RAPTOR: End-to-end Risk-Aware MDP Planning and Policy Learning by Backpropagation. - Noah Patton; Jihwan Jeong; Michael Gimelfarb; Scott Sanner (CoRR, 2021)

A Distributional Framework for Risk-Sensitive End-to-End Planning in Continuous MDPs. - Noah Patton; Jihwan Jeong; Mike Gimelfarb; Scott Sanner (AAAI, 2022)

Program Synthesis for Robot Learning from Demonstrations. - Noah Patton; Kia Rahmani; Meghana Missula; Joydeep Biswas; Isil Dillig (CoRR, 2023)

Synapse: Learning Preferential Concepts from Visual Demonstrations. - Sadanand Modak; Noah Patton; Isil Dillig; Joydeep Biswas (CoRR, 2024)

Programming-by-Demonstration for Long-Horizon Robot Tasks. - Noah Patton; Kia Rahmani; Meghana Missula; Joydeep Biswas; Isil Dillig (Proc. ACM Program. Lang., 2024)

SYNAPSE: SYmbolic Neural-Aided Preference Synthesis Engine. - Sadanand Modak; Noah Tobias Patton; Isil Dillig; Joydeep Biswas (AAAI, 2025)