78 citations · 297 across the 42 of their papers we have counts for
7 papers · 1 filter
Improving Classifier Confidence using Lossy Label-Invariant Transformations
Sooyong Jang, Insup Lee, James Weimer
Providing reliable model uncertainty estimates is imperative to enabling robust decision making by autonomous agents and humans alike. While recently there have been significant ad…
PAC Confidence Predictions for Deep Neural Network Classifiers
Sangdon Park, Shuo Li, Insup Lee +1
A key challenge for deploying deep neural networks (DNNs) in safety critical settings is the need to provide rigorous ways to quantify their uncertainty. In this paper, we propose…
A Skew-Sensitive Evaluation Framework for Imbalanced Data Classification
Min Du, Nesime Tatbul, Brian Rivers +7
Class distribution skews in imbalanced datasets may lead to models with prediction bias towards majority classes, making fair assessment of classifiers a challenging task. Metrics…
Optimal Virtual Cluster-based Multiprocessor Scheduling
Arvind Easwaran, Insik Shin, Insup Lee
Scheduling of constrained deadline sporadic task systems on multiprocessor platforms is an area which has received much attention in the recent past. It is widely believed that fin…
Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation
Sangdon Park, Osbert Bastani, James Weimer +1
Reliable uncertainty estimates are an important tool for helping autonomous agents or human decision makers understand and leverage predictive models. However, existing approaches…
PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction
Sangdon Park, Osbert Bastani, Nikolai Matni +1
We propose an algorithm combining calibrated prediction and generalization bounds from learning theory to construct confidence sets for deep neural networks with PAC guarantees---i…
Real-Time Detectors for Digital and Physical Adversarial Inputs to Perception Systems
Yiannis Kantaros, Taylor Carpenter, Kaustubh Sridhar +3
Deep neural network (DNN) models have proven to be vulnerable to adversarial digital and physical attacks. In this paper, we propose a novel attack- and dataset-agnostic and real-t…