Orr, Lucia Zheng, Mert Yüksekgönül
, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri S. Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, Yuta Koreeda:
Holistic Evaluation of Language Models. CoRR abs/2211.09110 (2022) - 2021
- [b1]Niladri S. Chatterji:
Why do Gradient Methods Work in Optimization and Sampling? University of California, Berkeley, USA, 2021 - [j2]Niladri S. Chatterji, Philip M. Long:
Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime. J. Mach. Learn. Res. 22: 129:1-129:30 (2021) - [j1]Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett:
When Does Gradient Descent with Logistic Loss Find Interpolating Two-Layer Networks? J. Mach. Learn. Res. 22: 159:1-159:48 (2021) - [c9]Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett:
When does gradient descent with logistic loss interpolate using deep networks with smoothed ReLU activations? COLT 2021: 927-1027 - [c8]Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
On the Theory of Reinforcement Learning with Once-per-Episode Feedback. NeurIPS 2021: 3401-3412 - [i18]Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett:
When does gradient descent with logistic loss interpolate using deep networks with smoothed ReLU activations? CoRR abs/2102.04998 (2021) - [i17]Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
On the Theory of Reinforcement Learning with Once-per-Episode Feedback. CoRR abs/2105.14363 (2021) - [i16]Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ B. Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri S. Chatterji, Annie S. Chen, Kathleen Creel, Jared Quincy Davis, Dorottya Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren E. Gillespie, Karan Goel, Noah D. Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark S. Krass, Ranjay Krishna, Rohith Kuditipudi, et al.:
On the Opportunities and Risks of Foundation Models. CoRR abs/2108.07258 (2021) - [i15]Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett:
The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks. CoRR abs/2108.11489 (2021) - [i14]Niladri S. Chatterji, Philip M. Long:
Foolish Crowds Support Benign Overfitting. CoRR abs/2110.02914 (2021) - [i13]Ke Alexander Wang, Niladri S. Chatterji, Saminul Haque, Tatsunori Hashimoto:
Is Importance Weighting Incompatible with Interpolating Classifiers? CoRR abs/2112.12986 (2021) - 2020
- [c7]Niladri S. Chatterji, Jelena Diakonikolas, Michael I. Jordan, Peter L. Bartlett:
Langevin Monte Carlo without smoothness. AISTATS 2020: 1716-1726 - [c6]Niladri S. Chatterji, Vidya Muthukumar, Peter L. Bartlett:
OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits. AISTATS 2020: 1844-1854 - [c5]Niladri S. Chatterji, Behnam Neyshabur, Hanie Sedghi:
The intriguing role of module criticality in the generalization of deep networks. ICLR 2020 - [i12]Niladri S. Chatterji, Peter L. Bartlett, Philip M. Long:
Oracle lower bounds for stochastic gradient sampling algorithms. CoRR abs/2002.00291 (2020) - [i11]Niladri S. Chatterji, Philip M. Long:
Finite-sample analysis of interpolating linear classifiers in the overparameterized regime. CoRR abs/2004.12019 (2020) - [i10]Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett:
When does gradient descent with logistic loss find interpolating two-layer networks? CoRR abs/2012.02409 (2020)
2010 – 2019
- 2019
- [c4]Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett:
Online learning with kernel losses. ICML 2019: 971-980 - [i9]Yi-An Ma, Niladri S. Chatterji, Xiang Cheng, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan:
Is There an Analog of Nesterov Acceleration for MCMC? CoRR abs/1902.00996 (2019) - [i8]Niladri S. Chatterji, Vidya Muthukumar, Peter L. Bartlett:
OSOM: A Simultaneously Optimal Algorithm for Multi-Armed and Linear Contextual Bandits. CoRR abs/1905.10040 (2019) - [i7]Niladri S. Chatterji, Jelena Diakonikolas, Michael I. Jordan, Peter L. Bartlett:
Langevin Monte Carlo without Smoothness. CoRR abs/1905.13285 (2019) - [i6]Niladri S. Chatterji, Behnam Neyshabur, Hanie Sedghi:
The intriguing role of module criticality in the generalization of deep networks. CoRR abs/1912.00528 (2019) - 2018
- [c3]Xiang Cheng, Niladri S. Chatterji, Peter L. Bartlett, Michael I. Jordan:
Underdamped Langevin MCMC: A non-asymptotic analysis. COLT 2018: 300-323 - [c2]Niladri S. Chatterji, Nicolas Flammarion, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan:
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo. ICML 2018: 763-772 - [i5]Niladri S. Chatterji, Nicolas Flammarion, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan:
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo. CoRR abs/1802.05431 (2018) - [i4]Aldo Pacchiano, Niladri S. Chatterji, Peter L. Bartlett:
Online learning with kernel losses. CoRR abs/1802.09732 (2018) - [i3]Xiang Cheng, Niladri S. Chatterji, Yasin Abbasi-Yadkori, Peter L. Bartlett, Michael I. Jordan:
Sharp Convergence Rates for Langevin Dynamics in the Nonconvex Setting. CoRR abs/1805.01648 (2018) - 2017
- [c1]Niladri S. Chatterji, Peter L. Bartlett:
Alternating minimization for dictionary learning with random initialization. NIPS 2017: 1997-2006 - [i2]Xiang Cheng, Niladri S. Chatterji, Peter L. Bartlett, Michael I. Jordan:
Underdamped Langevin MCMC: A non-asymptotic analysis. CoRR abs/1707.03663 (2017) - [i1]Niladri S. Chatterji, Peter L. Bartlett:
Alternating minimization for dictionary learning with random initialization. CoRR abs/1711.03634 (2017)
Coauthor Index
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