GEV-Canonical Regression for Accurate Binary Class Probability Estimation when One Class is Rare. - Arpit Agarwal 001; Harikrishna Narasimhan; Shivaram Kalyanakrishnan; Shivani Agarwal 001 (ICML, 2014)

On Consistent Surrogate Risk Minimization and Property Elicitation. - Arpit Agarwal 001; Shivani Agarwal 001 (COLT, 2015)

Informed Truthfulness in Multi-Task Peer Prediction. - Victor Shnayder; Arpit Agarwal 001; Rafael M. Frongillo; David C. Parkes (EC, 2016)

Learning with Limited Rounds of Adaptivity: Coin Tossing, Multi-Armed Bandits, and Ranking from Pairwise Comparisons. - Arpit Agarwal 001; Shivani Agarwal 001; Sepehr Assadi; Sanjeev Khanna (COLT, 2017)

Peer Prediction with Heterogeneous Users. - Arpit Agarwal 001; Debmalya Mandal; David C. Parkes; Nisarg Shah 001 (EC, 2017)

Accelerated Spectral Ranking. - Arpit Agarwal 001; Prathamesh Patil; Shivani Agarwal 001 (ICML, 2018)

Stochastic Submodular Cover with Limited Adaptivity. - Arpit Agarwal 001; Sepehr Assadi; Sanjeev Khanna (SODA, 2019)

Rank Aggregation from Pairwise Comparisons in the Presence of Adversarial Corruptions. - Arpit Agarwal 001; Shivani Agarwal 001; Sanjeev Khanna; Prathamesh Patil (ICML, 2020)

Choice Bandits. - Arpit Agarwal 001; Nicholas Johnson; Shivani Agarwal 001 (NeurIPS, 2020)

Stochastic Dueling Bandits with Adversarial Corruption. - Arpit Agarwal 001; Shivani Agarwal 001; Prathamesh Patil (ALT, 2021)

PySPH: A Python-based Framework for Smoothed Particle Hydrodynamics. - Prabhu Ramachandran; Aditya Bhosale; Kunal Puri; Pawan Negi; Abhinav Muta; A. Dinesh; Dileep Menon; Rahul Govind; Suraj Sanka; Amal S. Sebastian; Ananyo Sen; Rohan Kaushik; et al. (ACM Trans. Math. Softw., 2021)

PAC Top-k Identification under SST in Limited Rounds. - Arpit Agarwal 001; Sanjeev Khanna; Prathamesh Patil (AISTATS, 2022)

A Sharp Memory-Regret Trade-off for Multi-Pass Streaming Bandits. - Arpit Agarwal 001; Sanjeev Khanna; Prathamesh Patil (COLT, 2022)

Batched Dueling Bandits. - Arpit Agarwal 001; Rohan Ghuge; Viswanath Nagarajan (ICML, 2022)

An Asymptotically Optimal Batched Algorithm for the Dueling Bandit Problem. - Arpit Agarwal 001; Rohan Ghuge; Viswanath Nagarajan (NeurIPS, 2022)

Sublinear Algorithms for Hierarchical Clustering. - Arpit Agarwal 001; Sanjeev Khanna; Huan Li 002; Prathamesh Patil (NeurIPS, 2022)

Semi-Bandit Learning for Monotone Stochastic Optimization. - Arpit Agarwal 001; Rohan Ghuge; Viswanath Nagarajan (FOCS, 2024)

Learning-Augmented Dynamic Submodular Maximization. - Arpit Agarwal 001; Eric Balkanski (NeurIPS, 2024)

Parallel Approximate Maximum Flows in Near-Linear Work and Polylogarithmic Depth. - Arpit Agarwal 001; Sanjeev Khanna; Huan Li 002; Prathamesh Patil; Chen Wang 027; Nathan White; Peilin Zhong (SODA, 2024)

Misalignment, Learning, and Ranking: Harnessing Users Limited Attention. - Arpit Agarwal 001; Rad Niazadeh; Prathamesh Patil (CoRR, 2024)