Alternating minimization for dictionary learning with random initialization. - Niladri S. Chatterji; Peter L. Bartlett (NIPS, 2017)

Underdamped Langevin MCMC: A non-asymptotic analysis. - Xiang Cheng 006; Niladri S. Chatterji; Peter L. Bartlett; Michael I. Jordan (COLT, 2018)

On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo. - Niladri S. Chatterji; Nicolas Flammarion; Yi-An Ma; Peter L. Bartlett; Michael I. Jordan (ICML, 2018)

Sharp Convergence Rates for Langevin Dynamics in the Nonconvex Setting. - Xiang Cheng 006; Niladri S. Chatterji; Yasin Abbasi-Yadkori; Peter L. Bartlett; Michael I. Jordan (CoRR, 2018)

Online learning with kernel losses. - Niladri S. Chatterji; Aldo Pacchiano; Peter L. Bartlett (ICML, 2019)

Is There an Analog of Nesterov Acceleration for MCMC? - Yi-An Ma; Niladri S. Chatterji; Xiang Cheng 006; Nicolas Flammarion; Peter L. Bartlett; Michael I. Jordan (CoRR, 2019)

Langevin Monte Carlo without smoothness. - Niladri S. Chatterji; Jelena Diakonikolas; Michael I. Jordan; Peter L. Bartlett (AISTATS, 2020)

OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits. - Niladri S. Chatterji; Vidya Muthukumar; Peter L. Bartlett (AISTATS, 2020)

The intriguing role of module criticality in the generalization of deep networks. - Niladri S. Chatterji; Behnam Neyshabur; Hanie Sedghi (ICLR, 2020)

Oracle lower bounds for stochastic gradient sampling algorithms. - Niladri S. Chatterji; Peter L. Bartlett; Philip M. Long (CoRR, 2020)

When does gradient descent with logistic loss interpolate using deep networks with smoothed ReLU activations? - Niladri S. Chatterji; Philip M. Long; Peter L. Bartlett (COLT, 2021)

On the Theory of Reinforcement Learning with Once-per-Episode Feedback. - Niladri S. Chatterji; Aldo Pacchiano; Peter L. Bartlett; Michael I. Jordan (NeurIPS, 2021)

On the Opportunities and Risks of Foundation Models. - Rishi Bommasani; Drew A. Hudson; Ehsan Adeli 001; Russ B. Altman; Simran Arora; Sydney von Arx; Michael S. Bernstein; Jeannette Bohg; Antoine Bosselut; Emma Brunskill; Erik Brynjolfsson; Shyamal Buch; et al. (CoRR, 2021)

Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime. - Niladri S. Chatterji; Philip M. Long (J. Mach. Learn. Res., 2021)

When Does Gradient Descent with Logistic Loss Find Interpolating Two-Layer Networks? - Niladri S. Chatterji; Philip M. Long; Peter L. Bartlett (J. Mach. Learn. Res., 2021)

Why do Gradient Methods Work in Optimization and Sampling? - Niladri S. Chatterji (2021)

Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data. - Spencer Frei; Niladri S. Chatterji; Peter L. Bartlett (COLT, 2022)

Is Importance Weighting Incompatible with Interpolating Classifiers? - Ke Alexander Wang; Niladri Shekhar Chatterji; Saminul Haque; Tatsunori Hashimoto (ICLR, 2022)

Foolish Crowds Support Benign Overfitting. - Niladri S. Chatterji; Philip M. Long (J. Mach. Learn. Res., 2022)

The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks. - Niladri S. Chatterji; Philip M. Long; Peter L. Bartlett (J. Mach. Learn. Res., 2022)

Deep linear networks can benignly overfit when shallow ones do. - Niladri S. Chatterji; Philip M. Long (J. Mach. Learn. Res., 2023)

Random Feature Amplification: Feature Learning and Generalization in Neural Networks. - Spencer Frei; Niladri S. Chatterji; Peter L. Bartlett (J. Mach. Learn. Res., 2023)

Undersampling is a Minimax Optimal Robustness Intervention in Nonparametric Classification. - Niladri S. Chatterji; Saminul Haque; Tatsunori Hashimoto (Trans. Mach. Learn. Res., 2023)

Holistic Evaluation of Language Models. - Percy Liang; Rishi Bommasani; Tony Lee; Dimitris Tsipras; Dilara Soylu; Michihiro Yasunaga; Yian Zhang; Deepak Narayanan; Yuhuai Wu; Ananya Kumar; Benjamin Newman; Binhang Yuan; et al. (Trans. Mach. Learn. Res., 2023)

Proving Test Set Contamination in Black-Box Language Models. - Yonatan Oren; Nicole Meister; Niladri S. Chatterji; Faisal Ladhak; Tatsunori Hashimoto (ICLR, 2024)

Compute Optimal Scaling of Skills: Knowledge vs Reasoning. - Nicholas Roberts; Niladri S. Chatterji; Sharan Narang; Mike Lewis; Dieuwke Hupkes (ACL (Findings), 2025)

Correlating and Predicting Human Evaluations of Language Models from Natural Language Processing Benchmarks. - Rylan Schaeffer; Punit Singh Koura; Binh Tang; Ranjan Subramanian; Aaditya K. Singh; Todor Mihaylov; Prajjwal Bhargava; Lovish Madaan; Niladri S. Chatterji; Vedanuj Goswami; Sergey Edunov; Dieuwke Hupkes; et al. (CoRR, 2025)