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Collaboration networkThis graph maps the connections between all the collaborators of {}'s publications listed on this page.
Each link represents a collaboration on the same publication. The thickness of the link represents the number of collaborations.
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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Mai, V., Maisonneuve, P., Zhang, T., Nekoei, H., Paull, L., & Lesage-Landry, A. (2024). Correction to: Multi-agent reinforcement learning for fast-timescale demand response of residential loads. Machine Learning, 113, 3355-3355. External link
Mai, V., Maisonneuve, P., Zhang, T., Nekoei, H., Paull, L., & Lesage-Landry, A. (2023). Multi-agent reinforcement learning for fast-timescale demand response of residential loads. Machine Learning, 113, 5203-5234. External link
Mai, V., Maisonneuve, P., Zhang, T., Nekoei, H., Paull, L., & Lesage-Landry, A. (2023, May). Multi-Agent Reinforcement Learning for Fast-Timescale Demand Response of Residential Loads [Poster]. 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023), London, UK. Published in Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems. External link
Rowe, L., Girgis, R., Gosselin, A., Paull, L., Pal, C. J., & Heide, F. (2025, June). Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments [Paper]. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2025), Nashville, TN, USA. External link
Rowe, L., Girgis, R., Gosselin, A., Carrez, B., Golemo, F., Heide, F., Paull, L., & Pal, C. J. (2024, November). CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning [Paper]. 8th Annual Conference on Robot Learning (CoRL 2024), Munich, Germany. External link
