5 papers
Learning Kernel-Based MDPs from Episodic Preferential Feedback
Nikola Pavlovic, Sattar Vakili, Qing Zhao
Human feedback often arrives as preferences rather than calibrated numeric rewards, motivating reinforcement learning from preferential feedback, also referred to as reinforcement…
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…
Differential Privacy in Kernelized Contextual Bandits via Random Projections
Nikola Pavlovic, Sudeep Salgia, Qing Zhao
We consider the problem of contextual kernel bandits with stochastic contexts, where the underlying reward function belongs to a known Reproducing Kernel Hilbert Space. We study th…
Differentially Private Kernelized Contextual Bandits
Nikola Pavlovic, Sudeep Salgia, Qing Zhao
We consider the problem of contextual kernel bandits with stochastic contexts, where the underlying reward function belongs to a known Reproducing Kernel Hilbert Space (RKHS). We s…
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…