Towards Healing the Blindness of Score Matching
Mingtian Zhang, Oscar Key, Peter Hayes, David Barber, Brooks Paige, Fran\c{c}ois-Xavier Briol

TL;DR
This paper introduces a new family of divergences designed to address the blindness problem in score-based methods, improving density estimation for multi-modal distributions in machine learning.
Contribution
The authors propose a novel divergence family that mitigates blindness in score matching, enhancing performance in density estimation tasks.
Findings
Improved density estimation accuracy for multi-modal distributions
Demonstrated effectiveness of the new divergence over traditional methods
Addressed the blindness problem in score-based divergences
Abstract
Score-based divergences have been widely used in machine learning and statistics applications. Despite their empirical success, a blindness problem has been observed when using these for multi-modal distributions. In this work, we discuss the blindness problem and propose a new family of divergences that can mitigate the blindness problem. We illustrate our proposed divergence in the context of density estimation and report improved performance compared to traditional approaches.
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Taxonomy
TopicsAdvanced Statistical Methods and Models · Bayesian Modeling and Causal Inference · Time Series Analysis and Forecasting
