Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning. - Flemming Kondrup; Thomas Jiralerspong; Elaine Lau; Nathan de Lara; Jacob Shkrob; My Duc Tran; Doina Precup; Sumana Basu (AAAI, 2023)

Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RL. - Chen Sun 007; Wannan Yang; Thomas Jiralerspong; Dane Malenfant; Benjamin Alsbury-Nealy; Yoshua Bengio; Blake A. Richards (NeurIPS, 2023)

A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control. - Marshall Wang; John Willes; Thomas Jiralerspong; Matin Moezzi (CoRR, 2023)

Forecaster: Towards Temporally Abstract Tree-Search Planning from Pixels. - Thomas Jiralerspong; Flemming Kondrup; Doina Precup; Khimya Khetarpal (CoRR, 2023)

Network Analysis of the iNaturalist Citizen Science Community. - Yu Lu Liu; Thomas Jiralerspong (CoRR, 2023)

Efficient Causal Graph Discovery Using Large Language Models. - Thomas Jiralerspong; Xiaoyin Chen; Yash More; Vedant Shah; Yoshua Bengio (CoRR, 2024)

A Complexity-Based Theory of Compositionality. - Eric Elmoznino; Thomas Jiralerspong; Yoshua Bengio; Guillaume Lajoie (CoRR, 2024)

General Causal Imputation via Synthetic Interventions. - Marco Jiralerspong; Thomas Jiralerspong; Vedant Shah; Dhanya Sridhar; Gauthier Gidel (CoRR, 2024)

Geometric Signatures of Compositionality Across a Language Model's Lifetime. - Jin Hwa Lee; Thomas Jiralerspong; Lei Yu; Yoshua Bengio; Emily Cheng (ACL (1), 2025)

Expressivity of Neural Networks with Random Weights and Learned Biases. - Ezekiel Williams; Alexandre Payeur; Avery Hee-Woon Ryoo; Thomas Jiralerspong; Matthew G. Perich; Luca Mazzucato; Guillaume Lajoie (ICLR, 2025)

Shaping Inductive Bias in Diffusion Models through Frequency-Based Noise Control. - Thomas Jiralerspong; Berton Earnshaw; Jason S. Hartford; Yoshua Bengio; Luca Scimeca (CoRR, 2025)