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 …

In this paper, we study the combinatorial semi-bandits (CMAB) and focus on reducing the
dependency of the batch-size $ K $ in the regret bound, where $ K $ is the total number of …

We investigate the online bandit learning of the monotone multi-linear DR-submodular
functions, designing the algorithm $\mathtt {BanditMLSM} $ that attains $ O (T^{2/3}\log T) …

Influence Maximization (IM), which aims to select a set of users from a social network to
maximize the expected number of influenced users, is an evergreen hot research topic. Its …

We initiate a systematic study on {\em dynamic influence maximization}(DIM). In the DIM
problem, one maintains a seed set $ S $ of at most $ k $ nodes in a dynamically involving …

We investigate the problem of combinatorial multi-armed bandits with stochastic submodular
(in expectation) rewards and full-bandit feedback, where no extra information other than the …

In combinatorial causal bandits (CCB), the learning agent chooses at most K variables in
each round to intervene, collects feedback from the observed variables, with the goal of …

We investigate the problem of unconstrained combinatorial multi-armed bandits with full-
bandit feedback and stochastic rewards for submodular maximization. Previous works …

We consider the problem of Influence Maximization (IM), the task of selecting seed nodes in
a social network such that the expected number of nodes influenced is maximized. We …

We propose a novel combinatorial stochastic-greedy bandit (SGB) algorithm for
combinatorial multi-armed bandit problems when no extra information other than the joint …