3 papers
cs.DC2025
SneakPeek: Data-Aware Model Selection and Scheduling for Inference Serving on the Edge
Joel Wolfrath, Daniel Frink, Abhishek Chandra
Modern applications increasingly rely on inference serving systems to provide low-latency insights with a diverse set of machine learning models. Existing systems often utilize res…
cs.LG2024
Stronger Baseline Models -- A Key Requirement for Aligning Machine Learning Research with Clinical Utility
Nathan Wolfrath, Joel Wolfrath, Hengrui Hu +2
Machine Learning (ML) research has increased substantially in recent years, due to the success of predictive modeling across diverse application domains. However, well-known barrie…
cs.LG2024
A Biased Estimator for MinMax Sampling and Distributed Aggregation
Joel Wolfrath, Abhishek Chandra
MinMax sampling is a technique for downsampling a real-valued vector which minimizes the maximum variance over all vector components. This approach is useful for reducing the amoun…