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09/01/2023
Curating Naturally Adversarial Datasets for Trustworthy AI in Healthcare
Deep learning models have shown promising predictive accuracy for time-s...
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04/27/2023
Detection of Adversarial Physical Attacks in Time-Series Image Data
Deep neural networks (DNN) have become a common sensing modality in auto...
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12/02/2022
Guaranteed Conformance of Neurosymbolic Models to Natural Constraints
Deep neural networks have emerged as the workhorse for a large section o...
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10/14/2022
Let's Talk Through Physics! Covert Cyber-Physical Data Exfiltration on Air-Gapped Edge Devices
Although organizations are continuously making concerted efforts to hard...
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06/13/2022
Towards Alternative Techniques for Improving Adversarial Robustness: Analysis of Adversarial Training at a Spectrum of Perturbations
Adversarial training (AT) and its variants have spearheaded progress in ...
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05/17/2022
A Framework for Checkpointing and Recovery of Hierarchical Cyber-Physical Systems
This paper tackles the problem of making complex resource-constrained cy...
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02/25/2022
Exploring with Sticky Mittens: Reinforcement Learning with Expert Interventions via Option Templates
Environments with sparse rewards and long horizons pose a significant ch...
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07/19/2021
CHEF: A Cheap and Fast Pipeline for Iteratively Cleaning Label Uncertainties (Technical Report)
High-quality labels are expensive to obtain for many machine learning ta...
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06/03/2021
Robust Learning via Persistency of Excitation
Improving adversarial robustness of neural networks remains a major chal...
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04/30/2021
ModelGuard: Runtime Validation of Lipschitz-continuous Models
This paper presents ModelGuard, a sampling-based approach to runtime mod...
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02/25/2021
Confidence Calibration with Bounded Error Using Transformations
As machine learning techniques become widely adopted in new domains, esp...
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11/09/2020
Improving Classifier Confidence using Lossy Label-Invariant Transformations
Providing reliable model uncertainty estimates is imperative to enabling...
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02/29/2020
Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation
Reliable uncertainty estimates are an important tool for helping autonom...
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02/23/2020
VisionGuard: Runtime Detection of Adversarial Inputs to Perception Systems
Deep neural network (DNN) models have proven to be vulnerable to adversa...
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12/20/2019
Resilient Cyberphysical Systems and their Application Drivers: A Technology Roadmap
Cyberphysical systems (CPS) are ubiquitous in our personal and professio...
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10/24/2019
Case Study: Verifying the Safety of an Autonomous Racing Car with a Neural Network Controller
This paper describes a verification case study on an autonomous racing c...
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06/20/2018
Self-Driving Vehicle Verification Towards a Benchmark
Industrial cyber-physical systems are hybrid systems with strict safety ...
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08/10/2017
