Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices. - Don Kurian Dennis; Chirag Pabbaraju; Harsha Vardhan Simhadri; Prateek Jain 002 (NeurIPS, 2018)

GesturePod: Enabling On-device Gesture-based Interaction for White Cane Users. - Shishir G. Patil; Don Kurian Dennis; Chirag Pabbaraju; Nadeem Shaheer; Harsha Vardhan Simhadri; Vivek Seshadri; Manik Varma; Prateek Jain 002 (UIST, 2019)

Learning Functions over Sets via Permutation Adversarial Networks. - Chirag Pabbaraju; Prateek Jain 002 (CoRR, 2019)

Efficient semidefinite-programming-based inference for binary and multi-class MRFs. - Chirag Pabbaraju; Po-Wei Wang; J. Zico Kolter (NeurIPS, 2020)

Estimating Lipschitz constants of monotone deep equilibrium models. - Chirag Pabbaraju; Ezra Winston; J. Zico Kolter (ICLR, 2021)

Universal Approximation Using Well-Conditioned Normalizing Flows. - Holden Lee; Chirag Pabbaraju; Anish Prasad Sevekari; Andrej Risteski (NeurIPS, 2021)

Universal Approximation for Log-concave Distributions using Well-conditioned Normalizing Flows. - Holden Lee; Chirag Pabbaraju; Anish Prasad Sevekari; Andrej Risteski (CoRR, 2021)

Pitfalls of Gaussians as a noise distribution in NCE. - Holden Lee; Chirag Pabbaraju; Anish Prasad Sevekari; Andrej Risteski (ICLR, 2023)

Harnessing the power of choices in decision tree learning. - Guy Blanc; Jane Lange; Chirag Pabbaraju; Colin Sullivan; Li-Yang Tan; Mo Tiwari (NeurIPS, 2023)

Provable benefits of score matching. - Chirag Pabbaraju; Dhruv Rohatgi; Anish Prasad Sevekari; Holden Lee; Ankur Moitra; Andrej Risteski (NeurIPS, 2023)

A Characterization of List Learnability. - Moses Charikar; Chirag Pabbaraju (STOC, 2023)

Testing with Non-identically Distributed Samples. - Shivam Garg 001; Chirag Pabbaraju; Kirankumar Shiragur; Gregory Valiant (CoRR, 2023)

Average-Case Dimensionality Reduction in 𝓁1: Tree Ising Models. - Moses Charikar; Spencer Compton; Chirag Pabbaraju (CoRR, 2023)

Multiclass Learnability Does Not Imply Sample Compression. - Chirag Pabbaraju (ALT, 2024)

Quantifying the Gain in Weak-to-Strong Generalization. - Moses Charikar; Chirag Pabbaraju; Kirankumar Shiragur (NeurIPS, 2024)

Credit Attribution and Stable Compression. - Roi Livni; Shay Moran; Kobbi Nissim; Chirag Pabbaraju (NeurIPS, 2024)

A Characterization of List Regression. - Chirag Pabbaraju; Sahasrajit Sarmasarkar (ALT, 2025)

Proofs as Explanations: Short Certificates for Reliable Predictions. - Avrim Blum; Steve Hanneke; Chirag Pabbaraju; Donya Saless (COLT, 2025)

Exploring Facets of Language Generation in the Limit. - Moses Charikar; Chirag Pabbaraju (COLT, 2025)

Lower Bounds for Greedy Teaching Set Constructions. - Spencer Compton; Chirag Pabbaraju; Nikita Zhivotovskiy (COLT, 2025)

New and Improved Bounds for Markov Paging. - Chirag Pabbaraju; Ali Vakilian (ICALP, 2025)

Embedding Probability Distributions into Low Dimensional ℓ1: Tree Ising Models via Truncated Metrics. - Moses Charikar; Spencer Compton; Chirag Pabbaraju (SODA, 2025)

Relating Misfit to Gain in Weak-to-Strong Generalization Beyond the Squared Loss. - Abhijeet Mulgund; Chirag Pabbaraju (CoRR, 2025)