Verifying Handcoded Probabilistic Inference Procedures. - Eric Atkinson; Cambridge Yang; Michael Carbin (CoRR, 2018)

Compiler Auto-Vectorization with Imitation Learning. - Charith Mendis; Cambridge Yang; Yewen Pu; Saman P. Amarasinghe; Michael Carbin (NeurIPS, 2019)

Simplifying Multiple-Statement Reductions with the Polyhedral Model. - Cambridge Yang; Eric Atkinson; Michael Carbin (CoRR, 2020)

Reinforcement Learning for General LTL Objectives Is Intractable. - Cambridge Yang; Michael L. Littman; Michael Carbin (CoRR, 2021)

Simplifying dependent reductions in the polyhedral model. - Cambridge Yang; Eric Atkinson; Michael Carbin (Proc. ACM Program. Lang., 2021)

On the (In)Tractability of Reinforcement Learning for LTL Objectives. - Cambridge Yang; Michael L. Littman; Michael Carbin (IJCAI, 2022)

Computably Continuous Reinforcement-Learning Objectives Are PAC-Learnable. - Cambridge Yang; Michael Littman 002; Michael Carbin (AAAI, 2023)