3 papers
cs.LG2026
Measuring What Matters: Synthetic Benchmarks for Concept Bottleneck Models
Julian Skirzynski, Harry Cheon, Shreyas Kadekodi +2
Concept bottleneck models predict outcomes from high-level concepts detected in inputs. Although concepts provide a simple way to reap benefits from interpretability, very few data…
cs.CY2026
Power Consumption Patterns Using Telemetry Data
Harry Cheon, Yuyang Pang, Zhiting Hu +4
This paper examines the analysis of package power consumption using Intel's telemetry data. It challenges the prevailing belief that hardware choice is the primary determinant of a…
cs.LG2025
Responsiveness Verification: Will Predictions Change? How Much? How Often?
Seung Hyun Cheon, Harry Cheon, Meredith Stewart +3
Machine learning models are often used in applications where their inputs change due to routine interactions, strategic manipulation, or noise. In such settings, models can undermi…