Cramming Contextual Bandits for On-policy Statistical Evaluation
Fuente:
arXiv
Saved in:
| Main Authors: | Jia, Zeyang, Imai, Kosuke, Li, Michael Lingzhi |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
by: Imai, Kosuke, et al.
Published: (2022)
by: Imai, Kosuke, et al.
Published: (2022)
Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules
by: Li, Michael Lingzhi, et al.
Published: (2024)
by: Li, Michael Lingzhi, et al.
Published: (2024)
Statistical Performance Guarantee for Subgroup Identification with Generic Machine Learning
by: Li, Michael Lingzhi, et al.
Published: (2023)
by: Li, Michael Lingzhi, et al.
Published: (2023)
Individualized Policy Evaluation and Learning under Clustered Network Interference
by: Zhang, Yi, et al.
Published: (2023)
by: Zhang, Yi, et al.
Published: (2023)
GenAI-Powered Inference
by: Imai, Kosuke, et al.
Published: (2025)
by: Imai, Kosuke, et al.
Published: (2025)
Linear Contextual Bandits with Interference
by: Xu, Yang, et al.
Published: (2024)
by: Xu, Yang, et al.
Published: (2024)
Comment on "Generic machine learning inference on heterogeneous treatment effects in randomized experiments."
by: Imai, Kosuke, et al.
Published: (2025)
by: Imai, Kosuke, et al.
Published: (2025)
Minimax Regret Estimation for Generalizing Heterogeneous Treatment Effects with Multisite Data
by: Zhang, Yi, et al.
Published: (2024)
by: Zhang, Yi, et al.
Published: (2024)
High-dimensional Nonparametric Contextual Bandit Problem
by: Iwazaki, Shogo, et al.
Published: (2025)
by: Iwazaki, Shogo, et al.
Published: (2025)
Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis
by: Ham, Dae Woong, et al.
Published: (2022)
by: Ham, Dae Woong, et al.
Published: (2022)
Locally Private Nonparametric Contextual Multi-armed Bandits
by: Ma, Yuheng, et al.
Published: (2025)
by: Ma, Yuheng, et al.
Published: (2025)
Sampling as Bandits: Evaluation-Efficient Design for Black-Box Densities
by: Matsubara, Takuo, et al.
Published: (2025)
by: Matsubara, Takuo, et al.
Published: (2025)
Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment
by: Ben-Michael, Eli, et al.
Published: (2021)
by: Ben-Michael, Eli, et al.
Published: (2021)
Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits
by: Zhan, Ruohan, et al.
Published: (2021)
by: Zhan, Ruohan, et al.
Published: (2021)
Attack-Resistant Uniform Fairness for Linear and Smooth Contextual Bandits
by: Zhang, Qingwen, et al.
Published: (2026)
by: Zhang, Qingwen, et al.
Published: (2026)
Optimal Multitask Linear Regression and Contextual Bandits under Sparse Heterogeneity
by: Huang, Xinmeng, et al.
Published: (2023)
by: Huang, Xinmeng, et al.
Published: (2023)
Tree Bandits for Generative Bayes
by: O'Hagan, Sean, et al.
Published: (2024)
by: O'Hagan, Sean, et al.
Published: (2024)
Bayesian Safe Policy Learning with Chance Constrained Optimization: Application to Military Security Assessment during the Vietnam War
by: Jia, Zeyang, et al.
Published: (2023)
by: Jia, Zeyang, et al.
Published: (2023)
Statistical Inference for Online Algorithms
by: Carter, Selina, et al.
Published: (2025)
by: Carter, Selina, et al.
Published: (2025)
Robust Tensor Regression with Nonconvexity: Algorithmic and Statistical Theory
by: Song, Zihao, et al.
Published: (2026)
by: Song, Zihao, et al.
Published: (2026)
A Frequentist Statistical Introduction to Variational Inference, Autoencoders, and Diffusion Models
by: Chen, Yen-Chi
Published: (2025)
by: Chen, Yen-Chi
Published: (2025)
Off-policy Evaluation in Doubly Inhomogeneous Environments
by: Bian, Zeyu, et al.
