Research
I am interested in sequential learning problems arising in reinforcement learning, stochastic optimization, distributed learning, bandits, bayesian optimization, and active online learning. My broad research goals are to:
- facilitate provably efficient, interpretable, adaptive data-driven decision-making;
- investigate fundamental trade-offs in distributed learning problems;
- design optimal solutions that comply with practical systemic limitations such as communication, privacy, and computation.
My research employs tools from high-dimensional statistics, probability theory, large-scale optimization, information theory and machine learning.
Publications and Preprints
The Sample-Communication Complexity Trade-off in Federated Q-Learning
Sudeep Salgia, Yuejie Chi
Neural Information Processing Systems (NeurIPS), 2024. Accepted as an oral (top 1% of accepted papers)
Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency
Sudeep Salgia, Sattar Vakili, Qing Zhao
International Conference on Machine Learning (ICML), 2024. Resolves an open COLT problem
Adaptive Binning Coincidence Test for Uniformity Testing
Sudeep Salgia, Xinyi Wang, Qing Zhao, Lang Tong
IEEE Transactions on Signal Processing, 2024
Characterizing the Accuracy-Communication-Privacy Trade-off in Distributed Stochastic Convex Optimization
Sudeep Salgia, Nikola Pavlovic, Yuejie Chi, Qing Zhao
Submitted to International Conference on Artificial Intelligence and Statistics (AISTATS), 2025
Order-Optimal Regret in Distributed Kernel Bandits using Uniform Sampling with Shared Randomness
Nikola Pavlovic, Sudeep Salgia, Qing Zhao
Preliminary version in NeurIPS BDU Workshop, 2024
Submitted to International Conference on Artificial Intelligence and Statistics (AISTATS), 2025
Differentially Private Kernelized Contextual Bandits
Nikola Pavlovic, Sudeep Salgia, Qing Zhao
Submitted to International Conference on Artificial Intelligence and Statistics (AISTATS), 2025
A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regret
Sudeep Salgia, Qing Zhao, Tamir Gabay, Kobi Cohen
IEEE Transactions on Signal Processing 2024
Collaborative Learning in Kernel-based Bandits
Sudeep Salgia, Sattar Vakili, Qing Zhao
IEEE Transactions on Signal Processing 2023
Distributed Linear Bandits under Communication Constraints
Sudeep Salgia, Qing Zhao
ICML 2023
Provably and Practically Efficient Neural Contextual Bandits
Sudeep Salgia, Sattar Vakili, Qing Zhao
ICML 2023
A perspective on data sharing in digital food safety systems
Chenhao Qian, Yuhan Liu, Cecil Barnett-Neefs, Sudeep Salgia, Omer Serbetci, Aaron Adalja, Jayadev Acharya, Qing Zhao, Renata Ivanek, Martin Wiedmann
Critical Reviews in Food Science and Nutrition
Disagreement-based Active Learning in Online Settings
Boshuang Huang, Sudeep Salgia, Qing Zhao
IEEE Transactions on Signal Processing 2022
A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance
Sudeep Salgia, Sattar Vakili, Qing Zhao
NeurIPS 2021
An order-optimal adaptive test plan for noisy group testing under unknown noise models
Sudeep Salgia, Qing Zhao
ICASSP 2021
Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization
Sudeep Salgia, Qing Zhao, Sattar Vakili
ICML 2020
Stochastic Gradient Descent on a Tree: an Adaptive and Robust Approach to Stochastic Convex Optimization
Sattar Vakili, Sudeep Salgia, Qing Zhao
Allerton 2019
On Bandlimited Spatiotemporal Field Sampling with Location and Time Unaware Mobile Sensors
Sudeep Salgia, Animesh Kumar
ICASSP 2018
Other Projects
PhD Thesis: Stochastic Optimization and Learning: An Adaptive and Resource-Efficient Approach
Bandlimited Spatiotemporal Field Sampling with Location and Time Unaware Mobile Senors
UG thesis under the guidance of Prof. Animesh Kumar