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Pages
Posts
Double Descent and Bayesian Linear Regression
Published:
This notebook explores the double descent phenomenon through the lens of Bayesian linear regression. It demonstrates how model complexity affects generalization by comparing polynomial and Legendre basis expansions.
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
publications
Gaussian Processes with Bayesian Inference of Covariate Couplings
Published in TMLR, 2025
Beyond ARD: a GP kernel that learns sparse and interpretable covariate couplings.
Mattia Rosso, Juho Ylä-Jaäski, Zheyang Shen, Markus Heinonen, & Maurizio Filippone (2025). Gaussian Processes with Bayesian Inference of Covariate Couplings. Transactions on Machine Learning Research.
Download Paper | Download Bibtex
Scaling Laws for Uncertainty in Deep Learning
Published in Arxiv (preprint), 2025
Predictive uncertainties in deep learning follow scaling laws, showing that epistemic uncertainty persists even with large data.
Mattia Rosso, Simone Rossi, Giulio Franzese, Markus Heinonen, & Maurizio Filippone (2025). Scaling Laws for Uncertainty in Deep Learning.
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summaries
Position: LLMs Can’t Jump
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talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.