Research
I have been working on sequential learning problems arising in stochastic optimization, distributed learning, bandits, bayesian optimization, and active online learning. My research focuses on establishing fundamental limits on feasible performance and developing machine learning algorithms that achieve or approach the performance limits under practical constraints like computation complexity and communication costs. You can find my detailed Research Statement here.
Publications
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
Preprints
Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency
Sudeep Salgia, Sattar Vakili, Qing Zhao
As Easy as ABC: Adaptive Binning Coincidence Test for Uniformity Testing Sudeep Salgia, Qing Zhao, Lang Tong
Spatial Field estimation from Samples taken at Unknown Locations generated by an Unknown Autoregressive Process
Sudeep Salgia, Animesh Kumar
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