Shubhanshu Shekhar

Shubhanshu Shekhar

Ph.D. Candidate

University of California, San Diego

Hello!

I am a Postdoctoral researcher in the Department of Statistics and Data Science at CMU, working with Prof. Aaditya Ramdas. My research interests lie in the general areas of maching learning and sequential decision making. I am currently working on the problem of sequential nonparametric hypothesis testing. In the past, I have worked on the topics of Bayesian Optimization, Active Learning and Reinforcement Learning.

Interests

  • Gaussian Process Bandits
  • Active Learning
  • Optimization
  • Reinforcement Learning

Education

  • PhD in Electrical Engineering

    University of California, San Diego

  • M.E. in Electrical Engineering

    Indian Institute of Science, Bangalore

  • B.E. in Electrical Engineering

    Indian Institute of Technology, Kharagpur

Recent News

All news»

[01/22/21] Our paper Significance of Gradient Information in Bayesian Optimization got accepted at AISTATS 2021 (final version in preparation).

[11/20/20] Awarded the Shannon Memorial Fellowship by CMRR, UCSD for the academic year 2020/21.

[06/01/20] Our paper Adaptive Sampling for Learning Probability Distributions got accepted at ICML 2020.

[05/14/20] Our paper Active Model Estimation in Markov Decision Processes got accepted at UAI 2020.

Bayesian Optimization

Low complexity algorithms with improved regret bounds

Active Learning in Bandits and MDPs

Optimal scheme for allocating samples to learn $K$ distributions uniformly well.

Kernelized Bandits

Algorithm with uniformly improved regret bounds + a computationally efficient heuristic with better empirical performance

Active Learning for Classification With Abstention

A minimax near-optimal active learning algorithm for classification with abstention.

Sample Complexity of Species Tree Estimation

Precise characterization of sample complexity of a popular species tree reconstruction algorithm - ASTRAL

Experience

 
 
 
 
 

Research Intern

Facebook AI Research

Jun 2019 – Sep 2019 Menlo Park, California
Worked on the design and analysis of an adaptive sampling scheme for estimating probability distributions uniformly well in terms of several commonly used distance measures.
 
 
 
 
 

Research Intern

Qualcomm Research

Jun 2018 – Sep 2018 San Diego, California
Worked on the desgin of algorithms for optimizing the low-level code representation of commonly used tensor-operations in Deep Learning.

Coursework

Selected list of graduate coursework in ECE and Math departments


ECE

  • Matrix Analysis
  • Information Theory
  • Random Processes
  • Parameter Estimation
  • Wave Theory of Information
  • Detection and Estimation Theory
  • Convex Optimization and Applications
  • Stochastic Processes in Dynamical Systems

Math

The term in bracket denotes the number of courses in the series.

  • Real Analysis (3)
  • Applied Algebra (2)
  • Probability Theory (3)
  • Functional Analysis (2)
  • Mathematical Statistics (3)
  • Statistical Learning Theory (1)

Teaching

I have served as a TA for the following courses.

Recent Posts

[UIT 1] Regret bound for mixture method

Summary: Derivation of upper bound on the regret for the mixture method (KT scheme) for individual sequence prediction. Suppose $\mathcal{X} = \{1, \ldots, m\}$ for some integer $m \geq 2$ and let $\Theta = \Delta_m$ denote the $m-1$ dimensional probability simplex.

Lower Bound for Active Learning in Bandits via Le Cam's method

Summary: We introduce LeCam’s method for obtaining minimax lower bounds for statistical estimation problems, which proceeds by relating the probabililty of error of a binary hypothesis testing problem to the total-variation distance between the two distributions.

Recent Publications

Multi-Scale Zero-Order Optimization of Smooth Functions in an RKHS

We aim to optimize a black-box function f : X→ R under the assumption that f is H¨older smooth and has bounded norm in the Reproducing …

Adaptive Sampling for Estimating Multiple Probability Distributions

We consider the problem of allocating samples to a finite set of discrete distributions in order to learn them uniformly well in terms …

Active learning for binary classification with abstention

We construct and analyze active learning algorithms for the problem of binary classification with abstention. We consider three …

Active Model Estimation in Markov Decision Processes

We study the problem of efficient exploration in order to learn an accurate model of an environment, modeled as a Markov decision …

Multiscale Gaussian Process Level Set Estimation

In this paper, the problem of estimating the level set of a black-box function from noisy and expensive evaluation queries is …

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