Recent works on neural contextual bandits have achieved compelling performances due to
their ability to leverage the strong representation power of neural networks (NNs) for reward …

The contextual bandit has been identified as a powerful framework to formulate the
recommendation process as a sequential decision-making process, where each item is …

Reward-biased maximum likelihood estimation (RBMLE) is a classic principle in the
adaptive control literature for tackling explore-exploit trade-offs. This paper studies the …

Real-world applications of contextual bandits often exhibit non-stationarity due to
seasonality, serendipity, and evolving social trends. While a number of non-stationary …

We propose a novel algorithm for offline reinforcement learning called Value Iteration with
Perturbed Rewards (VIPeR), which amalgamates the pessimism principle with random …

We propose an algorithm for non-stationary kernel bandits that does not require prior
knowledge of the degree of non-stationarity. The algorithm follows randomized strategies …

In contextual multi-armed bandits, the relationship between contextual information and
rewards is typically unknown, complicating the trade-off between exploration and …

In the dynamic landscape of online businesses, recommender systems are pivotal in
enhancing user experiences. While traditional approaches have relied on static supervised …

Contextual bandit algorithms aim to identify the optimal arm with the highest reward among
a set of candidates, based on the accessible contextual information. Among these …

Current recommender systems predominantly employ supervised learning algorithms, which
often fail to optimize for long-term user engagement. This short-sighted approach highlights …