The safe linear bandit problem is a version of the classic linear bandit...
Strategic decisions are often made over multiple periods of time, wherei...
We consider a safe optimization problem with bandit feedback in which an...
Accurately modeling the behavior of traffic participants is essential fo...
In adversarial interactions, one is often required to make strategic
dec...
We study a collaborative multi-agent stochastic linear bandit setting, w...
In this work we investigate meta-learning (or learning-to-learn) approac...
Strategic decision-making in uncertain and adversarial environments is
c...
In this paper, we propose a first-order distributed optimization algorit...
We consider General Lotto games of asymmetric information where one play...
We study two model selection settings in stochastic linear bandits (LB)....
Multi-agent systems are designed to concurrently accomplish a diverse se...
We study stage-wise conservative linear stochastic bandits: an instance ...
Many applications require a learner to make sequential decisions given
u...
The design and performance analysis of bandit algorithms in the presence...
In this paper, we investigate informational asymmetries in the Colonel B...
How does system-level information impact the ability of an adversary to
...
Bandit algorithms have various application in safety-critical systems, w...
In this paper, we study an online charge scheduling strategy for fleets ...
A system relying on the collective behavior of decision-makers can be
vu...
A system whose operation relies on the collective behavior of a populati...
In this paper, we design a pricing framework for online electric vehicle...
We study the interaction between a fleet of electric, self-driving vehic...