257 citations · 519 across the 43 of their papers we have counts for
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Directional Optimism for Safe Linear Bandits
Spencer Hutchinson, Berkay Turan, Mahnoosh Alizadeh
The safe linear bandit problem is a version of the classical stochastic linear bandit problem where the learner's actions must satisfy an uncertain constraint at all rounds. Due it…
The Impact of the Geometric Properties of the Constraint Set in Safe Optimization with Bandit Feedback
Spencer Hutchinson, Berkay Turan, Mahnoosh Alizadeh
We consider a safe optimization problem with bandit feedback in which an agent sequentially chooses actions and observes responses from the environment, with the goal of maximizing…
Collaborative Multi-agent Stochastic Linear Bandits
Ahmadreza Moradipari, Mohammad Ghavamzadeh, Mahnoosh Alizadeh
We study a collaborative multi-agent stochastic linear bandit setting, where agents that form a network communicate locally to minimize their overall regret. In this setting, e…
Multi-Environment Meta-Learning in Stochastic Linear Bandits
Ahmadreza Moradipari, Mohammad Ghavamzadeh, Taha Rajabzadeh +2
In this work we investigate meta-learning (or learning-to-learn) approaches in multi-task linear stochastic bandit problems that can originate from multiple environments. Inspired…
Feature and Parameter Selection in Stochastic Linear Bandits
Ahmadreza Moradipari, Berkay Turan, Yasin Abbasi-Yadkori +2
We study two model selection settings in stochastic linear bandits (LB). In the first setting, which we refer to as feature selection, the expected reward of the LB problem is in t…
Stage-wise Conservative Linear Bandits
Ahmadreza Moradipari, Christos Thrampoulidis, Mahnoosh Alizadeh
We study stage-wise conservative linear stochastic bandits: an instance of bandit optimization, which accounts for (unknown) safety constraints that appear in applications such as…
Regret Bounds for Safe Gaussian Process Bandit Optimization
Sanae Amani, Mahnoosh Alizadeh, Christos Thrampoulidis
Many applications require a learner to make sequential decisions given uncertainty regarding both the system's payoff function and safety constraints. In safety-critical systems, i…
Safe Linear Thompson Sampling with Side Information
Ahmadreza Moradipari, Sanae Amani, Mahnoosh Alizadeh +1
The design and performance analysis of bandit algorithms in the presence of stage-wise safety or reliability constraints has recently garnered significant interest. In this work, w…
Linear Stochastic Bandits Under Safety Constraints
Sanae Amani, Mahnoosh Alizadeh, Christos Thrampoulidis
Bandit algorithms have various application in safety-critical systems, where it is important to respect the system constraints that rely on the bandit's unknown parameters at every…