Search Results for author: Matthew Cleaveland

Found 7 papers, 4 papers with code

Multi-Modal Conformal Prediction Regions by Optimizing Convex Shape Templates

1 code implementation12 Dec 2023 Renukanandan Tumu, Matthew Cleaveland, Rahul Mangharam, George J. Pappas, Lars Lindemann

However, little work has gone into finding non-conformity score functions that produce prediction regions that are multi-modal and practical, i. e., that can efficiently be used in engineering applications.

Autonomous Vehicles Conformal Prediction +2

Causal Repair of Learning-enabled Cyber-physical Systems

no code implementations6 Apr 2023 Pengyuan Lu, Ivan Ruchkin, Matthew Cleaveland, Oleg Sokolsky, Insup Lee

However, given the high diversity and complexity of LECs, it is challenging to encode domain knowledge (e. g., the CPS dynamics) in a scalable actual causality model that could generate useful repair suggestions.

counterfactual OpenAI Gym

Conformal Prediction Regions for Time Series using Linear Complementarity Programming

1 code implementation3 Apr 2023 Matthew Cleaveland, Insup Lee, George J. Pappas, Lars Lindemann

In fact, to obtain prediction regions over $T$ time steps with confidence $1-\delta$, {previous works require that each individual prediction region is valid} with confidence $1-\delta/T$.

Conformal Prediction Time Series +1

Risk Verification of Stochastic Systems with Neural Network Controllers

no code implementations26 Aug 2022 Matthew Cleaveland, Lars Lindemann, Radoslav Ivanov, George Pappas

Motivated by the fragility of neural network (NN) controllers in safety-critical applications, we present a data-driven framework for verifying the risk of stochastic dynamical systems with NN controllers.

Confidence Composition for Monitors of Verification Assumptions

1 code implementation3 Nov 2021 Ivan Ruchkin, Matthew Cleaveland, Radoslav Ivanov, Pengyuan Lu, Taylor Carpenter, Oleg Sokolsky, Insup Lee

To predict safety violations in a verified system, we propose a three-step confidence composition (CoCo) framework for monitoring verification assumptions.

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