116 citations · 1k across the 78 of their papers we have counts for
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Is Prior-Free Black-Box Non-Stationary Reinforcement Learning Feasible?
Argyrios Gerogiannis, Yu-Han Huang, Venugopal V. Veeravalli
We study the problem of Non-Stationary Reinforcement Learning (NS-RL) without prior knowledge about the system's non-stationarity. A state-of-the-art, black-box algorithm, known as…
High Probability Latency Sequential Change Detection over an Unknown Finite Horizon
Yu-Han Huang, Venugopal V. Veeravalli
A finite horizon variant of the quickest change detection problem is studied, in which the goal is to minimize a delay threshold (latency), under constraints on the probability of…
Track-MDP: Reinforcement Learning for Target Tracking with Controlled Sensing
Adarsh M. Subramaniam, Argyrios Gerogiannis, James Z. Hare +1
State of the art methods for target tracking with sensor management (or controlled sensing) are model-based and are obtained through solutions to Partially Observable Markov Decisi…
Quickest Change Detection with Confusing Change
Yu-Zhen Janice Chen, Jinhang Zuo, Venugopal V. Veeravalli +1
In the problem of quickest change detection (QCD), a change occurs at some unknown time in the distribution of a sequence of independent observations. This work studies a QCD probl…
Distributed and Rate-Adaptive Feature Compression
Aditya Deshmukh, Venugopal V. Veeravalli, Gunjan Verma
We study the problem of distributed and rate-adaptive feature compression for linear regression. A set of distributed sensors collect disjoint features of regressor data. A fusion…
Quickest Change Detection for Multiple Data Streams Using the James-Stein Estimator
Topi Halme, Venugopal V. Veeravalli, Visa Koivunen
The problem of quickest change detection is studied in the context of detecting an arbitrary unknown mean-shift in multiple independent Gaussian data streams. The James-Stein estim…