惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

A
Arctic Wolf
V
V2EX
P
Proofpoint News Feed
The Hacker News
The Hacker News
GbyAI
GbyAI
G
Google Developers Blog
S
Schneier on Security
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
W
WeLiveSecurity
Security Archives - TechRepublic
Security Archives - TechRepublic
博客园 - Franky
Recent Announcements
Recent Announcements
腾讯CDC
Hacker News - Newest:
Hacker News - Newest: "LLM"
K
Kaspersky official blog
U
Unit 42
Engineering at Meta
Engineering at Meta
J
Java Code Geeks
Google Online Security Blog
Google Online Security Blog
Last Week in AI
Last Week in AI
V
Vulnerabilities – Threatpost
N
News and Events Feed by Topic
O
OpenAI News
量子位
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
Y
Y Combinator Blog
博客园 - 【当耐特】
Vercel News
Vercel News
Hacker News: Ask HN
Hacker News: Ask HN
T
Tor Project blog
Apple Machine Learning Research
Apple Machine Learning Research
Microsoft Security Blog
Microsoft Security Blog
Exploit-DB.com RSS Feed
Exploit-DB.com RSS Feed
AWS News Blog
AWS News Blog
MongoDB | Blog
MongoDB | Blog
S
Security Affairs
A
About on SuperTechFans
Project Zero
Project Zero
D
Darknet – Hacking Tools, Hacker News & Cyber Security
博客园 - 聂微东
Webroot Blog
Webroot Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Cloudbric
Cloudbric
T
Tenable Blog
月光博客
月光博客
C
Check Point Blog
宝玉的分享
宝玉的分享
V
Visual Studio Blog
T
The Blog of Author Tim Ferriss
NISL@THU
NISL@THU

