Up a level |
This 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.
Use the mouse wheel or scroll gestures to zoom into the graph.
You can click on the nodes and links to highlight them and move the nodes by dragging them.
Hold down the "Ctrl" key or the "⌘" key while clicking on the nodes to open the list of this person's publications.
A 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 with Applications, 1 page. External link
Nekoei, H., Badrinaaraayanan, A., Sinha, A., Amini, M., Rajendran, J., Mahajan, A., & Anbil Parthipan, S. C. (2023, August). Dealing with non-stationarity in decentralized cooperative multi-agent deep reinforcement learning via multi-timescale learning [Paper]. 2nd Conference on Lifelong Learning Agents (CoLLAs 2023), Montreal, Qc. Canada. Unavailable
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 with Applications, 32 pages. External link
Nekoei, H., Zhao, X. T., Rajendran, J., Liu, M. A., & Anbil Parthipan, S. C. (2023, August). Towards few-shot coordination : revisiting ad-hoc teamplay challenge in the game of Hanabi [Paper]. 2nd Conference on Lifelong Learning Agents (CoLLAs 2023), Montreal, Qc, Canada. Unavailable
Nekoei, H., Badrinaaraayanan, A., Courville, A., & Anbil Parthipan, S. C. (2021, July). Continuous Coordination As a Realistic Scenario for Lifelong Learning [Paper]. International Conference on Machine Learning (ICML 2021). External link
Van Seijen, H., Nekoei, H., Racah, E., & Anbil Parthipan, S. C. (2020, December). The LoCA regret: A consistent metric to evaluate model-based behavior in reinforcement learning [Paper]. 34th Conference on Neural Information Processing Systems (NeurIPS 2020). Unavailable