12 citations · 13 across the 5 of their papers we have counts for
5 papers
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…
Generalization Bounds for Neural Belief Propagation Decoders
Sudarshan Adiga, Xin Xiao, Ravi Tandon +2
Machine learning based approaches are being increasingly used for designing decoders for next generation communication systems. One widely used framework is neural belief propagati…
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…