18 citations · 24 across the 6 of their papers we have counts for
6 papers
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…
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…
Selective Amnesia: A Continual Learning Approach to Forgetting in Deep Generative Models
Alvin Heng, Harold Soh
The recent proliferation of large-scale text-to-image models has led to growing concerns that such models may be misused to generate harmful, misleading, and inappropriate content.…
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…
Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series
Abdul Fatir Ansari, Alvin Heng, Andre Lim +1
Learning accurate predictive models of real-world dynamic phenomena (e.g., climate, biological) remains a challenging task. One key issue is that the data generated by both natural…
Pair hopping in systems of strongly interacting hard-core bosons
Alvin J. R. Heng, Wenan Guo, Anders W. Sandvik +1
We have used the Stochastic Series Expansion quantum Monte Carlo method to study interacting hard-core bosons on the square lattice, with pair-hopping processes supplementing the s…