14 citations · 29 across the 4 of their papers we have counts for
4 papers
Probabilistically robust conformal prediction
Subhankar Ghosh, Yuanjie Shi, Taha Belkhouja +3
Conformal prediction (CP) is a framework to quantify uncertainty of machine learning classifiers including deep neural networks. Given a testing example and a trained classifier, C…
Improving Uncertainty Quantification of Deep Classifiers via Neighborhood Conformal Prediction: Novel Algorithm and Theoretical Analysis
Subhankar Ghosh, Taha Belkhouja, Yan Yan +1
Safe deployment of deep neural networks in high-stake real-world applications requires theoretically sound uncertainty quantification. Conformal prediction (CP) is a principled fra…
Training Robust Deep Models for Time-Series Domain: Novel Algorithms and Theoretical Analysis
Taha Belkhouja, Yan Yan, Janardhan Rao Doppa
Despite the success of deep neural networks (DNNs) for real-world applications over time-series data such as mobile health, little is known about how to train robust DNNs for time-…
Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical Features
Taha Belkhouja, Janardhan Rao Doppa
Time-series data arises in many real-world applications (e.g., mobile health) and deep neural networks (DNNs) have shown great success in solving them. Despite their success, littl…