9 accepted papers
We investigate the contextual slate bandit problem with generalized linear rewards under limited adaptivity. At each round, the learner is presented with $N$ sets of items and constructs a slate by selecting one item per set; the resulting slate yields a scalar reward sampled from a Generalized Line…
We study the Logistic Contextual Slate Bandit problem, where, at each round, an agent selects a slate of $N$ items from an exponentially large set (of size $2^{\Omega(N)}$) of candidate slates provided by the environment. A single binary reward, determined by a logistic model, is observed for the ch
We study the generalized linear contextual bandit problem within the constraints of limited adaptivity. In this paper, we present two algorithms, B-GLinCB and RS-GLinCB, that address, respectively, two prevalent limited adaptivity settings. Given a budget $M$ on the number of policy updates, in the…
Many real-world systems such as e-commerce websites and content-serving platforms employ two-stage recommendation — in the first stage, multiple nominators (experts) provide ranked lists of items (one nominator per category, e.g., sports and political news articles), and in the second stage, an aggr…
We study the causal bandit problem that entails identifying a near-optimal intervention from a specified set A of (possibly non-atomic) interventions over a given causal graph. Here, an optimal intervention in A is one that maximizes the expected value for a designated reward variable in the graph,…
We study the problem of determining the best atomic intervention in a Causal Bayesian Network (CBN) specified only by its causal graph. We model this as a stochastic multi-armed bandit (MAB) problem with side-information, where interventions on CBN correspond to arms of the bandit instance. First, w…
A well-studied challenge that arises in the structure learning problem of causal directed acyclic graphs (DAG) is that using observational data, one can only learn the graph up to a "Markov equivalence class" (MEC). The remaining undirected edges have to be oriented using interventions, which can be…
Learning good interventions in a causal graph can be modelled as a stochastic multi-armed bandit problem with side-information. First, we study this problem when interventions are more expensive than observations and a budget is specified. If there are no backdoor paths from the intervenable nodes t…
In many real-world scenarios, such as gene knockout experiments, targeted interventions are often accompanied by unknown interventions at off-target sites. Moreover, different units can get randomly exposed to different unknown interventions, thereby creating a mixture of interventions. Identifying…