Bayesian Network Structure Learning with Side Constraints. - Andrew C. Li; Peter Beek (PGM, 2018)

Interpretable Sequence Classification via Discrete Optimization. - Maayan Shvo; Andrew C. Li; Rodrigo Toro Icarte; Sheila A. McIlraith (AAAI, 2021)

LTL2Action: Generalizing LTL Instructions for Multi-Task RL. - Pashootan Vaezipoor; Andrew C. Li; Rodrigo Toro Icarte; Sheila A. McIlraith (ICML, 2021)

Learning to Follow Instructions in Text-Based Games. - Mathieu Tuli; Andrew C. Li; Pashootan Vaezipoor; Toryn Q. Klassen; Scott Sanner; Sheila A. McIlraith (NeurIPS, 2022)

Challenges to Solving Combinatorially Hard Long-Horizon Deep RL Tasks. - Andrew C. Li; Pashootan Vaezipoor; Rodrigo Toro Icarte; Sheila A. McIlraith (CoRR, 2022)

Noisy Symbolic Abstractions for Deep RL: A case study with Reward Machines. - Andrew C. Li; Zizhao Chen; Pashootan Vaezipoor; Toryn Q. Klassen; Rodrigo Toro Icarte; Sheila A. McIlraith (CoRR, 2022)

Learning Belief Representations for Partially Observable Deep RL. - Andrew Wang; Andrew C. Li; Toryn Q. Klassen; Rodrigo Toro Icarte; Sheila A. McIlraith (ICML, 2023)

Learning Symbolic Representations for Reinforcement Learning of Non-Markovian Behavior. - Phillip J. K. Christoffersen; Andrew C. Li; Rodrigo Toro Icarte; Sheila A. McIlraith (CoRR, 2023)

Reward Machines for Deep RL in Noisy and Uncertain Environments. - Andrew C. Li; Zizhao Chen; Toryn Q. Klassen; Pashootan Vaezipoor; Rodrigo Toro Icarte; Sheila A. McIlraith (NeurIPS, 2024)

Ground-Compose-Reinforce: Tasking Reinforcement Learning Agents through Formal Language. - Andrew C. Li; Toryn Q. Klassen; Andrew Wang; Parand A. Alamdari; Sheila A. McIlraith (CoRR, 2025)