Sequential Hypothesis Testing

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9 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Anytime-valid simulation-based hypothesis testing
Oct 6, 2026Patrick Forré, Lydia BrennerSimulation-Based Inference Sequential Hypothesis Testing
University of Amsterdam · Nikhef
Valid Stopping in Adaptive Generator-Verifier Loops
Oct 5, 2026Mahmoud Hegazy, Michael I. Jordan, Aymeric DieuleveutFDR Control E-Values
CMAP, École polytechnique, IP Paris, France · Inria Paris, France · EECS, UC Berkeley, USA
Sequential Capacity of Quantum Processes with Finite Memory
Oct 1, 2026Yibin WangSequential Hypothesis Testing
Graduate School of Mathematics, Nagoya University, Nagoya, 464-8601, Aichi, Japan.
Prequential E-Values for Selected-GP Near-Optimality Certificates
Sep 30, 2026Ami Tavory, Noa CohenBlack-Box Optimization Anytime-Valid Inference
Meta Platforms
Always-On Experimentation
Sep 30, 2026Ricardo J. Sandoval, David Arbour, Avi Feller +1FDR Control A/b Testing
University of California, Berkeley · Adobe Research · Inria, Paris
PEAR: Progressive Evidence-Based AutoResearch for Industrial Search Systems
Sep 28, 2026Yifan Wang, Shipeng Zhu, Fei Xiong +6Information Retrieval Sequential Hypothesis Testing
Global E-Commerce Agentic Search Team
ICMAPE: In-Context Multiagent Pure Exploration
Sep 27, 2026Xinyi Hu, Alessio Russo, Aldo PacchianoRL Exploration Decentralized MARL
Boston University · Broad Institute of MIT and Harvard
The Price of Peeking: Anytime-Valid Leakage Detection on ML-KEM EM Traces
Sep 27, 2026Georgios Feretzakis, Alexandros PapaspyridisAnytime-Valid Inference Sequential Hypothesis Testing
Human-AI-Powered Hypothesis Testing: Cost-Aware Selective AI Scoring and Sequential Human Escalation
Sep 24, 2026Dae Woong, Ham, Xuejun Zhao +2Cost-Aware Inference Human-in-the-Loop Evaluation
David · University of Michigan, Ann Arbor, MI 48109, USA · University of North Carolina at Charlotte, Charlotte, NC 28223, USA
How Many Posterior Samples? Calibrated Stopping for Adaptive Sensing
Sep 18, 2026Vincent Corlay, Andriy EnttselActive Perception Optimal Stopping
Mitsubishi Electric R&D Centre Europe
Predictive Likelihood Ratios for Language Model Watermark Detection
Sep 14, 2026Li MaBayesian Inference LLM Watermarking
Department of Statistics and Data Science Institute, University of Chicago
Deciding When to Switch: E-Processes for Adaptive Minimax Training for Generative Adversarial Nets
Aug 10, 2026Hyunjoo Kim, Sicheng Wu, Agastya Venkatraman +2Min-Max Optimization Sequential Hypothesis Testing
Department of Statistics, Ewha Womans University · Department of Mathematics, Purdue University · Department of Computer Science, Purdue University
From Approachability Residuals to Anytime-Valid Evidence: The Online Convex Geometry of Testing by Betting
Aug 10, 2026Jinze ZhaoSequential Hypothesis Testing
University of California, San Diego
Efficient Online LLM Watermark Detection via Rao-Blackwellized E-Processes
Jul 24, 2026Lu Luo, Dandan Mo, Chengdong Xu +4LLM Watermarking Sequential Hypothesis Testing
Yunnan Key Laboratory of Statistical Modeling and Data Analysis, Yunnan University, Kunming, China · School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China
Detect Early, Escalate Rarely: Anytime Detection of AI-Generated Video from the Compressed Bitstream
Jul 21, 2026Mert Onur Cakiroglu, Mehmet Dalkilic, Hasan KurbanDeepfake Detection Sequential Hypothesis Testing
Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, USA · College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
Aggregation of Statistical Evidence under Exchangeability
Jul 17, 2026Antonin Schrab, Rajen Shah, Arthur Gretton +1Sequential Hypothesis Testing Multiple Hypothesis Testing
Department of Computer Science and Technology, University of Cambridge, UK · Statistical Laboratory, University of Cambridge, UK · Gatsby Computational Neuroscience Unit, University College London, UK +1
Bet on Features: Anytime-Valid and Feature-Aware Auditing of Conditional Quantile Forecasters
Jul 13, 2026Ivane Antonov, Sohom Mukherjee, Richard Pibernik +1Quantile Regression Sequential Hypothesis Testing
Julius-Maximilians-Universität Würzburg · INSEAD
