Two-sample testing

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

5 leaderboard tables shown for this task, 5 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 84 papers with code (338 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• PacGAN: The power of two samples in generative adversarial networks
  12 Dec 2017 7 repositories listed
  Generative adversarial networks (GANs) are innovative techniques for learning generative models of complex data distributions from samples.
• Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing
  14 Dec 2018 5 repositories listed
  We thus first propose a measure of `sensitivity' and show empirically that normal samples and adversarial samples have distinguishable sensitivity.
• hyppo: A Multivariate Hypothesis Testing Python Package
  3 Jul 2019 4 repositories listed
  We introduce hyppo, a unified library for performing multivariate hypothesis testing, including independence, two-sample, and k-sample testing.
• Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
  9 Jun 2019 4 repositories listed
  Based on automatic deep learning segmentations, we extracted three features which quantify two-dimensional and three-dimensional characteristics of the tumors.
• AutoML Two-Sample Test
  17 Jun 2022 3 repositories listed Syntology ran 10 of 11 samples · 1 unverified · 2 pointer-only (licence)
  Two-sample tests are important in statistics and machine learning, both as tools for scientific discovery as well as to detect distribution shifts.
• MMD Aggregated Two-Sample Test
  28 Oct 2021 3 repositories listed Syntology ran 4 of 15 samples · 11 unverified
  In practice, this parameter is unknown and, hence, the optimal MMD test with this particular kernel cannot be used.
• Gaussian Differential Privacy
  7 May 2019 3 repositories listed
  More precisely, the privacy guarantees of \emph{any} hypothesis testing based definition of privacy (including original DP) converges to GDP in the limit under composition.
• Generative Moment Matching Networks
  10 Feb 2015 3 repositories listed
  We consider the problem of learning deep generative models from data.
• Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inference
  5 Oct 2020 2 repositories listed Syntology ran 4 of 8 samples · 4 unverified · 8 pointer-only (licence)
  To overcome this difficulty, we introduce a conditional selective inference (SI) framework -- a new statistical inference framework for data-driven hypotheses that has recently received considerable attention -- to…
• Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation
  23 Jun 2020 2 repositories listed Syntology ran 8 of 11 samples · 3 unverified · 11 pointer-only (licence)
  However, the adoption of such policies in practice is often challenging, as they are hard to interpret within the application context, and lack measures of uncertainty for the learned policy value and its decisions.
• AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
  18 Jun 2020 2 repositories listed Syntology ran 5 of 20 samples · 15 unverified
  We present an improved method for symbolic regression that seeks to fit data to formulas that are Pareto-optimal, in the sense of having the best accuracy for a given complexity.
• Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting
  24 Feb 2020 2 repositories listed
  In this paper, we present ACORE (Approximate Computation via Odds Ratio Estimation), a frequentist approach to LFI that first formulates the classical likelihood ratio test (LRT) as a parametrized classification…
• Decision-Making with Auto-Encoding Variational Bayes
  17 Feb 2020 2 repositories listed
  To make decisions based on a model fit with auto-encoding variational Bayes (AEVB), practitioners often let the variational distribution serve as a surrogate for the posterior distribution.
• Comparing distributions: ℓ₁ geometry improves kernel two-sample testing
  19 Sep 2019 2 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  Here, we show that Lᵖ distances (with p≥1) between these distribution representatives give metrics on the space of distributions that are well-behaved to detect differences between distributions as they metrize the weak…
• Scalable and Efficient Hypothesis Testing with Random Forests
  16 Apr 2019 2 repositories listed
  Throughout the last decade, random forests have established themselves as among the most accurate and popular supervised learning methods.
• Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
  29 Oct 2018 2 repositories listed Syntology ran 2 of 6 samples · 4 unverified
  We might hope that when faced with unexpected inputs, well-designed software systems would fire off warnings.
• A Differentially Private Kernel Two-Sample Test
  1 Aug 2018 2 repositories listed
  As a result, a simple chi-squared test is obtained, where a test statistic depends on a mean and covariance of empirical differences between the samples, which we perturb for a privacy guarantee.
• Statistical Anomaly Detection via Composite Hypothesis Testing for Markov Models
  27 Feb 2017 2 repositories listed
  Under Markovian assumptions, we leverage a Central Limit Theorem (CLT) for the empirical measure in the test statistic of the composite hypothesis Hoeffding test so as to establish weak convergence results for the test…
• Online Robust Principal Component Analysis with Change Point Detection
  19 Feb 2017 2 repositories listed
  Robust PCA methods are typically batch algorithms which requires loading all observations into memory before processing.
• Minimax Optimal Two-Sample Testing under Local Differential Privacy
  13 Nov 2024 1 repository listed
  We explore the trade-off between privacy and statistical utility in private two-sample testing under local differential privacy (LDP) for both multinomial and continuous data.
• Model Equality Testing: Which Model Is This API Serving?
  26 Oct 2024 1 repository listed Syntology ran 0 of 5 samples · 5 unverified · 5 pointer-only (licence)
  Users often interact with large language models through black-box inference APIs, both for closed- and open-weight models (e.
• General Frameworks for Conditional Two-Sample Testing
  22 Oct 2024 1 repository listed
  Our first framework allows us to convert any conditional independence test into a conditional two-sample test in a black-box manner, while preserving the asymptotic properties of the original conditional independence…
• Credal Two-Sample Tests of Epistemic Uncertainty
  16 Oct 2024 1 repository listed Syntology ran 12 of 12 samples · 0 unverified
  We introduce credal two-sample testing, a new hypothesis testing framework for comparing credal sets -- convex sets of probability measures where each element captures aleatoric uncertainty and the set itself represents…
• Machine Learning for Two-Sample Testing under Right-Censored Data: A Simulation Study
  12 Sep 2024 1 repository listed
  The focus of this study is to evaluate the effectiveness of Machine Learning (ML) methods for two-sample testing with right-censored observations.
• Computational-Statistical Trade-off in Kernel Two-Sample Testing with Random Fourier Features
  12 Jul 2024 1 repository listed
  Despite its success and widespread adoption, the primary limitation of the MMD test has been its quadratic-time complexity, which poses challenges for large-scale analysis.
• Deep anytime-valid hypothesis testing
  30 Oct 2023 1 repository listed
  We propose a general framework for constructing powerful, sequential hypothesis tests for a large class of nonparametric testing problems.
• Kernel-Based Tests for Likelihood-Free Hypothesis Testing
  17 Aug 2023 1 repository listed
  Given n observations from two balanced classes, consider the task of labeling an additional m inputs that are known to all belong to \emph{one} of the two classes.
• MMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting
  14 Jun 2023 1 repository listed Syntology ran 2 of 15 samples · 13 unverified
  We propose novel statistics which maximise the power of a two-sample test based on the Maximum Mean Discrepancy (MMD), by adapting over the set of kernels used in defining it.
• The Representation Jensen-Shannon Divergence
  25 May 2023 1 repository listed
  We then compute the Jensen-Shannon divergence between these operators, thereby establishing a proper divergence measure between probability distributions in the input space.
• Multimodal Multi-User Surface Recognition with the Kernel Two-Sample Test
  8 Mar 2023 1 repository listed
  Machine learning and deep learning have been used extensively to classify physical surfaces through images and time-series contact data.

Syntology lines on 10 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.