Cover Image

Using causal abstractions to accelerate decision-making in complex bandit problems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Dyer, Joel, Bishop, Nicholas, Calinescu, Anisoara, Wooldridge, Michael, Zennaro, Fabio Massimo
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Causally Abstracted Multi-armed Bandits
by: Zennaro, Fabio Massimo, et al.
Published: (2024)

Sandbagging in a Simple Survival Bandit Problem
by: Dyer, Joel, et al.
Published: (2025)

Bayesian Decision Making around Experts
by: Ornia, Daniel Jarne, et al.
Published: (2025)

Neural Network-Based Parameter Estimation of a Labour Market Agent-Based Model
by: Alves, M Lopes, et al.
Published: (2026)

Automatic Differentiation of Agent-Based Models
by: Quera-Bofarull, Arnau, et al.
Published: (2025)

Emergent Risk Awareness in Rational Agents under Resource Constraints
by: Ornia, Daniel Jarne, et al.
Published: (2025)

A multi-objective combinatorial optimisation framework for large scale hierarchical population synthesis
by: Mahmood, Imran, et al.
Published: (2024)

Multi-Level Causal Embeddings
by: Schooltink, Willem, et al.
Published: (2026)

SAGE: Scalable Ground Truth Evaluations for Large Sparse Autoencoders
by: Venhoff, Constantin, et al.
Published: (2024)

Incorporating structural uncertainty in causal decision making
by: Kaptein, Maurits
Published: (2025)

Mathematics of statistical sequential decision-making: concentration, risk-awareness and modelling in stochastic bandits, with applications to bariatric surgery
by: Saux, Patrick
Published: (2024)

IMAGIC-500: IMputation benchmark on A Generative Imaginary Country (500k samples)
by: Sun, Siyi, et al.
Published: (2025)

Causal Abstraction Learning based on the Semantic Embedding Principle
by: D'Acunto, Gabriele, et al.
Published: (2025)

Efficient learning by implicit exploration in bandit problems with side observations
by: Kocak, Tomas, et al.
Published: (2026)

Extreme bandits
by: Carpentier, Alexandra, et al.
Published: (2026)

Spectral bandits
by: Kocák, Tomáš, et al.
Published: (2026)

Active clustering with bandit feedback
by: Thuot, Victor, et al.
Published: (2024)

TEE4EHR: Transformer Event Encoder for Better Representation Learning in Electronic Health Records
by: Karami, Hojjat, et al.
Published: (2024)

Accelerating prototype selection with spatial abstraction
by: Carbonera, Joel Luís
Published: (2024)

Functional multi-armed bandit and the best function identification problems
by: Dorn, Yuriy, et al.
Published: (2025)

Robust Uncertainty Quantification Using Conformalised Monte Carlo Prediction
by: Bethell, Daniel, et al.
Published: (2023)

HELLINGER-UCB: A novel algorithm for stochastic multi-armed bandit problem and cold start problem in recommender system
by: Yang, Ruibo, et al.
Published: (2024)

Instance-dependent Stochastic Lipschitz bandit
by: Potfer, Marius, et al.
Published: (2026)

Spectral bandits for smooth graph functions
by: Valko, Michal, et al.
Published: (2026)

Approximate information maximization for bandit games
by: Barbier-Chebbah, Alex, et al.
Published: (2023)

Online learning in bandits with predicted context
by: Guo, Yongyi, et al.
Published: (2023)

Risk and optimal policies in bandit experiments
by: Adusumilli, Karun
Published: (2021)

BACON: A fully explainable AI model with graded logic for decision making problems
by: Bai, Haishi, et al.
Published: (2025)

SynEHRgy: Synthesizing Mixed-Type Structured Electronic Health Records using Decoder-Only Transformers
by: Karami, Hojjat, et al.
Published: (2024)

On the optimal regret of collaborative personalized linear bandits
by: Huang, Bruce, et al.
Published: (2025)

Offline-to-online hyperparameter transfer for stochastic bandits
by: Sharma, Dravyansh, et al.
Published: (2025)

Revealing graph bandits for maximizing local influence
by: Carpentier, Alexandra, et al.
Published: (2026)

Unified theory of upper confidence bound policies for bandit problems targeting total reward, maximal reward, and more
by: Kikkawa, Nobuaki, et al.
Published: (2024)

Linear bandits with polylogarithmic minimax regret
by: Lumbreras, Josep, et al.
Published: (2024)

Efficient kernelized bandit algorithms via exploration distributions
by: Hu, Bingshan, et al.
Published: (2025)

Leveraging priors on distribution functions for multi-arm bandits
by: Vashishtha, Sumit, et al.
Published: (2025)

When and why randomised exploration works (in linear bandits)
by: Abeille, Marc, et al.
Published: (2025)

Generative models for decision-making under distributional shift
by: Cheng, Xiuyuan, et al.
Published: (2026)

Lookahead identification in adversarial bandits: accuracy and memory bounds
by: Brukhim, Nataly, et al.
Published: (2026)

Trading off rewards and errors in multi-armed bandits
by: Erraqabi, Akram, et al.
Published: (2026)