Stop Guessing When to Stop Testing: Efficient Model Evaluation with Just Enough Data
Jul 9, 2026Ofir Arviv, Kristjan Greenewald, Yotam Perlitz +3Early Stopping Optimal Stopping
IBM Research
Sequential Fairness Auditing with Limited Output Access
Jun 29, 2026Ioannis Pitsiorlas, Martha V. Sourla, Marios KountourisAlgorithmic Fairness Group Fairness
EURECOM, France · DaSCI, University of Granada, Spain
Bayesian control for coding agents
Jun 23, 2026Theodore Papamarkou, Vladislav Smirnov, Viktor Mazanov +4Language Model-Based Control Coding Agents
1PolyShape · 3MBZUAI
Ranking-and-Selection with Multiple Correct Answers and Non-Answerable Estimates
Jun 20, 2026Qiaoqiao Wang, Wei YouBest-Arm Identification Adaptive Sampling
Dept. of Industrial Engineering and Decision Analytics, The Hong Kong University of Science and Technology, Hong Kong, China
Betting on Moments: Legendre Jumper Martingales for Online Exchangeability Testing
Jun 18, 2026Johan Hallberg SzabadváryConformal Prediction Distribution Shift Detection
Department of Mathematics, Stockholm University
Sequential Kernel-based Conditional Independence Testing via Adaptive Betting
Jun 17, 2026Zheng He, Danica J. SutherlandKernel Methods Conditional Independence Testing
Department of Computer Science, University of British Columbia, Vancouver, Canada · Alberta Machine Intelligence Institute, Edmonton, Canada
PACE: Anytime-Valid Acceptance Tests for Self-Evolving Agents
Jun 6, 2026Zayx ShawnSequential Hypothesis Testing LLM Agent Reliability
Independent Researcher
Correcting Split Selection in Online Decision Trees via Anytime-Valid Inference
May 29, 2026Salim I. Amoukou, Saumitra Mishra, Manuela VelosoAnytime-Valid Inference Decision Tree Learning
J.P. Morgan AI Research.
Optimal Rates for Differentially Private Hypothesis Testing with E-values
May 27, 2026Ben Jacobsen, Tomas Gonzalez, Gavin Brown +2E-Values Differential Privacy
University of Wisconsin-Madison · Carnegie Mellon University · University of Wisconsin–Madison
Semi-Supervised Hypothesis Testing by Betting on Predictions
May 27, 2026Yaniv Tenzer, Elad Tolochinsky, Yaniv RomanoE-Values Semi-Supervised Learning
Department of Computer Science, Technion – Israel Institute of Technology · Department of Electrical and Computer Engineering, Technion – Israel Institute of Technology
EviTrack: Selection over Sampling for Delayed Disambiguation
May 19, 2026Omer HaqSequential Hypothesis Testing Sequence Modeling
Independent Researcher · Baltimore, MD, USA
Sequential Consensus for Multi-Agent LLM Debates: A Wald-SPRT compute governor with calibration-based failure detection
May 18, 2026Andrea MorandiLLM Inference Efficiency Multi-Agent Debate
Cisco Systems, Inc.
A Regret Perspective on Online Multiple Testing
May 13, 2026Qingyang Hao, Kongchang Zhou, Fang Kong +1FDR Control Regret Minimization
Southern University of Science and Technology

Oct 6, 2026, stat.MLJ/K move · Enter open · S save

Anytime-valid simulation-based hypothesis testing

Patrick Forré, Lydia Brenner

University of Amsterdam · Nikhef

Simulation-Based Inference Sequential Hypothesis Testing

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For a given data distribution

(X_t)_{t \in \mathbb{N}} \sim Q

i.i.d., we investigate the hypothesis testing problem:

H_0: Q = P_0

vs.

H_1: Q = P_1

, for two different model probability distributions

P_0

and

P_1

. In contrast to the standard setting, where analytic densities

p_0

and

p_1

are given, here, we consider the density-free setting, where we only have access to i.i.d. simulations

(Z^0_t)_{t \in \mathbb{N}} \sim P_0

and

(Z^1_t)_{t \in \mathbb{N}} \sim P_1

. For this simulation-based hypothesis testing setting, we construct an e-test martingale, resulting in a sequential test with anytime-valid type-I error guarantees, approximate growth optimality, geometrically decaying type-II error bounds, and asymptotic power one. Most ingredients used in our constructions are variants of well known concepts. The value of this paper lies in the compact presentation of an effective, anytime-valid solution for the density-free simulation-based sequential hypothesis testing case.

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