Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

In E-commerce platforms such as Amazon and TaoBao, ranking items in a search session is
a typical multi-step decision-making problem. Learning to rank (LTR) methods have been …

We study the application of the Thompson sampling (TS) methodology to the stochastic
combinatorial multi-armed bandit (CMAB) framework. We analyze the standard TS algorithm …

In this paper, we study the stochastic combinatorial multi-armed bandit (CMAB) framework
that allows a general nonlinear reward function, whose expected value may not depend only …

We propose the contextual combinatorial cascading bandits, a combinatorial online learning
game, where at each time step a learning agent is given a set of contextual information, then …

We study the online influence maximization problem in social networks under the
independent cascade model. Specifically, we aim to learn the set of" best influencers" in a …

Most recommender systems recommend a list of items. The user examines the list, from the
first item to the last, and often chooses the first attractive item and does not examine the rest …

Online learning to rank is a core problem in information retrieval and machine learning.
Many provably efficient algorithms have been recently proposed for this problem in specific …

We study contextual combinatorial bandits with probabilistically triggered arms (C $^ 2$
MAB-T) under a variety of smoothness conditions that capture a wide range of applications …