Copertina

Optimizing Adaptive Experiments: A Unified Approach to Regret Minimization and Best-Arm Identification

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Qin, Chao, Russo, Daniel
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Econometrics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

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

The Role of Contextual Information in Best Arm Identification
di: Kato, Masahiro, et al.
Pubblicazione: (2021)

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

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

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)

Admissibility of Completely Randomized Trials: A Large-Deviation Approach
di: Imbens, Guido, et al.
Pubblicazione: (2025)

Best Arm Identification with Minimal Regret
di: Yang, Junwen, et al.
Pubblicazione: (2024)

Efficient Adaptive Experimental Design for Average Treatment Effect Estimation
di: Kato, Masahiro, et al.
Pubblicazione: (2020)

Inference for Batched Adaptive Experiments
di: Kemper, Jan, et al.
Pubblicazione: (2025)

Regret Equals Covariance: A Closed-Form Characterization for Stochastic Optimization
di: Aldridge, Irene
Pubblicazione: (2026)

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)

Active Adaptive Experimental Design for Treatment Effect Estimation with Covariate Choices
di: Kato, Masahiro, et al.
Pubblicazione: (2024)

A Unifying Framework for Robust and Efficient Inference with Unstructured Data
di: Carlson, Jacob, et al.
Pubblicazione: (2025)

Identification of Multivariate Measurement Error Models
di: Hu, Yingyao
Pubblicazione: (2025)

Prediction-Guided Active Experiments
di: Ao, Ruicheng, et al.
Pubblicazione: (2024)

Adaptive Experimental Design for Policy Learning
di: Kato, Masahiro, et al.
Pubblicazione: (2024)

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

Optimal Conditional Inference in Adaptive Experiments
di: Chen, Jiafeng, et al.
Pubblicazione: (2023)

Adaptive maximization of social welfare
di: Cesa-Bianchi, Nicolo, et al.
Pubblicazione: (2023)

An Empirical Risk Minimization Approach for Offline Inverse RL and Dynamic Discrete Choice Model
di: Kang, Enoch H., et al.
Pubblicazione: (2025)

Identification and Estimation of Simultaneous Equation Models Using Higher-Order Cumulant Restrictions
di: Jiang, Ziyu
Pubblicazione: (2025)

Higher-Order Causal Message Passing for Experimentation with Complex Interference
di: Bayati, Mohsen, et al.
Pubblicazione: (2024)

Testing Effect Homogeneity and Confounding in High-Dimensional Experimental and Observational Studies
di: Armendariz, Ana, et al.
Pubblicazione: (2026)

Neighborhood Adaptive Estimators for Causal Inference under Network Interference
di: Belloni, Alexandre, et al.
Pubblicazione: (2022)

PPI-SVRG: Unifying Prediction-Powered Inference and Variance Reduction for Semi-Supervised Optimization
di: Ao, Ruicheng, et al.
Pubblicazione: (2026)

A Job I Like or a Job I Can Get: Designing Job Recommender Systems Using Field Experiments
di: Bied, Guillaume, et al.
Pubblicazione: (2026)

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

A Granular Framework for Construction Material Price Forecasting: Econometric and Machine-Learning Approaches
di: Lyu, Boge, et al.
Pubblicazione: (2025)

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

Data Fusion for Partial Identification of Causal Effects
di: Lanners, Quinn, et al.
Pubblicazione: (2025)

Two Approaches to Direct Estimation of Riesz Representers
di: Bruns-Smith, David
Pubblicazione: (2026)

A Double Machine Learning Approach to Combining Experimental and Observational Data
di: Parikh, Harsh, et al.
Pubblicazione: (2023)

Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing Approach
di: Carlson, Jacob
Pubblicazione: (2025)

Identification of Average Treatment Effects in Nonparametric Panel Models
di: Athey, Susan, et al.
Pubblicazione: (2025)

Learning Nonlinear Factor Models with Unknown Monotone Links from Incomplete and Noisy Data
di: Chao, Yutong, et al.
Pubblicazione: (2026)

Beating the Winner's Curse via Inference-Aware Policy Optimization
di: Bastani, Hamsa, et al.
Pubblicazione: (2025)

Identification and estimation for matrix time series CP-factor models
di: Chang, Jinyuan, et al.
Pubblicazione: (2024)

Conformal Prediction: a Unified Review of Theory and New Challenges
di: Fontana, Matteo, et al.
Pubblicazione: (2020)

From Reviews to Actionable Insights: An LLM-Based Approach for Attribute and Feature Extraction
di: Boughanmi, Khaled, et al.
Pubblicazione: (2025)