Machine learning · learning to rank · optimal transport · causal inference

I'm a Senior Research Scientist at Rakuten Group, Inc. in Paris, in the Search & Relevance team, where I design deep-learning-based learning-to-rank systems at scale for Rakuten Ichiba: custom models and their training pipelines. Before that, I spent eight years at the Rakuten Institute of Technology, working on recommender systems and, earlier, collaborating with Rakuten businesses such as Rakuten France and Rakuten Viber. My recent work covers optimal transport, learning to rank and counterfactual evaluation for e-commerce search, and bias correction for treatment-effect estimation.

I did my PhD at the Microsoft Research – Inria Joint Centre (Parietal team) and Université Paris-Saclay, building fast and stable statistical learning methods for large-scale brain imaging (fMRI). More about me →

Recent publications

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  P. Loyola, K. Hasegawa, A. Hoyos-Idrobo, K. Ono, T. Suzumura, Y. Hirate, M. Yamaoka. Annealing machine-assisted learning of graph neural network for combinatorial optimization. NeurIPS 2024 Workshop on Machine Learning with New Compute Paradigms, 2024.
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  A. Abraham, A. Hoyos-Idrobo. Improving bias correction standards by quantifying its effects on treatment outcomes. ECML-PKDD 2024 Workshops, Communications in Computer and Information Science, 189–204, 2024. [arXiv]
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  P. Loyola, E. Marrese-Taylor, A. Hoyos-Idrobo. Perceptual structure in the absence of grounding: the impact of abstractedness and subjectivity in color language for LLMs. Findings of the Association for Computational Linguistics: EMNLP 2023, 1536–1542, 2023. [arXiv]
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  A. Hoyos-Idrobo. Learning to re-rank with constrained meta-optimal transport. Proceedings of the 46th International ACM SIGIR Conference (SIGIR 2023), 48–57, 2023. [arXiv]
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  P. Goswami, A. Hoyos-Idrobo, T. Deschamps, N. Feuillet. From theory to practice: testing harvesting-based propensity estimators on counterfactual learning-to-rank from implicit feedback for e-commerce search. SIGIR Workshop on eCommerce (SIGIR eCom 2022), 2022.

All publications →