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Tag: two

  • Differentially Private Two-Stage Gradient Descent for Instrumental Variable Regression

    Differentially Private Two-Stage Gradient Descent for Instrumental Variable Regression arXiv:2509.22794v1 Announce Type: new Abstract: We study instrumental variable regression (IVaR) under differential privacy constraints. Classical IVaR methods (like two-stage least squares regression) rely on solving moment equations that directly use sensitive covariates and instruments, creating significant risks of privacy leakage and posing challenges in designing…

  • A Two-armed Bandit Framework for A/B Testing

    A Two-armed Bandit Framework for A/B Testing arXiv:2507.18118v1 Announce Type: new Abstract: A/B testing is widely used in modern technology companies for policy evaluation and product deployment, with the goal of comparing the outcomes under a newly-developed policy against a standard control. Various causal inference and reinforcement learning methods developed in the literature are applicable…

  • A Two-Sample Test of Text Generation Similarity

    A Two-Sample Test of Text Generation Similarity arXiv:2505.05269v1 Announce Type: new Abstract: The surge in digitized text data requires reliable inferential methods on observed textual patterns. This article proposes a novel two-sample text test for comparing similarity between two groups of documents. The hypothesis is whether the probabilistic mapping generating the textual data is identical…

  • From Two Sample Testing to Singular Gaussian Discrimination

    From Two Sample Testing to Singular Gaussian Discrimination arXiv:2505.04613v1 Announce Type: new Abstract: We establish that testing for the equality of two probability measures on a general separable and compact metric space is equivalent to testing for the singularity between two corresponding Gaussian measures on a suitable Reproducing Kernel Hilbert Space. The corresponding Gaussians are…

  • One-Tailed Vs. Two-Tailed Tests

    One-Tailed Vs. Two-Tailed Tests Introduction If you’ve ever analyzed data using built-in t-test functions, such as those in R or SciPy, here’s a question for you: have you ever adjusted the default setting for the alternative hypothesis? If your answer is no—or if you’re not even sure what this means—then this blog post is for…

  • An Efficient Permutation-Based Kernel Two-Sample Test

    An Efficient Permutation-Based Kernel Two-Sample Test arXiv:2502.13570v1 Announce Type: new Abstract: Two-sample hypothesis testing-determining whether two sets of data are drawn from the same distribution-is a fundamental problem in statistics and machine learning with broad scientific applications. In the context of nonparametric testing, maximum mean discrepancy (MMD) has gained popularity as a test statistic due…

  • Guiding Two-Layer Neural Network Lipschitzness via Gradient Descent Learning Rate Constraints

    Guiding Two-Layer Neural Network Lipschitzness via Gradient Descent Learning Rate Constraints arXiv:2502.03792v1 Announce Type: new Abstract: We demonstrate that applying an eventual decay to the learning rate (LR) in empirical risk minimization (ERM), where the mean-squared-error loss is minimized using standard gradient descent (GD) for training a two-layer neural network with Lipschitz activation functions, ensures…

  • Show and Tell

    Show and Tell Photo by Ståle Grut on Unsplash Introduction Natural Language Processing and Computer Vision used to be two completely different fields. Well, at least back when I started to learn machine learning and deep learning, I feel like there are multiple paths to follow, and each of them, including NLP and Computer Vision,…