Ultra Fast Medoid Identification via Correlated Sequential Halving. - Tavor Z. Baharav; David Tse (NeurIPS, 2019)

My Fair Bandit: Distributed Learning of Max-Min Fairness with Multi-player Bandits. - Ilai Bistritz; Tavor Z. Baharav; Amir Leshem; Nicholas Bambos (ICML, 2020)

Adaptive Learning of Rank-One Models for Efficient Pairwise Sequence Alignment. - Govinda M. Kamath; Tavor Z. Baharav; Ilan Shomorony (NeurIPS, 2020)

Spectral Jaccard Similarity: A New Approach to Estimating Pairwise Sequence Alignments. - Tavor Z. Baharav; Govinda M. Kamath; David N. C. Tse; Ilan Shomorony (Patterns, 2020)

Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification. - Tavor Z. Baharav; Daniel L. Jiang; Kedarnath Kolluri; Sujay Sanghavi; Inderjit S. Dhillon (CIKM, 2021)

Bandit-Based Monte Carlo Optimization for Nearest Neighbors. - Vivek Kumar Bagaria; Tavor Z. Baharav; Govinda M. Kamath; David N. C. Tse (IEEE J. Sel. Areas Inf. Theory, 2021)

One for All and All for One: Distributed Learning of Fair Allocations With Multi-Player Bandits. - Ilai Bistritz; Tavor Z. Baharav; Amir Leshem; Nicholas Bambos (IEEE J. Sel. Areas Inf. Theory, 2021)

Approximate Function Evaluation via Multi-Armed Bandits. - Tavor Z. Baharav; Gary Cheng 004; Mert Pilanci; David Tse (AISTATS, 2022)

Beyond the Best: Distribution Functional Estimation in Infinite-Armed Bandits. - Yifei Wang 005; Tavor Z. Baharav; Yanjun Han; Jiantao Jiao; David Tse (NeurIPS, 2022)

Beyond the Best: Estimating Distribution Functionals in Infinite-Armed Bandits. - Yifei Wang 005; Tavor Z. Baharav; Yanjun Han; Jiantao Jiao; David Tse (CoRR, 2022)

Adaptive Data Depth via Multi-Armed Bandits. - Tavor Z. Baharav; Tze Leung Lai (J. Mach. Learn. Res., 2023)

Adaptive Sampling for Efficient Softmax Approximation. - Tavor Z. Baharav; Ryan Kang; Colin Sullivan; Mo Tiwari; Eric Luxenberg; David Tse; Mert Pilanci (NeurIPS, 2024)

Stacked SVD or SVD stacked? A Random Matrix Theory perspective on data integration. - Tavor Z. Baharav; Phillip B. Nicol; Rafael A. Irizarry; Rong Ma (CoRR, 2025)