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09/12/2023
Promises of Deep Kernel Learning for Control Synthesis
Deep Kernel Learning (DKL) combines the representational power of neural...
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06/23/2023
Adversarial Robustness Certification for Bayesian Neural Networks
We study the problem of certifying the robustness of Bayesian neural net...
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06/19/2023
BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic Programming
In this paper, we introduce BNN-DP, an efficient algorithmic framework f...
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04/21/2023
Individual Fairness in Bayesian Neural Networks
We study Individual Fairness (IF) for Bayesian neural networks (BNNs). S...
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11/02/2022
Interval Markov Decision Processes with Continuous Action-Spaces
Interval Markov Decision Processes (IMDPs) are uncertain Markov models, ...
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07/13/2022
On the Robustness of Bayesian Neural Networks to Adversarial Attacks
Vulnerability to adversarial attacks is one of the principal hurdles to ...
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06/15/2022
Safety Guarantees for Neural Network Dynamic Systems via Stochastic Barrier Functions
Neural Networks (NNs) have been successfully employed to represent the s...
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06/03/2022
Safety Certification for Stochastic Systems via Neural Barrier Functions
Providing non-trivial certificates of safety for non-linear stochastic s...
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05/11/2022
Individual Fairness Guarantees for Neural Networks
We consider the problem of certifying the individual fairness (IF) of fe...
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12/31/2021
Formal Verification of Unknown Dynamical Systems via Gaussian Process Regression
Leveraging autonomous systems in safety-critical scenarios requires veri...
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05/21/2021
Certification of Iterative Predictions in Bayesian Neural Networks
We consider the problem of computing reach-avoid probabilities for itera...
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04/07/2021
Adversarial Robustness Guarantees for Gaussian Processes
Gaussian processes (GPs) enable principled computation of model uncertai...
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02/10/2021
Bayesian Inference with Certifiable Adversarial Robustness
We consider adversarial training of deep neural networks through the len...
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12/23/2020
Gradient-Free Adversarial Attacks for Bayesian Neural Networks
The existence of adversarial examples underscores the importance of unde...
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10/01/2020
Assessing Robustness of Text Classification through Maximal Safe Radius Computation
Neural network NLP models are vulnerable to small modifications of the i...
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04/21/2020
Probabilistic Safety for Bayesian Neural Networks
We study probabilistic safety for Bayesian Neural Networks (BNNs) under ...
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02/11/2020
Robustness of Bayesian Neural Networks to Gradient-Based Attacks
Vulnerability to adversarial attacks is one of the principal hurdles to ...
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11/29/2019
Safety Guarantees for Planning Based on Iterative Gaussian Processes
Gaussian Processes (GPs) are widely employed in control and learning bec...
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09/21/2019
Uncertainty Quantification with Statistical Guarantees in End-to-End Autonomous Driving Control
Deep neural network controllers for autonomous driving have recently ben...
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05/28/2019
Robustness Quantification for Classification with Gaussian Processes
We consider Bayesian classification with Gaussian processes (GPs) and de...
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03/05/2019
Statistical Guarantees for the Robustness of Bayesian Neural Networks
We introduce a probabilistic robustness measure for Bayesian Neural Netw...
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09/17/2018
Robustness Guarantees for Bayesian Inference with Gaussian Processes
Bayesian inference and Gaussian processes are widely used in application...
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04/23/2018
Central Limit Model Checking
We consider probabilistic model checking for continuous-time Markov chai...
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10/22/2017
