Sheila McIlraith
Professor
Studies AI planning, reinforcement learning with reward machines and human-compatible AI.
Sheila McIlraith's lab at Toronto works on reinforcement learning. 38 papers since 2023, 12 at top venues, mostly NeurIPS and AAAI. 26 alumni. Applications close .
At a glance
- papers, 2023–2026
- 10/ year
- at T1 venues
- 32%
- PhD students
- 0
- MS students
- 0
- alumni listed
- 26
Papers by yearfrom the lab homepage, 2023–2026
- 5 papers2026 · T1 1 · collecting
- 10 papers2025 · T1 3
- 11 papers2024 · T1 4
- 12 papers2023 · T1 4
T1 venuesOtherVenues: NeurIPS, AAAI, ICML, KR, SIGCSE
Research focuspaper titles that use each topic's words
- Reinforcement learning8 papers
- planning6 papers
- reward4 papers
- machines4 papers
- reinforcement4 papers
Paper list
From the lab homepage.
20265 papers
- FAccTFormal Methods Meet LLMs: Auditing, Monitoring, and Intervention for Compliance of Advanced AI Systems (opens in a new tab)
- SaTMLGauss-Newton Unlearning for the LLM Era (opens in a new tab)
- RLCMulti-Agent Reinforcement Learning with Reward Machines for Mixed Cooperative-Competitive Environments
- AAAI WorkshopOptimizing a Margin of Safety via Prompt Repair for Large Language Models
- AAAI (T1)Satisficing and Optimal Generalised Planning via Goal Regression (opens in a new tab)
202510 papers
- ICML WorkshopAuditing, Monitoring, and Intervention for Compliance of Advanced AI Systems
- NeurIPS (T1)Better Training Data Attribution via Better Inverse Hessian-Vector Products (opens in a new tab)
- NeurIPS (T1)Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia (opens in a new tab)
- NeurIPS (T1)Ground-Compose-Reinforce: Tasking Reinforcement Learning Agents through Formal Language (opens in a new tab)
- ICML WorkshopIt's Rational for Agents to Procrastinate
- EWRLLanguage Models For Generalised PDDL Planning: Synthesising Sound and Programmatic Policies (opens in a new tab)
- TKRMOOSE: Satisficing and Optimal Generalised Planning via Goal Regression
- COLMMulti-Agent Verification: Scaling Test-Time Compute with Multiple Verifiers (opens in a new tab)
- Journal of Artificial Intelligence ResearchOptimal Decision Trees for Interpretable and Constrained Clustering (opens in a new tab)
- KRPushdown Reward Machines for Reinforcement Learning (opens in a new tab)
202411 papers
- Pluralistic Alignment @ NeurIPS WorkshopBeing Considerate as a Pathway Towards Pluralistic Alignment for Agentic AI (opens in a new tab)
- SIGCSEDo Embedded Ethics Modules Have Impact Beyond the Classroom? (opens in a new tab)
- ACM EngageCSEduEmbedded Ethics: Pandemic Contact Tracing and Ethical Trade-offs (opens in a new tab)
- ACM EngageCSEduEmbedded Ethics: Pandemic Exposure Notification Systems and Giving Ethical Justifications (opens in a new tab)
- Science (T1)Managing extreme AI risks amid rapid progress (opens in a new tab)
- SoCSNeural Sequence Generation with Constraints via Beam Search with Cuts: A Case Study on VRP (opens in a new tab)
- CACMNow, Later, and Lasting: Ten Priorities for AI Research, Policy, and Practice (opens in a new tab)
- Pluralistic Alignment @ NeurIPS WorkshopPluralistic Alignment Over Time (opens in a new tab)
- AAAI (T1)PRP Rebooted: Advancing the State of the Art in FOND Planning (opens in a new tab)
- ICML (T1)Remembering to Be Fair: On Non-Markovian Fairness in Sequential Decision Making (opens in a new tab)
- NeurIPS (T1)Reward Machines for Deep RL in Noisy and Uncertain Environments (opens in a new tab)
202312 papers
- Workshop on Interactive Learning with Implicit Human Feedback (ILHF) Workshop @ICML23A Generative Model for Text Control in Minecraft
- Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS)Epistemic Side Effects: An AI Safety Problem (opens in a new tab)
- Workshop on Structured Probabilistic Inference \& Generative Modeling @ICML23Generative Model for Text Control in Minecraft (Abridged Version)
- SIGCSEIs More Better When Embedding Ethics in CS Courses? (opens in a new tab)
- ICML (T1)Learning Belief Representations for Partially Observable Deep RL (opens in a new tab)
- Artificial Intelligence (T1)Learning Reward Machines: A Study in Partially Observable Reinforcement Learning (opens in a new tab)
- IJCAI (T1)Optimal Decision Trees For Interpretable Clustering with Constraints
- KRPlanning with Epistemic Preferences (opens in a new tab)
- CPSAT-Based Learning of Compact Binary Decision Diagrams for Classification (opens in a new tab)
- ConstraintsSAT-based optimal classification trees for non-binary data (opens in a new tab)
- NeurIPS (T1)STEVE-1: A Generative Model for Text-to-Behavior in Minecraft (opens in a new tab)
- HAXPTowards Human-Aware AI via Planning with Epistemic Preferences (opens in a new tab)
Where alumni went26 listed, 0% to industry
Industry 0Academia 1
Also to
- USC
Apply to TorontoFall 2027
Computer Science MSc/PhD
- Round
- Single round (Dec 1)
- Opens
- —
- GRE
- optional
- English
- TOEFL/IELTS if applicable
- Fee
- CAD 135
Confirmed on the program page.
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34 T1
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