Bayesian function optimization with adaptive discretization. - Shubhanshu Shekhar; Tara Javidi (Allerton, 2017)

Gaussian Process bandits with adaptive discretization. - Shubhanshu Shekhar; Tara Javidi (CoRR, 2017)

Species Tree Estimation Using ASTRAL: How Many Genes Are Enough? - Shubhanshu Shekhar; Sebastien Roch; Siavash Mirarab (IEEE ACM Trans. Comput. Biol. Bioinform., 2018)

Multiscale Gaussian Process Level Set Estimation. - Shubhanshu Shekhar; Tara Javidi (AISTATS, 2019)

Binary Classification with Bounded Abstention Rate. - Shubhanshu Shekhar; Mohammad Ghavamzadeh; Tara Javidi (CoRR, 2019)

Active Learning for Binary Classification with Abstention. - Shubhanshu Shekhar; Mohammad Ghavamzadeh; Tara Javidi (CoRR, 2019)

Adaptive Sampling for Estimating Multiple Probability Distributions. - Shubhanshu Shekhar; Mohammad Ghavamzadeh; Tara Javidi (CoRR, 2019)

Adaptive Sampling for Estimating Probability Distributions. - Shubhanshu Shekhar; Tara Javidi; Mohammad Ghavamzadeh (ICML, 2020)

Active Model Estimation in Markov Decision Processes. - Jean Tarbouriech; Shubhanshu Shekhar; Matteo Pirotta; Mohammad Ghavamzadeh; Alessandro Lazaric (UAI, 2020)

Multi-Scale Zero-Order Optimization of Smooth Functions in an RKHS. - Shubhanshu Shekhar; Tara Javidi (CoRR, 2020)

Significance of Gradient Information in Bayesian Optimization. - Shubhanshu Shekhar; Tara Javidi (AISTATS, 2021)

Uncertain-aware Safe Exploratory Planning using Gaussian Process and Neural Control Contraction Metric. - Dawei Sun; Mohammad Javad Khojasteh; Shubhanshu Shekhar; Chuchu Fan (L4DC, 2021)

Adaptive Sampling for Minimax Fair Classification. - Shubhanshu Shekhar; Greg Fields; Mohammad Ghavamzadeh; Tara Javidi (NeurIPS, 2021)

Adaptive Sampling for Minimax Fair Classification. - Shubhanshu Shekhar; Mohammad Ghavamzadeh; Tara Javidi (CoRR, 2021)

Uncertainty-aware Safe Exploratory Planning using Gaussian Process and Neural Control Contraction Metric. - Dawei Sun; Mohammad Javad Khojasteh; Shubhanshu Shekhar; Chuchu Fan (CoRR, 2021)

Game-theoretic Formulations of Sequential Nonparametric One- and Two-Sample Tests. - Shubhanshu Shekhar; Aaditya Ramdas (CoRR, 2021)

Active Learning for Classification With Abstention. - Shubhanshu Shekhar; Mohammad Ghavamzadeh; Tara Javidi (IEEE J. Sel. Areas Inf. Theory, 2021)

Instance Dependent Regret Analysis of Kernelized Bandits. - Shubhanshu Shekhar; Tara Javidi (ICML, 2022)

Multi-Scale Zero-Order Optimization of Smooth Functions in an RKHS. - Madison Lee; Shubhanshu Shekhar; Tara Javidi (ISIT, 2022)

A permutation-free kernel two-sample test. - Shubhanshu Shekhar; Ilmun Kim; Aaditya Ramdas (NeurIPS, 2022)

Sequential Changepoint Detection via Backward Confidence Sequences. - Shubhanshu Shekhar; Aaditya Ramdas (ICML, 2023)

Risk-limiting financial audits via weighted sampling without replacement. - Shubhanshu Shekhar; Ziyu Xu 001; Zachary C. Lipton; Pierre J. Liang; Aaditya Ramdas (UAI, 2023)

Sequential change detection via backward confidence sequences. - Shubhanshu Shekhar; Aaditya Ramdas (CoRR, 2023)

On the near-optimality of betting confidence sets for bounded means. - Shubhanshu Shekhar; Aaditya Ramdas (CoRR, 2023)

A Permutation-Free Kernel Independence Test. - Shubhanshu Shekhar; Ilmun Kim; Aaditya Ramdas (J. Mach. Learn. Res., 2023)

Deep anytime-valid hypothesis testing. - Teodora Pandeva; Patrick Forré; Aaditya Ramdas; Shubhanshu Shekhar (AISTATS, 2024)

Reducing sequential change detection to sequential estimation. - Shubhanshu Shekhar; Aaditya Ramdas (ICML, 2024)

Nonparametric Two-Sample Testing by Betting. - Shubhanshu Shekhar; Aaditya Ramdas (IEEE Trans. Inf. Theory, 2024)