43 citations · 53 across the 6 of their papers we have counts for
8 papers
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…
Safe Reinforcement Learning with Linear Function Approximation
Sanae Amani, Christos Thrampoulidis, Lin F. Yang
Safety in reinforcement learning has become increasingly important in recent years. Yet, existing solutions either fail to strictly avoid choosing unsafe actions, which may lead to…
UCB-based Algorithms for Multinomial Logistic Regression Bandits
Sanae Amani, Christos Thrampoulidis
Out of the rich family of generalized linear bandits, perhaps the most well studied ones are logisitc bandits that are used in problems with binary rewards: for instance, when the…
Decentralized Multi-Agent Linear Bandits with Safety Constraints
Sanae Amani, Christos Thrampoulidis
We study decentralized stochastic linear bandits, where a network of agents acts cooperatively to efficiently solve a linear bandit-optimization problem over a -dimensional…
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…