4 papers
Reliable OOD Virtual Screening with Extrapolatory Pseudo-Label Matching
Yunni Qu, Bhargav Vaduri, Karthikeya Jatoth +6
Machine learning (ML) models are increasingly deployed for virtual screening in drug discovery, where the goal is to identify novel, chemically diverse scaffolds while minimizing e…
Relaxed Efficient Acquisition of Context and Temporal Features
Yunni Qu, Dzung Dinh, Grant King +5
In many biomedical applications, measurements are not freely available at inference time: each laboratory test, imaging modality, or assessment incurs financial cost, time burden,…
NOCTA: Non-Greedy Objective Cost-Tradeoff Acquisition for Longitudinal Data
Dzung Dinh, Boqi Chen, Yunni Qu +2
In many critical domains, features are not freely available at inference time: each measurement may come with a cost of time, money, and risk. Longitudinal prediction further compl…
Information Templates: A New Paradigm for Intelligent Active Feature Acquisition
Hung-Tien Huang, Dzung Dinh, Junier B. Oliva
Active feature acquisition (AFA) is an instance-adaptive paradigm in which, at inference time, a policy sequentially chooses which features to acquire (at a cost) before predicting…