arXiv AI
Jun 29
The Minimal Search Space for Conditional Causal Bandits
arXiv:2502. 06577v3 Announce Type: replace-cross Abstract: Causal knowledge can be used to support decision-making problems.
Evaluating and Learning Robust Bandit Policies Under Uncertain Causal Mechanisms
arXiv:2508. 02812v3 Announce Type: replace Abstract: Causal graphical models can encode large amounts structural knowledge, both from the background knowledge of domain experts and the structural knowledge discovered from randomized experiments or observational data.
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arXiv AI
Jul 20
Information-Directed Sampling for Causal Bandits
arXiv:2607. 15577v1 Announce Type: cross Abstract: Causal bandits exploit structural relationships among variables to share information across interventions and accelerate the identification of high-reward decisions.
arXiv Machine Learning
Jun 15
Graph Structured Combinatorial Semi-Bandit with Nonlinear Reward Associations through Separable Signals
arXiv:2606. 14650v1 Announce Type: new Abstract: The identification of optimal structures within vast arrays of interconnected data necessitates significant sampling- and computational effort.
arXiv Machine Learning
Jun 2
Off-Policy Learning in Large Action Spaces: Optimization Matters More Than Estimation
arXiv:2509. 03456v2 Announce Type: replace-cross Abstract: Off-policy evaluation (OPE) and off-policy learning (OPL) are foundational for decision-making in offline contextual bandits.
arXiv AI
Jul 13
Correlation-Aware Contextual Bandits with Surrogate Rewards for LLM Routing
arXiv:2607. 09015v1 Announce Type: cross Abstract: We study contextual bandit problems with correlated arms and access to surrogate reward signals produced by a machine learning model, motivated by applications such as large language model (LLM) routing.
arXiv AI
Jul 7
Fixed-Confidence Best-Arm Identification for Causal Mediation Analysis
arXiv:2607. 04315v1 Announce Type: cross Abstract: This paper studies the problem of identifying the treatment that maximizes the expected natural direct potential outcome (NDPO), which captures the potential outcome of an intervention while excluding the pathway transmitted through a mediator that researchers may wish to remove from evaluation.
arXiv Machine Learning
Jun 2
Bandit Simulation for Average Reward Inference
arXiv:2606. 00913v1 Announce Type: cross Abstract: Multi-arm bandit algorithms are increasingly used in online platforms, clinical trials, and social science experiments, but valid statistical inference on their performance remains an open challenge.
arXiv Machine Learning
Jun 2
A Direct Approach for Handling Contextual Bandits with Latent State Dynamics
arXiv:2604. 08149v2 Announce Type: replace Abstract: We consider a linear contextual bandit model where contexts and rewards are governed by a finite hidden Markov chain.
arXiv AI
Jun 16
When Do We Need LLMs? A Diagnostic for Language-Driven Bandits
arXiv:2604. 05859v2 Announce Type: replace Abstract: We study Contextual Multi-Armed Bandits (CMABs) for non-episodic decision-making problems where the context includes both textual and numerical information (e.
arXiv Machine Learning
Aug 10
Bootstrap-Conditioned Action Selection with Tabular Foundation Models
arXiv:2608. 06559v1 Announce Type: new Abstract: Contextual bandits offer a natural framework for sample-efficient personalization, but practical deployment remains difficult under sparse, biased interaction data, unreliable uncertainty estimates, and severe cold starts.
arXiv AI
Aug 5
In-Context Pure Exploration in Continuous Decision Spaces
arXiv:2602. 17976v2 Announce Type: replace-cross Abstract: In active sequential testing, also termed pure exploration, a learner is tasked with the goal to adaptively acquire information so as to identify an unknown ground-truth hypothesis with as few queries as possible.
arXiv Machine Learning
Jul 31
On-Policy and Off-Policy Learning for Large Action Spaces
arXiv:2607. 28408v1 Announce Type: new Abstract: This thesis studies policy learning in interactive systems where an agent observes a context, selects an action from a very large set, and receives partial feedback.