Copertina

The Role of Contextual Information in Best Arm Identification

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Kato, Masahiro, Ariu, Kaito
Natura:	Preprint
Pubblicazione:	2021
Soggetti:	Machine Learning Econometrics Statistics Theory Methodology
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Minimax and Bayes Optimal Best-Arm Identification
di: Kato, Masahiro
Pubblicazione: (2025)

Worst-Case Optimal Multi-Armed Gaussian Best Arm Identification with a Fixed Budget
di: Kato, Masahiro
Pubblicazione: (2023)

Locally Optimal Fixed-Budget Best Arm Identification in Two-Armed Gaussian Bandits with Unknown Variances
di: Kato, Masahiro
Pubblicazione: (2023)

Minimax Optimal Simple Regret in Two-Armed Best-Arm Identification
di: Kato, Masahiro
Pubblicazione: (2024)

Riesz Regression As Direct Density Ratio Estimation
di: Kato, Masahiro
Pubblicazione: (2025)

Semi-Supervised Treatment Effect Estimation with Unlabeled Covariates for Prediction-Powered Causal Inference
di: Kato, Masahiro
Pubblicazione: (2025)

Nearest Neighbor Matching as Least Squares Density Ratio Estimation and Riesz Regression
di: Kato, Masahiro
Pubblicazione: (2025)

Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning
di: Kato, Masahiro
Pubblicazione: (2026)

General Bayesian Policy Learning
di: Kato, Masahiro
Pubblicazione: (2026)

Direct Bias-Correction Term Estimation for Average Treatment Effect Estimation
di: Kato, Masahiro
Pubblicazione: (2025)

Causal-Policy Forest for End-to-End Policy Learning
di: Kato, Masahiro
Pubblicazione: (2025)

Debiased Nonparametric Regression for Statistical Inference and Distributionally Robustness
di: Kato, Masahiro
Pubblicazione: (2024)

Direct Debiased Machine Learning via Bregman Divergence Minimization
di: Kato, Masahiro
Pubblicazione: (2025)

A Unified Theory for Causal Inference: Direct Debiased Machine Learning via Bregman-Riesz Regression
di: Kato, Masahiro
Pubblicazione: (2025)

A Note on Doubly Robust Estimator in Regression Discontinuity Designs
di: Kato, Masahiro
Pubblicazione: (2024)

genriesz: A Python Package for Automatic Debiased Machine Learning with Generalized Riesz Regression
di: Kato, Masahiro
Pubblicazione: (2026)

Minimax and Bayes Optimal Adaptive Experimental Design for Treatment Choice
di: Kato, Masahiro
Pubblicazione: (2025)

Bridging the Gap between Empirical Welfare Maximization and Conditional Average Treatment Effect Estimation in Policy Learning
di: Kato, Masahiro
Pubblicazione: (2025)

ScoreMatchingRiesz: Score Matching for Debiased Machine Learning and Policy Path Estimation
di: Kato, Masahiro
Pubblicazione: (2025)

Debiased Regression for Root-N-Consistent Conditional Mean Estimation
di: Kato, Masahiro
Pubblicazione: (2024)

A Unified Framework for Debiased Machine Learning: Riesz Representer Fitting under Bregman Divergence
di: Kato, Masahiro
Pubblicazione: (2026)

Generalized Neyman Allocation for Locally Minimax Optimal Best-Arm Identification
di: Kato, Masahiro
Pubblicazione: (2024)

PUATE: Efficient Average Treatment Effect Estimation from Treated (Positive) and Unlabeled Units
di: Kato, Masahiro, et al.
Pubblicazione: (2025)

Selecting the Best Arm in One-Shot Multi-Arm RCTs: The Asymptotic Minimax-Regret Decision Framework for the Best-Population Selection Problem
di: Joo, Joonhwi
Pubblicazione: (2025)

Identification and Debiased Learning of Causal Effects with General Instrumental Variables
di: Chen, Shuyuan, et al.
Pubblicazione: (2025)

Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects
di: Ghassami, AmirEmad, et al.
Pubblicazione: (2022)

Sequential Audit Sampling with Statistical Guarantees
di: Kato, Masahiro, et al.
Pubblicazione: (2026)

Dynamic covariate balancing: estimating treatment effects over time with potential local projections
di: Viviano, Davide, et al.
Pubblicazione: (2021)

Dynamic treatment effects: high-dimensional inference under model misspecification
di: Zhang, Yuqian, et al.
Pubblicazione: (2021)

The Local Approach to Causal Inference under Network Interference
di: Auerbach, Eric, et al.
Pubblicazione: (2021)

Double Machine Learning of Continuous Treatment Effects with General Instrumental Variables
di: Chen, Shuyuan, et al.
Pubblicazione: (2026)

Near-Optimal Non-Parametric Sequential Tests and Confidence Sequences with Possibly Dependent Observations
di: Bibaut, Aurelien, et al.
Pubblicazione: (2022)

It's Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation
di: Jin, Jikai, et al.
Pubblicazione: (2025)

Modeling Covariate Transition for Efficient Estimation of Longitudinal Treatment Effects in Randomized Experiments
di: Chihara, Naoki, et al.
Pubblicazione: (2026)

Orthogonal Bootstrap: Efficient Simulation of Input Uncertainty
di: Liu, Kaizhao, et al.
Pubblicazione: (2024)

Demistifying Inference after Adaptive Experiments
di: Bibaut, Aurélien, et al.
Pubblicazione: (2024)

Bayesian Semi-supervised Inference via a Debiased Modeling Approach
di: Sert, Gözde, et al.
Pubblicazione: (2025)

Entrywise Inference for Missing Panel Data: A Simple and Instance-Optimal Approach
di: Yan, Yuling, et al.
Pubblicazione: (2024)

Smaller Confidence Intervals From IPW Estimators via Data-Dependent Coarsening
di: Kalavasis, Alkis, et al.
Pubblicazione: (2024)

Policy Learning for Optimal Dynamic Treatment Regimes with Observational Data
di: Sakaguchi, Shosei
Pubblicazione: (2024)