Published in 27th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 238:2026–2034, 2024
A discriminator-free, sample-based alternative to adversarial GAN training for implicit generative models, based on the Invariant Statistical Loss (ISL).
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Published in International Conference on Machine Learning (ICML) 2026, 2026
Rank-statistic approximations of f-divergences that can be estimated directly from samples, with applications to sample-based comparison of real and generated distributions and relevance to f-GAN-style objectives.
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Published in 29th International Conference on Artificial Intelligence and Statistics (AISTATS) — Spotlight, 2026
Dual-ISL provides a convex, likelihood-free and discriminator-free objective for neural implicit samplers, offering a stable alternative to adversarial GAN-style training.
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Published in Journal of Machine Learning Research, 27(122):1–49, 2026
A stable, discriminator-free alternative to adversarial GAN training for implicit generative models, extended to multivariate and heavy-tailed distributions with Pareto-ISL and ISL-slicing.
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Research seminar on rank-based statistical learning and implicit generative modeling, with emphasis on sample-based objectives, distribution comparison, and robust training.
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Research presentation in the Mathematics and Machine Learning special session on rank-based statistical learning, distribution comparison, and implicit generative modeling.
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Research seminar on sample-based approximation of f-divergences using rank statistics, including density-ratio-free estimation, sliced extensions, and applications to generative modeling.
Undergraduate course, University Carlos III of Madrid, Spain, 2023
Undergraduate course, University Carlos III of Madrid, Spain, 2024
Corporate training, Banco Bilbao Vizcaya Argentaria (BBVA), 2025
Course Instructor for BBVA internal Machine Learning training programs.
Student supervision, University Carlos III of Madrid, Spain, 2026