3 papers
cs.LG2026
Know When to Abstain: Optimal Selective Classification with Likelihood Ratios
Alvin Heng, Harold Soh
Selective classification enhances the reliability of predictive models by allowing them to abstain from making uncertain predictions. In this work, we revisit the design of optimal…
cs.LG2024
Out-of-Distribution Detection with a Single Unconditional Diffusion Model
Alvin Heng, Alexandre H. Thiery, Harold Soh
Out-of-distribution (OOD) detection is a critical task in machine learning that seeks to identify abnormal samples. Traditionally, unsupervised methods utilize a deep generative mo…
cs.LG2024
Generative Modeling with Flow-Guided Density Ratio Learning
Alvin Heng, Abdul Fatir Ansari, Harold Soh
We present Flow-Guided Density Ratio Learning (FDRL), a simple and scalable approach to generative modeling which builds on the stale (time-independent) approximation of the gradie…