1. ROSSI Simone
  2. ROSSI Simone
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ROSSI Simone

Conférence

Vargas, Luisa; Rossi, Simone; Zuluaga, Maria A.

Beyond morphological dilation: Revisiting conformal prediction for 3D tumor segmentation

UNSURE 2026, MICCAI Workshop Uncertainty for Safe utilization of Machine learning in Medical Imaging, 27 September 2026, Strasbourg, France
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Ouvrage

Agostinelli, Claudio; Aitchison, Laurence; Aldea, Emanuel; Allmendinger, Richard; Ament, Sebastian; Anson, Ben; Arbel, Julyan; Bakshy, Eytan; Balandat, Max; Bariletto, Nicola; et al.

Handbook of Bayesian deep learning

BayesAI Consortium, 24 August 2026
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DOI
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Poster / Demo

Qchohi, Abdessamed; Rossi, Simone

A Bayesian perspective on learning to grok in-context examples

PROBLM 2026, Symposium on Probabilistic Machine Learning, co-located with ICML 2026, 5 July 2026, Seoul, South Korea
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Journal

Papicchio, Simone; Rossi, Simone; Cagliero, Luca; Papotti, Paolo

Think2SQL: Blueprinting reward density and advantage scaling for effective text-to-SQL reasoning

Transactions on Machine Learning Research (TMLR), May 2026
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Talk

Rossi, Simone

Uncertainty Quantification with Bayesian Deep Learning: Foundations, Challenges, and Lessons Learned

MOMI 2026, Invited talk in 10th edition of "Le Monde des Mathématiques Industrielles", 28 April 2026, Sophia Antipolis, France
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Rapport

Qchohi, Abdessamed; Rossi, Simone

A Bayesian perspective on the role of epistemic uncertainty for delayed generalization in in-context learning

Submitted to ArXiV, 14 April 2026
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ARXIV
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Journal

Xie, Shifeng; Yuan, Rui; Rossi, Simone; Hannagan, Thomas

The initialization determines whether in-context learning is gradient descent

Transactions on Machine Learning Research, December 2025
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ARXIV
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Poster / Demo

Rosso, Mattia; Rossi, Simone; Franzese, Giulio; Heinonen, Markus; Filippone, Maurizio

Scaling laws for uncertainty in deep learning

EIML 2025, Epistemic Intelligence in Machine Learning, 6 December 2025, Copenhagen, Denmark
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Poster / Demo

Xie, Shifeng; Yuan, Rui; Rossi, Simone; Hannagan, Thomas

The initialization determines whether in-context learning is gradient descent

NeurIPS 2025, Workshop, What Can('t) Transformers Do?, 39th Annual Conference on Neural Information Processing Systems, 2-7 December 2025, San Diego, USA
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Rapport

Rosso, Mattia; Rossi, Simone; Franzese, Giulio; Heinonen, Markus; Filippone, Maurizio

Scaling laws for uncertainty in deep learning

Submitted to ArXiV, 11 June 2025
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ARXIV
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