7 citations · 15 across the 17 of their papers we have counts for
19 papers
Class-Conditional Conformal Prediction for Signal Temporal Logic Inference with Many Classes
Yixuan Wang, Danyang Li, Matthew Cleaveland +2
Signal Temporal Logic (STL) inference provides interpretable formulas for time series classification. For multiclass classification, however, accurate predictions do not necessaril…
Conformalized Signal Temporal Logic Inference under Covariate Shift
Yixuan Wang, Danyang Li, Matthew Cleaveland +2
Signal Temporal Logic (STL) inference learns interpretable logical rules for temporal behaviors in dynamical systems. To ensure the correctness of learned STL formulas, recent appr…
Real-Time Optical Communication Using Event-Based Vision with Moving Transmitters
Harmeet Dhillon, Pranay Katyal, Brendan Long +3
In multi-robot systems, traditional radio frequency (RF) communication struggles with contention and jamming. Optical communication offers a strong alternative. However, convention…
Conformal Prediction for Signal Temporal Logic Inference
Danyang Li, Yixuan Wang, Matthew Cleaveland +2
Signal Temporal Logic (STL) inference seeks to extract human-interpretable rules from time-series data, but existing methods lack formal confidence guarantees for the inferred rule…
Domain-Aware Machine Learning for Accelerating MILP-Based Motion Planning with Temporal Logic and Chance Constraints
Junyang Cai, Weimin Huang, Brendan Long +4
Motion-planning problems with temporal-logic or chance constraints are often encoded as mixed-integer linear programs (MILPs). Although these encodings provide rigorous specificati…
Conservative Perception Models for Probabilistic Verification
Matthew Cleaveland, Pengyuan Lu, Oleg Sokolsky +2
Verifying the behaviors of autonomous systems with learned perception components is a challenging problem due to the complexity of the perception and the uncertainty of operating e…
Multi-Modal Conformal Prediction Regions with Simple Structures by Optimizing Convex Shape Templates
Renukanandan Tumu, Matthew Cleaveland, Rahul Mangharam +2
Conformal prediction is a statistical tool for producing prediction regions for machine learning models that are valid with high probability. A key component of conformal predictio…
Repairing Learning-Enabled Controllers While Preserving What Works
Pengyuan Lu, Matthew Cleaveland, Oleg Sokolsky +2
Learning-enabled controllers have been adopted in various cyber-physical systems (CPS). When a learning-enabled controller fails to accomplish its task from a set of initial states…
Distributionally Robust Statistical Verification with Imprecise Neural Networks
Souradeep Dutta, Michele Caprio, Vivian Lin +5
A particularly challenging problem in AI safety is providing guarantees on the behavior of high-dimensional autonomous systems. Verification approaches centered around reachability…
Causal Repair of Learning-enabled Cyber-physical Systems
Pengyuan Lu, Ivan Ruchkin, Matthew Cleaveland +2
Models of actual causality leverage domain knowledge to generate convincing diagnoses of events that caused an outcome. It is promising to apply these models to diagnose and repair…
Conformal Prediction Regions for Time Series using Linear Complementarity Programming
Matthew Cleaveland, Insup Lee, George J. Pappas +1
Conformal prediction is a statistical tool for producing prediction regions of machine learning models that are valid with high probability. However, applying conformal prediction…
Conservative Safety Monitors of Stochastic Dynamical Systems
Matthew Cleaveland, Oleg Sokolsky, Insup Lee +1
Generating accurate runtime safety estimates for autonomous systems is vital to ensuring their continued proliferation. However, exhaustive reasoning about future behaviors is gene…
Adaptive Conformal Prediction for Motion Planning among Dynamic Agents
Anushri Dixit, Lars Lindemann, Skylar Wei +3
This paper proposes an algorithm for motion planning among dynamic agents using adaptive conformal prediction. We consider a deterministic control system and use trajectory predict…
Safe Planning in Dynamic Environments using Conformal Prediction
Lars Lindemann, Matthew Cleaveland, Gihyun Shim +1
We propose a framework for planning in unknown dynamic environments with probabilistic safety guarantees using conformal prediction. Particularly, we design a model predictive cont…
Learning Enabled Fast Planning and Control in Dynamic Environments with Intermittent Information
Matthew Cleaveland, Esen Yel, Yiannis Kantaros +2
This paper addresses a safe planning and control problem for mobile robots operating in communication- and sensor-limited dynamic environments. In this case the robots cannot sense…
Risk Verification of Stochastic Systems with Neural Network Controllers
Matthew Cleaveland, Lars Lindemann, Radoslav Ivanov +1
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 sys…
Confidence Composition for Monitors of Verification Assumptions
Ivan Ruchkin, Matthew Cleaveland, Radoslav Ivanov +4
Closed-loop verification of cyber-physical systems with neural network controllers offers strong safety guarantees under certain assumptions. It is, however, difficult to determine…
Monotonic Safety for Scalable and Data-Efficient Probabilistic Safety Analysis
Matthew Cleaveland, Ivan Ruchkin, Oleg Sokolsky +1
Autonomous systems with machine learning-based perception can exhibit unpredictable behaviors that are difficult to quantify, let alone verify. Such behaviors are convenient to cap…
Robust Motion Planning in the Presence of Estimation Uncertainty
Lars Lindemann, Matthew Cleaveland, Yiannis Kantaros +1
Motion planning is a fundamental problem and focuses on finding control inputs that enable a robot to reach a goal region while safely avoiding obstacles. However, in many situatio…