Generating Informative Samples for Risk-Averse Fine-Tuning of Downstream Tasks

Heasung Kim, Taekyun Lee, Hyeji Kim, Gustavo De Veciana

Keywords: Generative models, data augmentation, score-based generative models, risk, importance sampling, wireless communications, applications

TL;DR: A novel risk-averse training framework that leverages score-based generative models for data augmentation tailored to Conditional Value-at-Risk minimization

Abstract: Risk-averse modeling is critical in safety-sensitive and high-stakes applications. Conditional Value-at-Risk (CVaR) quantifies such risk by measuring the expected loss in the tail of the loss distribution, and minimizing it provides a principled framework for training robust models. However, direct CVaR minimization remains challenging due to the difficulty of accurately estimating rare, high-loss events—particularly at extreme quantiles. In this work, we propose a novel training framework that synthesizes informative samples for CVaR optimization using score-based generative models. Specifically, we guide a diffusion-based generative model to sample from a reweighted distribution that emphasizes inputs likely to incur high loss under a pretrained reference model. These samples are then incorporated via a loss-weighted importance sampling scheme to reduce noise in stochastic optimization. We establish convergence guarantees and show that the synthesized, high-loss-emphasized dataset substantially contributes to the noise reduction. Empirically, we validate the effectiveness of our approach across multiple settings, including a real-world wireless channel compression task, where our method achieves significant improvements over standard risk minimization strategies.

Primary Area: General machine learning (supervised, unsupervised, online, active, etc.)

Submission Number: 28167