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cs.LG2024
Fine-Grained Uncertainty Quantification via Collisions
Jesse Friedbaum, Sudarshan Adiga, Ravi Tandon
We propose a new and intuitive metric for aleatoric uncertainty quantification (UQ), the prevalence of class collisions defined as the same input being observed in different classe…
cs.LG2024
Trustworthy Actionable Perturbations
Jesse Friedbaum, Sudarshan Adiga, Ravi Tandon
Counterfactuals, or modified inputs that lead to a different outcome, are an important tool for understanding the logic used by machine learning classifiers and how to change an un…
cs.LG2022★ 1 cited
Unsupervised Change Detection using DRE-CUSUM
Sudarshan Adiga, Ravi Tandon
This paper presents DRE-CUSUM, an unsupervised density-ratio estimation (DRE) based approach to determine statistical changes in time-series data when no knowledge of the pre-and p…