Shubhanshu Shekhar

Orcid: 0000-0002-9439-1078

According to our database1, Shubhanshu Shekhar authored at least 28 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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On csauthors.net:

Bibliography

2024
Nonparametric Two-Sample Testing by Betting.
IEEE Trans. Inf. Theory, February, 2024

2023
Deep anytime-valid hypothesis testing.
CoRR, 2023

On the near-optimality of betting confidence sets for bounded means.
CoRR, 2023

Reducing sequential change detection to sequential estimation.
CoRR, 2023

Sequential change detection via backward confidence sequences.
CoRR, 2023

Risk-limiting financial audits via weighted sampling without replacement.
Proceedings of the Uncertainty in Artificial Intelligence, 2023

Sequential Changepoint Detection via Backward Confidence Sequences.
Proceedings of the International Conference on Machine Learning, 2023

2022
A Permutation-Free Kernel Independence Test.
CoRR, 2022

A permutation-free kernel two-sample test.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Multi-Scale Zero-Order Optimization of Smooth Functions in an RKHS.
Proceedings of the IEEE International Symposium on Information Theory, 2022

Instance Dependent Regret Analysis of Kernelized Bandits.
Proceedings of the International Conference on Machine Learning, 2022

2021
Active Learning for Classification With Abstention.
IEEE J. Sel. Areas Inf. Theory, 2021

Game-theoretic Formulations of Sequential Nonparametric One- and Two-Sample Tests.
CoRR, 2021

Uncertainty-aware Safe Exploratory Planning using Gaussian Process and Neural Control Contraction Metric.
CoRR, 2021

Adaptive Sampling for Minimax Fair Classification.
CoRR, 2021

Adaptive Sampling for Minimax Fair Classification.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Uncertain-aware Safe Exploratory Planning using Gaussian Process and Neural Control Contraction Metric.
Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021

Significance of Gradient Information in Bayesian Optimization.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Multi-Scale Zero-Order Optimization of Smooth Functions in an RKHS.
CoRR, 2020

Active Model Estimation in Markov Decision Processes.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Adaptive Sampling for Estimating Probability Distributions.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Adaptive Sampling for Estimating Multiple Probability Distributions.
CoRR, 2019

Active Learning for Binary Classification with Abstention.
CoRR, 2019

Binary Classification with Bounded Abstention Rate.
CoRR, 2019

Multiscale Gaussian Process Level Set Estimation.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
Species Tree Estimation Using ASTRAL: How Many Genes Are Enough?
IEEE ACM Trans. Comput. Biol. Bioinform., 2018

2017
Gaussian Process bandits with adaptive discretization.
CoRR, 2017

Bayesian function optimization with adaptive discretization.
Proceedings of the 55th Annual Allerton Conference on Communication, 2017


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