Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks. - Joshua J. Michalenko; Ameesh Shah; Abhinav Verma 001; Richard G. Baraniuk; Swarat Chaudhuri; Ankit B. Patel (ICLR (Poster), 2019)

Learning Differentiable Programs with Admissible Neural Heuristics. - Ameesh Shah; Eric Zhan; Jennifer J. Sun; Abhinav Verma 001; Yisong Yue; Swarat Chaudhuri (NeurIPS, 2020)

Demonstration Informed Specification Search. - Marcell Vazquez-Chanlatte; Ameesh Shah; Gil Lederman; Sanjit A. Seshia (CoRR, 2021)

Learning Deterministic Finite Automata Decompositions from Examples and Demonstrations. - Niklas Lauffer; Beyazit Yalcinkaya; Marcell Vazquez-Chanlatte; Ameesh Shah; Sanjit A. Seshia (FMCAD, 2022)

Modeling and Influencing Human Attentiveness in Autonomy-to-Human Perception Hand-offs. - Yash Vardhan Pant; Balasaravanan Thoravi Kumaravel; Ameesh Shah; Erin Kraemer; Marcell Vazquez-Chanlatte; Kshitij Kulkarni; Bjoern Hartmann; Sanjit A. Seshia (ITSC, 2022)

Who Needs to Know? Minimal Knowledge for Optimal Coordination. - Niklas Lauffer; Ameesh Shah; Micah Carroll; Michael D. Dennis; Stuart Russell 001 (ICML, 2023)

Specification-Guided Data Aggregation for Semantically Aware Imitation Learning. - Ameesh Shah; Jonathan A. DeCastro; John Gideon; Beyazit Yalcinkaya; Guy Rosman; Sanjit A. Seshia (CoRR, 2023)

Deep Policy Optimization with Temporal Logic Constraints. - Ameesh Shah; Cameron Voloshin; Chenxi Yang; Abhinav Verma 001; Swarat Chaudhuri; Sanjit A. Seshia (CoRR, 2024)

Learning Symbolic Task Decompositions for Multi-Agent Teams. - Ameesh Shah; Niklas Lauffer; Thomas Chen; Nikhil Pitta; Sanjit A. Seshia (AAMAS, 2025)

Learning Formal Specifications from Membership and Preference Queries. - Ameesh Shah; Marcell Vazquez-Chanlatte; Sebastian Junges; Sanjit A. Seshia (NeuS, 2025)

LTL-Constrained Policy Optimization with Cycle Experience Replay. - Ameesh Shah; Cameron Voloshin; Chenxi Yang; Abhinav Verma 001; Swarat Chaudhuri; Sanjit A. Seshia (Trans. Mach. Learn. Res., 2025)