114 citations · 381 across the 40 of their papers we have counts for
6 papers · 1 filter
Safe Linear Leveling Bandits
Ilker Demirel, Mehmet Ufuk Ozdemir, Cem Tekin
Multi-armed bandits (MAB) are extensively studied in various settings where the objective is to \textit{maximize} the actions' outcomes (i.e., rewards) over time. Since safety is c…
Contextual Combinatorial Multi-output GP Bandits with Group Constraints
Sepehr Elahi, Baran Atalar, Sevda Öğüt +1
In federated multi-armed bandit problems, maximizing global reward while satisfying minimum privacy requirements to protect clients is the main goal. To formulate such problems, we…
ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicine
Ilker Demirel, Ahmet Alparslan Celik, Cem Tekin
Finding an optimal individualized treatment regimen is considered one of the most challenging precision medicine problems. Various patient characteristics influence the response to…
Vector Optimization with Stochastic Bandit Feedback
Çağın Ararat, Cem Tekin
We introduce vector optimization problems with stochastic bandit feedback, in which preferences among designs are encoded by a polyhedral ordering cone . Our setup generalizes t…
Contextual Combinatorial Bandits with Changing Action Sets via Gaussian Processes
Andi Nika, Sepehr Elahi, Cem Tekin
We consider a contextual bandit problem with a combinatorial action set and time-varying base arm availability. At the beginning of each round, the agent observes the set of availa…
Conservative Policy Construction Using Variational Autoencoders for Logged Data with Missing Values
Mahed Abroshan, Kai Hou Yip, Cem Tekin +1
In high-stakes applications of data-driven decision making like healthcare, it is of paramount importance to learn a policy that maximizes the reward while avoiding potentially dan…