Cover Image

Exploration in the Limit

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Cho, Brian M., Kallus, Nathan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Statistics Theory Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Demistifying Inference after Adaptive Experiments
by: Bibaut, Aurélien, et al.
Published: (2024)

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

Nonparametric Instrumental Variable Inference with Many Weak Instruments
by: van der Laan, Lars, et al.
Published: (2025)

Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands
by: van der Laan, Lars, et al.
Published: (2025)

Efficient Inference after Directionally Stable Adaptive Experiments
by: Shen, Zikai, et al.
Published: (2026)

Nonparametric Instrumental Variable Analysis Without Structural Equations: Debiased Inference on Functionals of Inverse Problems with No Solutions
by: Shen, Zikai, et al.
Published: (2026)

Anytime-Valid Continuous-Time Confidence Processes for Inhomogeneous Poisson Processes
by: Lindon, Michael, et al.
Published: (2024)

Reward Maximization for Pure Exploration: Minimax Optimal Good Arm Identification for Nonparametric Multi-Armed Bandits
by: Cho, Brian, et al.
Published: (2024)

Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data Streams
by: Cho, Brian, et al.
Published: (2024)

Efficient Adaptive Experimentation with Noncompliance
by: Oprescu, Miruna, et al.
Published: (2025)

Simulation-Based Inference for Adaptive Experiments
by: Cho, Brian M, et al.
Published: (2025)

GAAVI: Global Asymptotic Anytime Valid Inference for the Conditional Mean Function
by: Cho, Brian M, et al.
Published: (2026)

The Fundamental Limits of Structure-Agnostic Functional Estimation
by: Balakrishnan, Sivaraman, et al.
Published: (2023)

Random Dot Product Graphs as Dynamical Systems: Limitations and Opportunities
by: Riva, Giulio Valentino Dalla
Published: (2026)

Fast kernel methods: Sobolev, physics-informed, and additive models
by: Doumèche, Nathan, et al.
Published: (2025)

Fundamental Computational Limits in Pursuing Invariant Causal Prediction and Invariance-Guided Regularization
by: Gu, Yihong, et al.
Published: (2025)

The Honest Truth About Causal Trees: Accuracy Limits for Heterogeneous Treatment Effect Estimation
by: Cattaneo, Matias D., et al.
Published: (2025)

A general framework for inference on algorithm-agnostic variable importance
by: Williamson, Brian D., et al.
Published: (2020)

Information-Theoretic Limits and Strong Consistency on Binary Non-uniform Hypergraph Stochastic Block Models
by: Wang, Hai-Xiao
Published: (2023)

Conformal Prediction with Time-Series Data via Sequential Conformalized Density Regions
by: Sampson, M., et al.
Published: (2026)

The Central Role of the Loss Function in Reinforcement Learning
by: Wang, Kaiwen, et al.
Published: (2024)

Clustered Switchback Designs for Experimentation Under Spatio-temporal Interference
by: Jia, Su, et al.
Published: (2023)

Distributional Treatment Effect Estimation across Heterogeneous Sites via Optimal Transport
by: Bateni, Borna, et al.
Published: (2025)

Conformal Prediction under Levy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbations
by: Aolaritei, Liviu, et al.
Published: (2025)

Strongly Consistent Community Detection in Popularity Adjusted Block Models
by: Yuan, Quan, et al.
Published: (2025)

The BdryMatérn GP: Reliable incorporation of boundary information on irregular domains for Gaussian process modeling
by: Ding, Liang, et al.
Published: (2025)

What is a good matching of probability measures? A counterfactual lens on transport maps
by: De Lara, Lucas, et al.
Published: (2025)

Hierarchical Bayesian Operator-induced Symbolic Regression Trees for Structural Learning of Scientific Expressions
by: Roy, Somjit, et al.
Published: (2025)

Statistical Inference for Manifold Similarity and Alignability across Noisy High-Dimensional Datasets
by: Chen, Hongrui, et al.
Published: (2025)

Optimal Bias-variance Tradeoff in Matrix and Tensor Estimation
by: Kumar, Shivam, et al.
Published: (2025)

Minimax-Optimal Two-Sample Test with Sliced Wasserstein
by: Tran, Binh Thuan, et al.
Published: (2025)

Interpretable Multivariate Conformal Prediction with Fast Transductive Standardization
by: Fan, Yunjie, et al.
Published: (2025)

Design Stability in Adaptive Experiments: Implications for Treatment Effect Estimation
by: Sengupta, Saikat, et al.
Published: (2025)

An introduction to Causal Modelling
by: Baishya, Gauranga Kumar
Published: (2025)

Geometric Calibration and Neutral Zones for Uncertainty-Aware Multi-Class Classification
by: Das, Soumojit, et al.
Published: (2025)

Optimal Nuisance Function Tuning for Estimating a Doubly Robust Functional under Proportional Asymptotics
by: McGrath, Sean, et al.
Published: (2025)

Hierarchical Clustering With Confidence
by: Wu, Di, et al.
Published: (2025)

Theoretical guarantees for change localization using conformal p-values
by: Bhattacharyya, Swapnaneel, et al.
Published: (2025)

Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spaces
by: Lugosi, Gábor, et al.
Published: (2025)

Optimal Differentially Private Ranking from Pairwise Comparisons
by: Cai, T. Tony, et al.
Published: (2025)