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) …

We study sequential bilateral trade where sellers and buyers valuations are completely
arbitrary ({\sl ie}, determined by an adversary). Sellers and buyers are strategic agents with …

Many online platforms, ranging from online retail stores to social media platforms, employ
algorithms to optimize their offered assortment of items (eg, products and contents). These …

We investigate the problem of stochastic, combinatorial multi-armed bandits where the
learner only has access to bandit feedback and the reward function can be non-linear. We …

In many online platforms, customers' decisions are substantially influenced by product
rankings as most customers only examine a few top-ranked products. Concurrently, such …

This paper presents a unified approach for maximizing continuous DR-submodular functions
that encompasses a range of settings and oracle access types. Our approach includes a …

In the classical contextual bandits problem, in each round $ t $, a learner observes some
context $ c $, chooses some action $ a $ to perform, and receives some reward $ r_ {a, t}(c) …

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 a carbon auction, licenses for CO2 emissions are allocated among multiple interested
players. Inspired by this setting, we consider repeated multi-unit auctions with uniform …

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