Stochastic Reachability for Systems up to a Million Dimensions. - Adam J. Thorpe; Vignesh Sivaramakrishnan; Meeko M. K. Oishi (CoRR, 2019)

Trust-based user-interface design for human-automation systems. - Abraham P. Vinod; Adam J. Thorpe; Philip A. Olaniyi; Tyler H. Summers; Meeko M. K. Oishi (CoRR, 2020)

Data-Driven Stochastic Reachability Using Hilbert Space Embeddings. - Adam J. Thorpe; Kendric R. Ortiz; Meeko M. K. Oishi (CoRR, 2020)

Model-Free Stochastic Reachability Using Kernel Distribution Embeddings. - Adam J. Thorpe; Meeko M. K. Oishi (IEEE Control. Syst. Lett., 2020)

Approximate Stochastic Reachability for High Dimensional Systems. - Adam J. Thorpe; Vignesh Sivaramakrishnan; Meeko M. K. Oishi (ACC, 2021)

ARCH-COMP21 Category Report: Stochastic Models. - Alessandro Abate; Henk A. P. Blom; Marc Bouissou; Nathalie Cauchi; Hassane Chraibi; Joanna Delicaris; Sofie Haesaert; Arnd Hartmanns; Mahmoud Khaled; Abolfazl Lavaei; Hao Ma; Kaushik Mallik; et al. (ARCH@ADHS, 2021)

Stochastic Optimal Control via Hilbert Space Embeddings of Distributions. - Adam J. Thorpe; Meeko M. K. Oishi (CDC, 2021)

SReachTools Kernel Module: Data-Driven Stochastic Reachability Using Hilbert Space Embeddings of Distributions. - Adam J. Thorpe; Kendric R. Ortiz; Meeko M. K. Oishi (CDC, 2021)

Learning Approximate Forward Reachable Sets Using Separating Kernels. - Adam J. Thorpe; Kendric R. Ortiz; Meeko M. K. Oishi (L4DC, 2021)

SOCKS: A Stochastic Optimal Control and Reachability Toolbox Using Kernel Methods. - Adam J. Thorpe; Meeko Oishi (HSCC, 2022)

Data-Driven Chance Constrained Control using Kernel Distribution Embeddings. - Adam J. Thorpe; Thomas Lew; Meeko Oishi; Marco Pavone 001 (L4DC, 2022)

State-based confidence bounds for data-driven stochastic reachability using Hilbert space embeddings. - Adam J. Thorpe; Kendric R. Ortiz; Meeko M. K. Oishi (Autom., 2022)

Characterizing Within-Driver Variability in Driving Dynamics During Obstacle Avoidance Maneuvers. - Kendric R. Ortiz; Adam J. Thorpe; AnaMaria Perez; Maya Luster; Brandon J. Pitts; Meeko Oishi (CoRR, 2022)

Sensor Selection for Dynamics-Driven User-Interface Design. - Abraham P. Vinod; Adam J. Thorpe; Philip A. Olaniyi; Tyler H. Summers; Meeko M. K. Oishi (IEEE Trans. Control. Syst. Technol., 2022)

Data-Driven Stochastic Optimal Control Using Kernel Gradients. - Adam J. Thorpe; Jake A. Gonzales; Meeko M. K. Oishi (ACC, 2023)

Refining Human-Centered Autonomy Using Side Information. - Adam J. Thorpe (CoRR, 2023)

GPSINDy: Data-Driven Discovery of Equations of Motion. - Junette Hsin; Shubhankar Agarwal; Adam J. Thorpe; David Fridovich-Keil (CoRR, 2023)

Physics-Informed Kernel Embeddings: Integrating Prior System Knowledge with Data-Driven Control. - Adam J. Thorpe; Cyrus Neary; Franck Djeumou; Meeko M. K. Oishi; Ufuk Topcu (ACC, 2024)

Using Reward Shaping to Train Cognitive-Based Control Policies for Intelligent Tutoring Systems. - Madeleine Yuh; Ethan Rabb; Adam J. Thorpe; Neera Jain (ACC, 2024)

Active Learning of Dynamics Using Prior Domain Knowledge in the Sampling Process. - Kevin S. Miller; Adam J. Thorpe; Ufuk Topcu (CDC, 2024)

Online Learning of Dynamical Systems Using Low-Rank Updates to Physics-Informed Kernel Distribution Embeddings. - Kendric R. Ortiz; Rachel DiPirro; Adam J. Thorpe; Meeko Oishi (CDC, 2024)

Sensing Resource Allocation Against Data-Poisoning Attacks in Traffic Routing. - Yue Yu 004; Adam J. Thorpe; Jesse Milzman; David Fridovich-Keil; Ufuk Topcu (CDC, 2024)

Zero-Shot Transfer of Neural ODEs. - Tyler Ingebrand; Adam J. Thorpe; Ufuk Topcu (NeurIPS, 2024)

Act Natural! Projecting Autonomous System Trajectories Into Naturalistic Behavior Sets. - Hamzah Khan; Adam J. Thorpe; David Fridovich-Keil (CoRR, 2024)

Basis-to-Basis Operator Learning Using Function Encoders. - Tyler Ingebrand; Adam J. Thorpe; Somdatta Goswami; Krishna Kumar; Ufuk Topcu (CoRR, 2024)

Characterizing the Effect of Mind Wandering on Braking Dynamics in Partially Autonomous Vehicles. - Harini Sridhar; Gaojian Huang; Adam J. Thorpe; Meeko Oishi; Brandon J. Pitts (ACM Trans. Cyber Phys. Syst., 2024)

Symbolic Regression on Sparse and Noisy Data with Gaussian Processes. - Junette Hsin; Shubhankar Agarwal; Adam J. Thorpe; Luis Sentis; David Fridovich-Keil (ACC, 2025)

Function Encoders: A Principled Approach to Transfer Learning in Hilbert Spaces. - Tyler Ingebrand; Adam J. Thorpe; Ufuk Topcu (CoRR, 2025)

Online Adaptation of Terrain-Aware Dynamics for Planning in Unstructured Environments. - William Ward; Sarah Etter; Tyler Ingebrand; Christian Ellis; Adam J. Thorpe; Ufuk Topcu (CoRR, 2025)