8 papers
DAL: A Practical Prior-Free Black-Box Framework for Piecewise Stationary Bandits
Argyrios Gerogiannis, Yu-Han Huang, Subhonmesh Bose +1
We introduce a practical, black-box framework termed Detection Augmented Learning (DAL) for the problem of piecewise stationary bandits without knowledge of the underlying non-stat…
Nonparametric Sparse Online Learning of the Koopman Operator
Boya Hou, Sina Sanjari, Nathan Dahlin +2
The Koopman operator provides a powerful framework for representing the dynamics of general nonlinear dynamical systems. However, existing data-driven approaches to learning the Ko…
Learning Where to Look: UCB-Driven Controlled Sensing for Quickest Change Detection
Yu-Han Huang, Argyrios Gerogiannis, Subhonmesh Bose +1
We study the multichannel quickest change detection problem with bandit feedback and controlled sensing, in which an agent sequentially selects one of the data streams to observe a…
Pricing Problems in Adoption of New Technologies
Yijin Wang, Subhonmesh Bose
We propose a generalization of the Bass diffusion model in discrete-time that explicitly models the effect of price in adoption. Our model is different from earlier price-incorpora…
Detection Augmented Bandit Procedures for Piecewise Stationary MABs: A Modular Approach
Yu-Han Huang, Argyrios Gerogiannis, Subhonmesh Bose +1
Conventional Multi-Armed Bandit (MAB) algorithms are designed for stationary environments, where the reward distributions associated with the arms do not change with time. In many…
Harnessing Information in Incentive Design
Raj Kiriti Velicheti, Subhonmesh Bose, Tamer BaÅar
Incentive design deals with interaction between a principal and an agent where the former can shape the latter's utility through a policy commitment. It is well known that the prin…