Published: (2023)
by: Bian, Zeyu, et al.
Published: (2023)
Multi-Armed Bandits with Network Interference
by: Agarwal, Abhineet, et al.
Published: (2024)
by: Agarwal, Abhineet, et al.
Published: (2024)
Statistical Guarantees in the Search for Less Discriminatory Algorithms
by: Hays, Chris, et al.
Published: (2025)
by: Hays, Chris, et al.
Published: (2025)
Collaborative Contextual Bayesian Optimization
by: Chang, Chih-Yu, et al.
Published: (2026)
by: Chang, Chih-Yu, et al.
Published: (2026)
Statistical learning for constrained functional parameters in infinite-dimensional models
by: Nabi, Razieh, et al.
Published: (2024)
by: Nabi, Razieh, et al.
Published: (2024)
Nearly Optimal Best Arm Identification for Semiparametric Bandits
by: Kim, Seok-Jin
Published: (2026)
by: Kim, Seok-Jin
Published: (2026)
Summary Statistics of Large-scale Model Outputs for Observation-corrected Outputs
by: Chakraborty, Atlanta, et al.
Published: (2025)
by: Chakraborty, Atlanta, et al.
Published: (2025)
Contextual Linear Optimization with Partial Feedback
by: Hu, Yichun, et al.
Published: (2024)
by: Hu, Yichun, et al.
Published: (2024)
Statistical inference after variable selection in Cox models: A simulation study
by: Schemet, Lena, et al.
Published: (2026)
by: Schemet, Lena, et al.
Published: (2026)
The Traveling Bandit: A Framework for Bayesian Optimization with Movement Costs
by: Chen, Qiyuan, et al.
Published: (2024)
by: Chen, Qiyuan, et al.
Published: (2024)
High-Dimensional Linear Bandits under Stochastic Latent Heterogeneity
by: Chen, Elynn, et al.
Published: (2025)
by: Chen, Elynn, et al.
Published: (2025)
An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits
by: Liang, Biyonka, et al.
Published: (2023)
by: Liang, Biyonka, et al.
Published: (2023)
Proximal Projection for Doubly Sparse Regularized Models
by: He, Jia Wei, et al.
Published: (2026)
by: He, Jia Wei, et al.
Published: (2026)
Causal Bandit Over Unknown Graphs: Upper Confidence Bounds With Backdoor Adjustment
by: Zhao, Yijia, et al.
Published: (2025)
by: Zhao, Yijia, et al.
Published: (2025)
Generator-Mediated Bandits: Thompson Sampling for GenAI-Powered Adaptive Interventions
by: Brooks, Marc, et al.
Published: (2025)
by: Brooks, Marc, et al.
Published: (2025)
High-Dimensional Statistics: Reflections on Progress and Open Problems
by: Maleki, Arian, et al.
Published: (2026)
by: Maleki, Arian, et al.
Published: (2026)
Contextual Online Uncertainty-Aware Preference Learning for Human Feedback
by: Lu, Nan, et al.
Published: (2025)
by: Lu, Nan, et al.
Published: (2025)
Spiking the training data to correct for test set contamination
by: Wei, Johnny Tian-Zheng, et al.
Published: (2026)
by: Wei, Johnny Tian-Zheng, et al.
Published: (2026)
Active Statistical Inference
by: Zrnic, Tijana, et al.
Published: (2024)
by: Zrnic, Tijana, et al.
Published: (2024)
Similar Items
-
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
by: Imai, Kosuke, et al.
Published: (2022) -
Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules
by: Li, Michael Lingzhi, et al.
Published: (2024) -
Statistical Performance Guarantee for Subgroup Identification with Generic Machine Learning
by: Li, Michael Lingzhi, et al.
Published: (2023) -
Individualized Policy Evaluation and Learning under Clustered Network Interference
by: Zhang, Yi, et al.
Published: (2023) -
GenAI-Powered Inference
by: Imai, Kosuke, et al.
Published: (2025)