Two-sample testing
76 papers with code • 5 benchmarks • 1 datasets
In statistical hypothesis testing, a two-sample test is a test performed on the data of two random samples, each independently obtained from a different given population. The purpose of the test is to determine whether the difference between these two populations is statistically significant. The statistics used in two-sample tests can be used to solve many machine learning problems, such as domain adaptation, covariate shift and generative adversarial networks.
Benchmarks
These leaderboards are used to track progress in Two-sample testing
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HIGGS Data Set
Most implemented papers
A Differentially Private Kernel Two-Sample Test
antoninschrab/dpkernel-paper
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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.
Scalable and Efficient Hypothesis Testing with Random Forests
Throughout the last decade, random forests have established themselves as among the most accurate and popular supervised learning methods.
Comparing distributions: $\ell_1$ geometry improves kernel two-sample testing
Here, we show that $L^p$ distances (with $p\geq 1$) 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 convergence.
Decision-Making with Auto-Encoding Variational Bayes
PierreBoyeau/sbVAE
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NeurIPS 2020
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.
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting
In this paper, we present $\texttt{ACORE}$ (Approximate Computation via Odds Ratio Estimation), a frequentist approach to LFI that first formulates the classical likelihood ratio test (LRT) as a parametrized classification problem, and then uses the equivalence of tests and confidence sets to build confidence regions for parameters of interest.
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
SJ001/AI-Feynman
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NeurIPS 2020
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.
Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation
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.
Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inference
vonguyenleduy/dnn_segmentation_selective_inference
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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 compute exact (non-asymptotic) valid p-values for the segmentation results.
B-tests: Low Variance Kernel Two-Sample Tests
A family of maximum mean discrepancy (MMD) kernel two-sample tests is introduced.
Short-term plasticity as cause-effect hypothesis testing in distal reward learning
Asynchrony, overlaps and delays in sensory-motor signals introduce ambiguity as to which stimuli, actions, and rewards are causally related.
