Cover Image

Sampling as Bandits: Evaluation-Efficient Design for Black-Box Densities

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Matsubara, Takuo, Duncan, Andrew, Cotter, Simon, Zygalakis, Konstantinos
Format:	Preprint
Published:	2025
Subjects:	Methodology Machine Learning Computation
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning
by: Matsubara, Takuo
Published: (2024)

Bures-Wasserstein Importance-Weighted Evidence Lower Bound: Exposition and Applications
by: Jiang, Peiwen, et al.
Published: (2026)

Inversion-Free Natural Gradient Descent on Riemannian Manifolds
by: Draca, Dario, et al.
Published: (2026)

Maximin Robust Bayesian Experimental Design
by: Abdulsamad, Hany, et al.
Published: (2026)

A Black-Box Debiasing Framework for Conditional Sampling
by: Cui, Han, et al.
Published: (2025)

Sampling by averaging: A multiscale approach to score estimation
by: Cordero-Encinar, Paula, et al.
Published: (2025)

Cramming Contextual Bandits for On-policy Statistical Evaluation
by: Jia, Zeyang, et al.
Published: (2024)

Tree Bandits for Generative Bayes
by: O'Hagan, Sean, et al.
Published: (2024)

Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box
by: Giordano, Ryan, et al.
Published: (2023)

Black Box Causal Inference: Effect Estimation via Meta Prediction
by: Bynum, Lucius E. J., et al.
Published: (2025)

Pareto Smoothed Importance Sampling
by: Vehtari, Aki, et al.
Published: (2015)

Robust and Conjugate Spatio-Temporal Gaussian Processes
by: Laplante, William, et al.
Published: (2025)

Generator-Mediated Bandits: Thompson Sampling for GenAI-Powered Adaptive Interventions
by: Brooks, Marc, et al.
Published: (2025)

midr: Learning from Black-Box Models by Maximum Interpretation Decomposition
by: Asashiba, Ryoichi, et al.
Published: (2025)

Bayesian Multiple Multivariate Density-Density Regression
by: Nguyen, Khai, et al.
Published: (2026)

ALMAB-DC: Active Learning, Multi-Armed Bandits, and Distributed Computing for Sequential Experimental Design and Black-Box Optimization
by: Hui-Mean, Foo, et al.
Published: (2026)

An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits
by: Liang, Biyonka, et al.
Published: (2023)

SADA: Safe and Adaptive Aggregation of Multiple Black-Box Predictions in Semi-Supervised Learning
by: Shan, Jiawei, et al.
Published: (2025)

Auditing Differential Privacy in the Black-Box Setting
by: Shi, Kaining, et al.
Published: (2025)

Metropolis Sampling
by: Martino, Luca, et al.
Published: (2017)

Efficient optimization of expensive black-box simulators via marginal means, with application to neutrino detector design
by: Kim, Hwanwoo, et al.
Published: (2025)

Bayesian Multivariate Density-Density Regression
by: Nguyen, Khai, et al.
Published: (2025)

Linear Contextual Bandits with Interference
by: Xu, Yang, et al.
Published: (2024)

Hierarchical Bayesian data selection
by: Cotter, Simon L.
Published: (2022)

Multi-Armed Bandits with Network Interference
by: Agarwal, Abhineet, et al.
Published: (2024)

Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits
by: Zhan, Ruohan, et al.
Published: (2021)

Locally Interpretable Individualized Treatment Rules for Black-Box Decision Models
by: Charvadeh, Yasin Khadem, et al.
Published: (2026)

Sample-Efficient Omniprediction for Proper Losses
by: Gibbs, Isaac, et al.
Published: (2025)

Optimization by Parallel Quasi-Quantum Annealing with Gradient-Based Sampling
by: Ichikawa, Yuma, et al.
Published: (2024)

Leveraging Black-box Models to Assess Feature Importance in Unconditional Distribution
by: Zhou, Jing, et al.
Published: (2024)

RCT Rejection Sampling for Causal Estimation Evaluation
by: Keith, Katherine A., et al.
Published: (2023)

High-dimensional Nonparametric Contextual Bandit Problem
by: Iwazaki, Shogo, et al.
Published: (2025)

Automated Efficient Estimation using Monte Carlo Efficient Influence Functions
by: Agrawal, Raj, et al.
Published: (2024)

Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms
by: Lau, Tim Tsz-Kit, et al.
Published: (2023)

Density estimation via mixture discrepancy and moments
by: Lei, Zhengyang, et al.
Published: (2025)

Expected information gain estimation via density approximations: Sample allocation and dimension reduction
by: Li, Fengyi, et al.
Published: (2024)

Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding
by: Rambachan, Ashesh, et al.
Published: (2022)

Locally Private Nonparametric Contextual Multi-armed Bandits
by: Ma, Yuheng, et al.
Published: (2025)

Nearly Optimal Best Arm Identification for Semiparametric Bandits
by: Kim, Seok-Jin
Published: (2026)

Safe, Scalable, and Accurate Bayes Posterior Sampling for Large-Data Generalized Linear Mixed Models
by: Baek, Youngsoo, et al.
Published: (2026)