Papers Two-sample testing
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Self-Supervised Metric Learning in Multi-View Data: A Downstream Task Perspective
Self-supervised metric learning has been a successful approach for learning a distance from an unlabeled dataset. The resulting distance is broadly useful for improving various distance-based downstream tasks, even when …
ClusteringMetric LearningTwo-sample testingA Witness Two-Sample Test
The Maximum Mean Discrepancy (MMD) has been the state-of-the-art nonparametric test for tackling the two-sample problem. Its statistic is given by the difference in expectations of the witness function, a real-valued fun…
Two-sample testingVocal Bursts Valence PredictionSmooth $p$-Wasserstein Distance: Structure, Empirical Approximation, and Statistical Applications
Discrepancy measures between probability distributions, often termed statistical distances, are ubiquitous in probability theory, statistics and machine learning. To combat the curse of dimensionality when estimating the…
Two-sample testingQuickest change detection for multi-task problems under unknown parameters
We consider the quickest change detection problem where both the parameters of pre- and post- change distributions are unknown, which prevent the use of classical simple hypothesis testing. Without additional assumptions…
Change DetectionTwo-sample testingUnderstanding Classifiers with Generative Models
Although deep neural networks are effective on supervised learning tasks, they have been shown to be brittle. They are prone to overfitting on their training distribution and are easily fooled by small adversarial pertur…
Two-sample testingA General Framework for Distributed Inference with Uncertain Models
This paper studies the problem of distributed classification with a network of heterogeneous agents. The agents seek to jointly identify the underlying target class that best describes a sequence of observations. The pro…
Learning TheoryTwo-sample testingPolicy design in experiments with unknown interference
This paper studies experimental designs for estimation and inference on policies with spillover effects. Units are organized into a finite number of large clusters and interact in unknown ways within each cluster. First,…
Experimental DesignTwo-sample testingAdversarially Robust Classification based on GLRT
Machine learning models are vulnerable to adversarial attacks that can often cause misclassification by introducing small but well designed perturbations. In this paper, we explore, in the setting of classical composite …
ClassificationGeneral ClassificationRobust classificationTwo-sample testingBottleneck Problems: Information and Estimation-Theoretic View
Information bottleneck (IB) and privacy funnel (PF) are two closely related optimization problems which have found applications in machine learning, design of privacy algorithms, capacity problems (e.g., Mrs. Gerber's Le…
LEMMATwo-sample testingDimension-agnostic inference using cross U-statistics
Classical asymptotic theory for statistical inference usually involves calibrating a statistic by fixing the dimension $d$ while letting the sample size $n$ increase to infinity. Recently, much effort has been dedicated …
Two-sample testingEstimating Linear Mixed Effects Models with Truncated Normally Distributed Random Effects
Linear Mixed Effects (LME) models have been widely applied in clustered data analysis in many areas including marketing research, clinical trials, and biomedical studies. Inference can be conducted using maximum likeliho…
MarketingTwo-sample testingRobust hypothesis testing and distribution estimation in Hellinger distance
We propose a simple robust hypothesis test that has the same sample complexity as that of the optimal Neyman-Pearson test up to constants, but robust to distribution perturbations under Hellinger distance. We discuss the…
Two-sample testingp-value peeking and estimating extrema
A pervasive issue in statistical hypothesis testing is that the reported $p$-values are biased downward by data "peeking" -- the practice of reporting only progressively extreme values of the test statistic as more data …
Two-sample testingIntrinsic Sliced Wasserstein Distances for Comparing Collections of Probability Distributions on Manifolds and Graphs
Collections of probability distributions arise in a variety of applications ranging from user activity pattern analysis to brain connectomics. In practice these distributions can be defined over diverse domain types incl…
Two-sample testingTowards Safe Policy Improvement for Non-Stationary MDPs
Many real-world sequential decision-making problems involve critical systems with financial risks and human-life risks. While several works in the past have proposed methods that are safe for deployment, they assume that…
Decision Makingreinforcement-learningReinforcement Learning (RL)Sequential Decision Making+3Detecting Rewards Deterioration in Episodic Reinforcement Learning
In many RL applications, once training ends, it is vital to detect any deterioration in the agent performance as soon as possible. Furthermore, it often has to be done without modifying the policy and under minimal assum…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Two-sample testingAn explainable deep vision system for animal classification and detection in trail-camera images with automatic post-deployment retraining
This paper introduces an automated vision system for animal detection in trail-camera images taken from a field under the administration of the Texas Parks and Wildlife Department. As traditional wildlife counting techni…
General ClassificationSensitivitySpecificityTwo-sample testingHow to Control the Error Rates of Binary Classifiers
The traditional binary classification framework constructs classifiers which may have good accuracy, but whose false positive and false negative error rates are not under users' control. In many cases, one of the errors …
Binary ClassificationClassificationGeneral ClassificationTwo-sample testingSurprise: Result List Truncation via Extreme Value Theory
Work in information retrieval has largely been centered around ranking and relevance: given a query, return some number of results ordered by relevance to the user. The problem of result list truncation, or where to trun…
Information RetrievalRetrievalTwo-sample testingQuantum-enhanced barcode decoding and pattern recognition
Quantum hypothesis testing is one of the most fundamental problems in quantum information theory, with crucial implications in areas like quantum sensing, where it has been used to prove quantum advantage in a series of …
Two-sample testing