Papers Two-sample testing
“Two-sample testing” 태그가 달린 논문 360편 · 필터 해제
On the Exploration of Local Significant Differences For Two-Sample Test
Recent years have witnessed increasing attentions on two-sample test with diverse real applications, while this work takes one more step on the exploration of local significant differences for two-sample test. We propose…
Two-sample testingKernel-Based Tests for Likelihood-Free Hypothesis Testing
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. Special cases of this problem are well-known: with c…
Binary ClassificationTwo-sample testingAdaptive learning of density ratios in RKHS
Estimating the ratio of two probability densities from finitely many observations of the densities is a central problem in machine learning and statistics with applications in two-sample testing, divergence estimation, g…
Density EstimationDensity Ratio EstimationNovelty DetectionTwo-sample testingMMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting
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. For finite sets, this reduces to combining (…
Two-sample testingThe Representation Jensen-Shannon Divergence
Quantifying the difference between probability distributions is crucial in machine learning. However, estimating statistical divergences from empirical samples is challenging due to unknown underlying distributions. This…
Domain AdaptationTwo-sample testingUnsupervised Domain AdaptationBootstrapped Edge Count Tests for Nonparametric Two-Sample Inference Under Heterogeneity
Nonparametric two-sample testing is a classical problem in inferential statistics. While modern two-sample tests, such as the edge count test and its variants, can handle multivariate and non-Euclidean data, contemporary…
Two-sample testingVocal Bursts Valence PredictionMultimodal Multi-User Surface Recognition with the Kernel Two-Sample Test
Machine learning and deep learning have been used extensively to classify physical surfaces through images and time-series contact data. However, these methods rely on human expertise and entail the time-consuming proces…
BenchmarkingTime SeriesTime Series AnalysisTwo-sample testing+1Active Sequential Two-Sample Testing
A two-sample hypothesis test is a statistical procedure used to determine whether the distributions generating two samples are identical. We consider the two-sample testing problem in a new scenario where the sample meas…
Two-sample testingvalidVocal Bursts Valence PredictionCompress Then Test: Powerful Kernel Testing in Near-linear Time
Kernel two-sample testing provides a powerful framework for distinguishing any pair of distributions based on $n$ sample points. However, existing kernel tests either run in $n^2$ time or sacrifice undue power to improve…
Two-sample testingA Permutation-free Kernel Two-Sample Test
The kernel Maximum Mean Discrepancy~(MMD) is a popular multivariate distance metric between distributions that has found utility in two-sample testing. The usual kernel-MMD test statistic is a degenerate U-statistic unde…
Two-sample testingVocal Bursts Valence PredictionMMD-B-Fair: Learning Fair Representations with Statistical Testing
We introduce a method, MMD-B-Fair, to learn fair representations of data via kernel two-sample testing. We find neural features of our data where a maximum mean discrepancy (MMD) test cannot distinguish between represent…
Representation LearningTwo-sample testingAutoML Two-Sample Test
Two-sample tests are important in statistics and machine learning, both as tools for scientific discovery as well as to detect distribution shifts. This led to the development of many sophisticated test procedures going …
AutoMLscientific discoveryTwo-sample testingVocal Bursts Valence PredictionSemi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows
Training normalizing flow generative models can be challenging due to the need to calculate computationally expensive determinants of Jacobians. This paper studies the likelihood-free training of flows and proposes the e…
Two-sample testingAddressing Maximization Bias in Reinforcement Learning with Two-Sample Testing
Value-based reinforcement-learning algorithms have shown strong results in games, robotics, and other real-world applications. Overestimation bias is a known threat to those algorithms and can sometimes lead to dramatic …
Q-Learningreinforcement-learningReinforcement Learning (RL)Two-sample testing+1A label-efficient two-sample test
Two-sample tests evaluate whether two samples are realizations of the same distribution (the null hypothesis) or two different distributions (the alternative hypothesis). We consider a new setting for this problem where …
Two-sample testingVocal Bursts Valence PredictionMMD Aggregated Two-Sample Test
We propose two novel nonparametric two-sample kernel tests based on the Maximum Mean Discrepancy (MMD). First, for a fixed kernel, we construct an MMD test using either permutations or a wild bootstrap, two popular numer…
TranslationTwo-sample testingVocal Bursts Valence PredictionGraphon based Clustering and Testing of Networks: Algorithms and Theory
Network-valued data are encountered in a wide range of applications and pose challenges in learning due to their complex structure and absence of vertex correspondence. Typical examples of such problems include classific…
ClassificationClusteringGraph ClassificationGraph Clustering+1Limit Distribution Theory for the Smooth 1-Wasserstein Distance with Applications
The smooth 1-Wasserstein distance (SWD) $W_1^\sigma$ was recently proposed as a means to mitigate the curse of dimensionality in empirical approximation while preserving the Wasserstein structure. Indeed, SWD exhibits pa…
Two-sample testingGeneralized Multivariate Signs for Nonparametric Hypothesis Testing in High Dimensions
High-dimensional data, where the dimension of the feature space is much larger than sample size, arise in a number of statistical applications. In this context, we construct the generalized multivariate sign transformati…
Two-sample testingVocal Bursts Intensity PredictionMeta Two-Sample Testing: Learning Kernels for Testing with Limited Data
Modern kernel-based two-sample tests have shown great success in distinguishing complex, high-dimensional distributions with appropriate learned kernels. Previous work has demonstrated that this kernel learning procedure…
Two-sample testingVocal Bursts Valence Prediction