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Tag: score

  • Initialization-Aware Score-Based Diffusion Sampling

    Initialization-Aware Score-Based Diffusion Sampling arXiv:2603.00772v1 Announce Type: new Abstract: Score-based generative models (SGMs) aim at generating samples from a target distribution by approximating the reverse-time dynamics of a stochastic differential equation. Despite their strong empirical performance, classical samplers initialized from a Gaussian distribution require a long time horizon noising typically inducing a large number of…

  • Generalized Leverage Score for Scalable Assessment of Privacy Vulnerability

    Generalized Leverage Score for Scalable Assessment of Privacy Vulnerability arXiv:2602.15919v1 Announce Type: new Abstract: Can the privacy vulnerability of individual data points be assessed without retraining models or explicitly simulating attacks? We answer affirmatively by showing that exposure to membership inference attack (MIA) is fundamentally governed by a data point’s influence on the learned model.…

  • The Proximity of the Inception Score as an Evaluation Criterion

    The Proximity of the Inception Score as an Evaluation Criterion The neighborhood of synthetic data The post The Proximity of the Inception Score as an Evaluation Criterion appeared first on Towards Data Science. Giuseppe Pio Cannata Go to original source

  • A Diffusive Classification Loss for Learning Energy-based Generative Models

    A Diffusive Classification Loss for Learning Energy-based Generative Models arXiv:2601.21025v1 Announce Type: new Abstract: Score-based generative models have recently achieved remarkable success. While they are usually parameterized by the score, an alternative way is to use a series of time-dependent energy-based models (EBMs), where the score is obtained from the negative input-gradient of the energy.…

  • Energy-Tweedie: Score meets Score, Energy meets Energy

    Energy-Tweedie: Score meets Score, Energy meets Energy arXiv:2512.23818v1 Announce Type: new Abstract: Denoising and score estimation have long been known to be linked via the classical Tweedie’s formula. In this work, we first extend the latter to a wider range of distributions often called “energy models” and denoted elliptical distributions in this work. Next, we…

  • Semiparametric KSD test: unifying score and distance-based approaches for goodness-of-fit testing

    Semiparametric KSD test: unifying score and distance-based approaches for goodness-of-fit testing arXiv:2512.20007v1 Announce Type: new Abstract: Goodness-of-fit (GoF) tests are fundamental for assessing model adequacy. Score-based tests are appealing because they require fitting the model only once under the null. However, extending them to powerful nonparametric alternatives is difficult due to the lack of suitable…

  • Disentangled representations via score-based variational autoencoders

    Disentangled representations via score-based variational autoencoders arXiv:2512.17127v1 Announce Type: new Abstract: We present the Score-based Autoencoder for Multiscale Inference (SAMI), a method for unsupervised representation learning that combines the theoretical frameworks of diffusion models and VAEs. By unifying their respective evidence lower bounds, SAMI formulates a principled objective that learns representations through score-based guidance of…

  • Provable Diffusion Posterior Sampling for Bayesian Inversion

    Provable Diffusion Posterior Sampling for Bayesian Inversion arXiv:2512.08022v1 Announce Type: new Abstract: This paper proposes a novel diffusion-based posterior sampling method within a plug-and-play (PnP) framework. Our approach constructs a probability transport from an easy-to-sample terminal distribution to the target posterior, using a warm-start strategy to initialize the particles. To approximate the posterior score, we…

  • Latent Nonlinear Denoising Score Matching for Enhanced Learning of Structured Distributions

    Latent Nonlinear Denoising Score Matching for Enhanced Learning of Structured Distributions arXiv:2512.06615v1 Announce Type: new Abstract: We present latent nonlinear denoising score matching (LNDSM), a novel training objective for score-based generative models that integrates nonlinear forward dynamics with the VAE-based latent SGM framework. This combination is achieved by reformulating the cross-entropy term using the approximate…

  • Variance-Bounded Evaluation without Ground Truth: VB-Score

    Variance-Bounded Evaluation without Ground Truth: VB-Score arXiv:2509.22751v1 Announce Type: new Abstract: Reliable evaluation is a central challenge in machine learning when tasks lack ground truth labels or involve ambiguity and noise. Conventional frameworks, rooted in the Cranfield paradigm and label-based metrics, fail in such cases because they cannot assess how robustly a system performs under…

  • A Malliavin calculus approach to score functions in diffusion generative models

    A Malliavin calculus approach to score functions in diffusion generative models arXiv:2507.05550v1 Announce Type: new Abstract: Score-based diffusion generative models have recently emerged as a powerful tool for modelling complex data distributions. These models aim at learning the score function, which defines a map from a known probability distribution to the target data distribution via…

  • Diffusion-Based Hypothesis Testing and Change-Point Detection

    Diffusion-Based Hypothesis Testing and Change-Point Detection arXiv:2506.16089v1 Announce Type: new Abstract: Score-based methods have recently seen increasing popularity in modeling and generation. Methods have been constructed to perform hypothesis testing and change-point detection with score functions, but these methods are in general not as powerful as their likelihood-based peers. Recent works consider generalizing the score-based…

  • Direct Fisher Score Estimation for Likelihood Maximization

    Direct Fisher Score Estimation for Likelihood Maximization arXiv:2506.06542v1 Announce Type: new Abstract: We study the problem of likelihood maximization when the likelihood function is intractable but model simulations are readily available. We propose a sequential, gradient-based optimization method that directly models the Fisher score based on a local score matching technique which uses simulations from…

  • Preconditioned Langevin Dynamics with Score-Based Generative Models for Infinite-Dimensional Linear Bayesian Inverse Problems

    Preconditioned Langevin Dynamics with Score-Based Generative Models for Infinite-Dimensional Linear Bayesian Inverse Problems arXiv:2505.18276v1 Announce Type: new Abstract: Designing algorithms for solving high-dimensional Bayesian inverse problems directly in infinite-dimensional function spaces – where such problems are naturally formulated – is crucial to ensure stability and convergence as the discretization of the underlying problem is refined.…

  • Sequential Change Point Detection via Denoising Score Matching

    Sequential Change Point Detection via Denoising Score Matching arXiv:2501.12667v1 Announce Type: new Abstract: Sequential change-point detection plays a critical role in numerous real-world applications, where timely identification of distributional shifts can greatly mitigate adverse outcomes. Classical methods commonly rely on parametric density assumptions of pre- and post-change distributions, limiting their effectiveness for high-dimensional, complex data…

  • Beyond Log-Concavity and Score Regularity: Improved Convergence Bounds for Score-Based Generative Models in W2-distance

    Beyond Log-Concavity and Score Regularity: Improved Convergence Bounds for Score-Based Generative Models in W2-distance arXiv:2501.02298v1 Announce Type: new Abstract: Score-based Generative Models (SGMs) aim to sample from a target distribution by learning score functions using samples perturbed by Gaussian noise. Existing convergence bounds for SGMs in the $mathcal{W}_2$-distance rely on stringent assumptions about the data…

  • Propensity-Score Matching Is the Bedrock of Causal Inference

    Propensity-Score Matching Is the Bedrock of Causal Inference And how to get started with it using Python Continue reading on Towards Data Science ยป Ari Joury, PhD Go to original source