12 citations · 13 across the 5 of their papers we have counts for
3 papers · 1 filter
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…
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…
Latency-Distortion Tradeoffs in Communicating Classification Results over Noisy Channels
Noel Teku, Sudarshan Adiga, Ravi Tandon
In this work, the problem of communicating decisions of a classifier over a noisy channel is considered. With machine learning based models being used in variety of time-sensitive…