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Found 11 papers, 1 papers with code
Optimal Regret with Limited Adaptivity for Generalized Linear Contextual Bandits
For our batch learning algorithm B-GLinCB, with $\Omega\left( \log{\log T} \right)$ batches, the regret scales as $\tilde{O}(\sqrt{T})$.
GAR-meets-RAG Paradigm for Zero-Shot Information Retrieval
Given a query and a document corpus, the information retrieval (IR) task is to output a ranked list of relevant documents.
Learning Good Interventions in Causal Graphs via Covering
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.
Disentangling Mixtures of Unknown Causal Interventions
In many real-world scenarios, such as gene knockout experiments, targeted interventions are often accompanied by unknown interventions at off-target sites.
Universal Lower Bound for Learning Causal DAGs with Atomic Interventions
Our second result shows that this bound is, in fact, within a factor of two of the size of the smallest set of single-node interventions that can orient the MEC.
Intervention Efficient Algorithm for Two-Stage Causal MDPs
We study Markov Decision Processes (MDP) wherein states correspond to causal graphs that stochastically generate rewards.
A Causal Bandit Approach to Learning Good Atomic Interventions in Presence of Unobserved Confounders
First, we propose a simple regret minimization algorithm that takes as input a semi-Markovian causal graph with atomic interventions and possibly unobservable variables, and achieves $\tilde{O}(\sqrt{M/T})$ expected simple regret, where $M$ is dependent on the input CBN and could be very small compared to the number of arms.
Efficient reconstruction of depth three circuits with top fan-in two
We develop efficient randomized algorithms to solve the black-box reconstruction problem for polynomials over finite fields, computable by depth three arithmetic circuits with alternating addition/multiplication gates, such that output gate is an addition gate with in-degree two.
Budgeted and Non-budgeted Causal Bandits
If there are no backdoor paths from an intervenable node to the reward node then we propose an algorithm to minimize simple regret that optimally trades-off observations and interventions based on the cost of intervention.
Dis-entangling Mixture of Interventions on a Causal Bayesian Network Using Aggregate Observations
We study the problem of separating a mixture of distributions, all of which come from interventions on a known causal bayesian network.