stat.ML updates on arXiv.org

A Polyak-Ruppert Central Limit Theorem for SA-Adam with Momentum and Non-Convergent Adaptive Preconditioning Non-asymptotic Tail Bounds for the Kostlan--Shub--Smale Field: Tensor PCA and Spherical $k$-Spin Complexity Conformal Prediction Intervals with Tail-Specific Guarantees Another Look at Log-PCA for Probability Measures: A Dynamical Formulation and Statistical Convergence Tight $L_\infty$ Sample Complexity for Low-Degree and Sparse Boolean Polynomials Bounded Difference Concentration for Infinitely Exchangeable Sequences with Applications to AI Benchmark Uncertainty A Bayesian Boolean Matrix Factorization with Application to Copy Number Analysis in Cancer Geometrical fairness in graph neural networks Proximal Mediation Analysis with Hidden Recanting Witnesses Differential Privacy of Gaussian Process Posterior Sampling Fast Nonparametric Conditional Independence Testing via Two-Stage Regression Tensor-based second-order causal discovery A Diffusion Approximation for Temporal-Difference Learning with Linear Features under Markovian Noise Finsler Geometry, Graph Neural Networks, and You Finite-Time Queue Peak Laws in Stochastic Networks: Logarithmic Scaling After Geometric Thresholds Sum-of-Squares Degree Barriers for the Reweighted-Hinge Method in Robust Halfspace Learning: A Christoffel-Function Characterization Uncertainty Quantification of Engineering Structures by Polynomial Chaos Expansion and Multivariate Active Learning Accelerated Convex Optimization via Hamiltonian Dynamics with Deterministic Integration Time Bayesian Poisson-Randomized Gamma Tensor Factorization with Application to International Trade Flows Kernel-Based Functional Balancing for Causal Inference with Compositional Treatments Model Validation of Agentic AI Systems: A POMDP-Based Framework for Belief-State, Forecast, and Policy Validation Martingale Doppelgänger-Eval: An Identification Framework for Auditing Candlestick Understanding in Vision-Language Models Anytime-valid Optimal Policy Identification FoundCause: Causal Discovery with Latent Confounders from Observational Data Randomized Midpoint Method for Log-Concave Sampling under Constraints Learning Survival Models with Right-Censored Reporting Delays Nested Sampling: A Critical and Comprehensive Theoretical Guide Understanding Long-Term Dynamics of Individual Metro Usage: A Hidden Semi-Markov State Framework with Survival Analysis Questioning the Coverage-Length Metric in Conformal Prediction: When Shorter Intervals Are Not Better Maximin Relative Improvement: Fair Learning as a Bargaining Problem MA-SBI: Misspecification-Aware Simulation-Based Inference via Side-Channel Guidance Policy Regret for Embedding Model Routing: Contextual Bandits with Low-Rank Experts LLMs on Tabular Data with Limited Semantics: Evidence from Industrial Car Retrofit Prediction Service-Induced Congestion in Memory-Constrained LLM Serving Proximal Policy Optimization for Amortized Discrete Sampling Topological Flow Matching Attention is Just Another Name for Coupling?: A Fast-Slow ODE Perspective on Hierarchical Pretraining Phantoms and Disclosures: a Causal Framework for Auditing Synthetic Data Learning a Sampling-Free Variational DNN Plugin from Tiny Training Sets to Refine OOD Segmentation With Uncertainty Estimation Exact Posterior Score Estimation for Solving Linear Inverse Problems Bridging data-driven priors via the score function for posterior sampling -- Comparative review and experimental study Audited Conformal Prediction for Classification under Unknown Distribution Shift A Koopman-PINN Framework for Epidemic Models: Parameter Inference and Forecasting Conformal Candidate Certification for Offline Model-Based Optimization Finite Resources False Discovery Rate Control in Structured Hypothesis Spaces The Reverse Telescoping Coordinate System for Positive Definite Matrices: Geometry, Computation, and Generative Modeling Structured Nonparametric Variational Inference for Dependent Latent Modeling Ricci-Filtration: Boosting Retrieval-Augmented Generation Reranker to Query-Answer Tasks by Discrete Ricci Flow Phase Transition in Convex Relaxations for Graph Alignment Information Gap and Feasibility-Aware Inference in Binomial Logistic Mixtures Stochastic trace estimation with tensor train random vectors Score-Based Martingale Posteriors for Deep Neural Networks Amortized mean-shift interacting particles Spectral Adaptive Conformal Prediction for Structured Non-Exchangeable Data Representation Costs in Data Science: Foundations and the Quasi-Banach Spaces of Deep Neural Networks PromptShift-CRC: Drift-Aware Conformal Risk Control for Foundation Models Under Prompt and Domain Shift Closing the Approximation Gap in Simulation-free Latent SDEs On the Geometry of Separation in Finite Gaussian Mixtures Generative Modeling on Metric Graphs via Neural Optimal Transport Diffusion Flow Matching: Dimension-Improved KL Bounds and Wasserstein Guarantees Spectral Sparsification of Laplacian-Constrained Gaussian and Hüsler-Reiss Graphical Models Optimal Multiscale Learning of Linear Operators A nonparametric two-sample test using a parametric integral probability metric Sobolev Approximation by Fixed-Size Neural Networks with Arbitrary Accuracy Dynestyx: A Probabilistic Programming Library for Dynamical Systems Learning the Geometry of Data: A Mathematical Review of Shape Space Analysis Learning Topological Representations for Molecular Dynamics Event Generation with Parallel Langevin Sampling and Learned Stein Diagnostics PHINN: Persistent Homology Inspired Neural Network for Rare-Event Time Series Generation A Decision-Theoretic View of Test-Time Training: When, How Far, and Which Directions to Adapt The Data Manifold under the Microscope The limits of interpretability in multiple linear regression Stop the Sampler! Classifier-Based Adaptive Stopping for Sampling Kernels One-Step Generalization Ratio Guided Optimization for Domain Generalization Scalable and Interpretable Representation Alignment with Ordinal Similarity Neural Bayesian Anomaly Mitigation: A Robust Loss that Doubles as an Unsupervised Contamination Classifier Generative Predictive Distributions for Time Series Functional Gradient Descent with Adaptive Representations Filtered Conformal Ellipsoids for Graph-Native Time Series Information Leakage Detection through Approximate Bayes-optimal Prediction TrIM: Transformed Iterative Mondrian Forests for Gradient-based Dimension Reduction and High-Dimensional Regression Optimality in importance sampling: a gentle survey Discrimination-free Insurance Pricing with Privatized Sensitive Attributes Enhancing Visual Feature Attribution via Weighted Integrated Gradients Optimal structure learning and conditional independence testing Kernel Two-Sample Testing via Directional Components Analysis Q-Learning with Fine-Grained Gap-Dependent Regret In-Context Learning Is Provably Bayesian Inference: A Generalization Theory for Meta-Learning deFOREST: Fusing Optical and Radar satellite data for Enhanced Sensing of Tree-loss Matching correlated VAR time series Drivers, Receivers, and Dynamic Linkages: The Directed Structure of SDG Interdependence, 2000--2024 Best Arm Identification with Minimal Regret Constraining the outputs of ReLU neural networks On the Benefits of Weight Normalization for Overparameterized Matrix Sensing Localized Kernel Projection Outlyingness: A Two-Stage Approach for Multi-Modal Outlier Detection Random Gradient-Free Optimization in Infinite Dimensional Spaces Forecasting the U.S. Treasury Yield Curve: A Distributionally Robust Machine Learning Approach for Interest Rate Risk Management Fast Non-Episodic Finite-Horizon RL with K-Step Lookahead Thresholding Sharp analysis of linear ensemble sampling CADO: From Imitation to Cost Minimization for Heatmap-based Solvers in Combinatorial Optimization
On Response-Adaptive Targeting Strategies for Multi-Treatment Experiments
[Submitted on 16 Jun 2026] · 2026-06-17 · via stat.ML updates on arXiv.org

View PDF HTML (experimental)

Abstract:Response-adaptive randomization (RAR) in clinical trials aims to improve ethical and statistical efficiency by dynamically allocating patients to treatments based on observed outcomes. While RAR based on a target optimal allocation have been extensively studied for two-arms settings, their extension to multi-treatment experiments ($K \geq 2$) remains theoretically fragmented, with most existing methods focusing on specific algorithms or restricted target allocations. In this paper, we introduce a unified framework for response-adaptive targeting, the $\alpha$-Rebalancing Targeting Strategies ($\alpha$RTS), which generalize the ERADE two-armed strategy of Hu et al. [2009]. We prove that all designs in this family share fundamental asymptotic properties: strong consistency, asymptotic normality of allocation proportions and treatment effect estimators, and asymptotic efficiency. To address sparse target regimes (where some treatments are asymptotically eliminated), we further propose $\alpha$RTS with Forced Exploration, a variant that guarantees infinite sampling for all treatments while preserving the asymptotic guarantees. Extensive simulations illustrate the finite-sample behavior of $\alpha$RTS variants in a 3-armed context, highlighting in particular the critical role of forced exploration in sparse settings.

Submission history

From: Redouane Yagouti [view email]
[v1] Tue, 16 Jun 2026 10:51:50 UTC (278 KB)