Search Results for author:
Found 6 papers, 5 papers with code
Probabilistically robust conformal prediction
We propose a novel adaptive PRCP (aPRCP) algorithm to achieve probabilistically robust coverage.
Improving Uncertainty Quantification of Deep Classifiers via Neighborhood Conformal Prediction: Novel Algorithm and Theoretical Analysis
Safe deployment of deep neural networks in high-stake real-world applications requires theoretically sound uncertainty quantification.
Dynamic Time Warping based Adversarial Framework for Time-Series Domain
Despite the rapid progress on research in adversarial robustness of deep neural networks (DNNs), there is little principled work for the time-series domain.
Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical Features
We also provide certified bounds on the norm of the statistical features for constructing adversarial examples.
Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring Approach
Experiments on diverse real-world benchmarks demonstrate that the SRS method is well-suited for time-series OOD detection when compared to baseline methods.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
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Training Robust Deep Models for Time-Series Domain: Novel Algorithms and Theoretical Analysis
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-series domain due to its unique characteristics compared to images and text data.
