8 citations · 9 across the 13 of their papers we have counts for
7 papers · 1 filter
PeakFocus: Bridging Peak Localization and Intensity Regression via a Unified Multi-Scale Framework for Electricity Load Forecasting
Wangzhi Yu, Peng Zhu, Qing Zhao +2
Electricity load peak forecasting (ELPF), simultaneously predicting peak timing and intensity, is a prerequisite for effective grid scheduling and risk management. However, existin…
Characterizing the Accuracy-Communication-Privacy Trade-off in Distributed Stochastic Convex Optimization
Sudeep Salgia, Nikola Pavlovic, Yuejie Chi +1
We consider the problem of differentially private stochastic convex optimization (DP-SCO) in a distributed setting with clients, where each of them has a local dataset of i…
Order-Optimal Regret in Distributed Kernel Bandits using Uniform Sampling with Shared Randomness
Nikola Pavlovic, Sudeep Salgia, Qing Zhao
We consider distributed kernel bandits where agents aim to collaboratively maximize an unknown reward function that lies in a reproducing kernel Hilbert space. Each agent seque…
Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency
Sudeep Salgia, Sattar Vakili, Qing Zhao
We consider Bayesian optimization using Gaussian Process models, also referred to as kernel-based bandit optimization. We study the methodology of exploring the domain using random…
Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach
Dan Ben Ami, Kobi Cohen, Qing Zhao
Federated learning (FL) is an emerging machine learning (ML) paradigm used to train models across multiple nodes (i.e., clients) holding local data sets, without explicitly exchang…
A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regret
Sudeep Salgia, Qing Zhao, Tamir Gabay +1
We consider the problem of online stochastic optimization in a distributed setting with clients connected through a central server. We develop a distributed online learning alg…
Distributed Linear Bandits under Communication Constraints
Sudeep Salgia, Qing Zhao
We consider distributed linear bandits where agents learn collaboratively to minimize the overall cumulative regret incurred by all agents. Information exchange is facilitated…