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New method optimizes score function estimation using derivative constraints · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a method for score function estimation using derivative constraints, applicable to both probability measure inference and score-based generative modeling. By constraining the hypothesis space to a Sobolev ball, the approach aims to prevent overfitting and achieve minimax estimation rates. This technique is expected to improve the quality of output from score-based generative models. AI

IMPACT This research could lead to more efficient and effective score-based generative models.

RANK_REASON The cluster contains a pre-print academic paper on a statistical machine learning topic.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New method optimizes score function estimation using derivative constraints · 2 sources tracked

COVERAGE [2]

  1. arXiv stat.ML TIER_1 Français(FR) · Thomas Bonis, Thanh Mai Pham Ngoc, Viet Chi Tran ·

    Optimal score function estimation via derivatives constraints

    arXiv:2606.19084v1 Announce Type: cross Abstract: We consider the problem of score function estimation via empirical risk minimization. We first start with the question of inferring the score function of a probability measure $\mu$ with density on the flat torus from a sample of …

  2. arXiv stat.ML TIER_1 Français(FR) · Viet Chi Tran ·

    Optimal score function estimation via derivatives constraints

    We consider the problem of score function estimation via empirical risk minimization. We first start with the question of inferring the score function of a probability measure $μ$ with density on the flat torus from a sample of distribution $μ$. We show that constraining the hypo…

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