Giovanni Saraceno
@giovannisaraceno.bsky.social
Fixed-term Assistant Professor at the Department of Statistical Sciences at University of Padova, Italia.
Robust statistics, directional statistics, kernel-based methods, neural networks, neuroscience applications.
Robust statistics, directional statistics, kernel-based methods, neural networks, neuroscience applications.
Reposted by Giovanni Saraceno
It was a pleasure to present my current research on cogntive modeling from ideal observer models to deep neural networks at the University of Padua @unipd.bsky.social, hosted by @giovannisaraceno.bsky.social!
Lots of great feedback and scientific exchange 🙏
www.stat.unipd.it/sites/stat.u...
Lots of great feedback and scientific exchange 🙏
www.stat.unipd.it/sites/stat.u...
April 17, 2025 at 11:53 AM
It was a pleasure to present my current research on cogntive modeling from ideal observer models to deep neural networks at the University of Padua @unipd.bsky.social, hosted by @giovannisaraceno.bsky.social!
Lots of great feedback and scientific exchange 🙏
www.stat.unipd.it/sites/stat.u...
Lots of great feedback and scientific exchange 🙏
www.stat.unipd.it/sites/stat.u...
Reposted by Giovanni Saraceno
Trick to understanding confidence intervals (ci): "Probability is on the method; confidence is in the output." The probability statement about ci's is only true prior to data collection, but that doesn't stop you from being "confident" about your interval. #edusky #statsky #academicChatter #phdchat
January 5, 2025 at 12:37 PM
Trick to understanding confidence intervals (ci): "Probability is on the method; confidence is in the output." The probability statement about ci's is only true prior to data collection, but that doesn't stop you from being "confident" about your interval. #edusky #statsky #academicChatter #phdchat
Reposted by Giovanni Saraceno
🤖Just saw this extremely evocative paper on deep NNs in the Bulletin of the AMS. The idea of *compositional sparsity* seems be very interesting for both theorists and practitioners.
Published version: www.ams.org/journals/bul...
Open-access version: cbmm.mit.edu/sites/defaul...
Published version: www.ams.org/journals/bul...
Open-access version: cbmm.mit.edu/sites/defaul...
December 16, 2024 at 5:52 PM
🤖Just saw this extremely evocative paper on deep NNs in the Bulletin of the AMS. The idea of *compositional sparsity* seems be very interesting for both theorists and practitioners.
Published version: www.ams.org/journals/bul...
Open-access version: cbmm.mit.edu/sites/defaul...
Published version: www.ams.org/journals/bul...
Open-access version: cbmm.mit.edu/sites/defaul...
Reposted by Giovanni Saraceno
Marianthi Markatou, Giovanni Saraceno
A unified framework for multivariate two-sample and k-sample kernel-based quadratic distance goodness-of-fit tests
https://arxiv.org/abs/2407.16374
A unified framework for multivariate two-sample and k-sample kernel-based quadratic distance goodness-of-fit tests
https://arxiv.org/abs/2407.16374
July 24, 2024 at 4:31 AM
Marianthi Markatou, Giovanni Saraceno
A unified framework for multivariate two-sample and k-sample kernel-based quadratic distance goodness-of-fit tests
https://arxiv.org/abs/2407.16374
A unified framework for multivariate two-sample and k-sample kernel-based quadratic distance goodness-of-fit tests
https://arxiv.org/abs/2407.16374
Reposted by Giovanni Saraceno
Claudio Agostinelli, Luca Greco, Giovanni Saraceno
Weighted likelihood methods for robust fitting of wrapped models for $p$-torus data. (arXiv:2401.04686v1 [stat.ME])
http://arxiv.org/abs/2401.04686
Weighted likelihood methods for robust fitting of wrapped models for $p$-torus data. (arXiv:2401.04686v1 [stat.ME])
http://arxiv.org/abs/2401.04686
January 10, 2024 at 4:02 AM
Claudio Agostinelli, Luca Greco, Giovanni Saraceno
Weighted likelihood methods for robust fitting of wrapped models for $p$-torus data. (arXiv:2401.04686v1 [stat.ME])
http://arxiv.org/abs/2401.04686
Weighted likelihood methods for robust fitting of wrapped models for $p$-torus data. (arXiv:2401.04686v1 [stat.ME])
http://arxiv.org/abs/2401.04686