Items where Author is "Marcotte, Étienne"
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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.
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de Boeck, J., Fortz, B., Labbé, M., Marcotte, É., Marcotte, P., & Savard, G. (2025). Bidding in day-ahead electricity markets: A dynamic programming framework. Computers & Operations Research, 179, 107024. External link
Williams, A. R., Ashok, A., Marcotte, É., Zantedeschi, V., Subramanian, J., Riachi, R., Requeima, J., Lacoste, A., Rish, I., Chapados, N., & Drouin, A. (2025, February). Context is Key: A Benchmark for Forecasting with Essential Textual Information [Paper]. 42nd International Conference on Machine Learning (PMLR 2025), Vancouver, BC, Canada. External link
Monteiro, J., Marcotte, É., Noël, P.-A., Zantedeschi, V., Vázquez, D., Chapados, N., Pal, C. J., & Taslakian, P. (2024, November). XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference [Paper]. Findings of the Association for Computational Linguistics (ACL 2024), Miami, FL, USA. External link
Ashok, A., Williams, A. R., Marcotte, É., Zantedeschi, V., Subramanian, J., Riachi, R., Requeima, J., Lacoste, A., Rish, I., Chapados, N., & Drouin, A. (2024, December). Context is Key: A Benchmark for Forecasting with Essential Textual Information [Paper]. NeurIPS 2024 Workshop on Time Series in the Age of Large Models, Vancouver, BC, Canada (56 pages). External link