Jekyll2026-09-10T08:30:04+00:00https://mauriziofilippone.github.io/feed.xmlMaurizio FilipponeMaurizio Filippone's academic portfolio — Bayesian Inference, Gaussian Processes, Deep LearningMaurizio Filipponemaurizio.filippone@kaust.edu.saCheck out our new book on ‘Bayesian Deep Learning’2026-08-25T00:00:00+00:002026-08-25T00:00:00+00:00https://mauriziofilippone.github.io/news/new-bookOur new “Handbook of Bayesian Deep Learning” is now available online (link)]]>Maurizio Filipponemaurizio.filippone@kaust.edu.saTalk at the SMILES summer schools ‘Bayesian Deep Learning’2026-07-19T00:00:00+00:002026-07-19T00:00:00+00:00https://mauriziofilippone.github.io/news/talk-at-smilesInvited talk at the ‘SMILES summer school of machine learning’ in Nanjing University: “Bayesian Deep Learning” (link)]]>Maurizio Filipponemaurizio.filippone@kaust.edu.saTalk at ISBA ‘Model Selection for Over-Parameterized Models’2026-07-02T00:00:00+00:002026-07-02T00:00:00+00:00https://mauriziofilippone.github.io/news/talk-at-isbaInvited talk at the ‘Calibrated Bayes: Model Design and Adaptation Under Limited Resources’ session at ISBA in Nagoya: “Model Selection for Over-Parameterized Models” (link)]]>Maurizio Filipponemaurizio.filippone@kaust.edu.saPaper ‘Review of timescale distributions for electrochemical impedance spectroscopy analysis: Advantages, synergies, and future directions’ accepted in DeCarbon2026-06-18T00:00:00+00:002026-06-18T00:00:00+00:00https://mauriziofilippone.github.io/news/paper-accepted-decarbonOur paper “Review of timescale distributions for electrochemical impedance spectroscopy analysis: Advantages, synergies, and future directions” has been accepted for publication in the journal DeCarbon (link)]]>Maurizio Filipponemaurizio.filippone@kaust.edu.saCheck out our new paper ‘DIPHINE: Diffusion-based Φ-ID Neural Estimator’ (link)2026-06-17T00:00:00+00:002026-06-17T00:00:00+00:00https://mauriziofilippone.github.io/news/new-paperCheck out our new paper “DIPHINE: Diffusion-based Φ-ID Neural Estimator” (link)]]>Maurizio Filipponemaurizio.filippone@kaust.edu.saPaper ‘Position: agentic AI orchestration should be Bayes-consistent’ accepted at ICML 20262026-04-30T00:00:00+00:002026-04-30T00:00:00+00:00https://mauriziofilippone.github.io/news/paper-accepted-icmlOur position paper “Position: agentic AI orchestration should be Bayes-consistent” has been accepted for publication at ICML 2026 (link)]]>Maurizio Filipponemaurizio.filippone@kaust.edu.saKAUST Rising Stars in AI Symposium 20262026-02-09T00:00:00+00:002026-02-09T00:00:00+00:00https://mauriziofilippone.github.io/news/rising-stars-aiThe KAUST Rising Stars in AI Symposium 2026 is happening this week (link)]]>Maurizio Filipponemaurizio.filippone@kaust.edu.saTalk at the AMCS-STAT school at KAUST: ‘Bayesian Deep Learning’2026-02-03T00:00:00+00:002026-02-03T00:00:00+00:00https://mauriziofilippone.github.io/news/talk-amcs-stat-schoolTalk at the AMCS-STAT school at KAUST: “Bayesian Deep Learning”]]>Maurizio Filipponemaurizio.filippone@kaust.edu.saPaper ‘Optimizing Data Augmentation through Bayesian Model Selection’ accepted at ICLR 20262026-01-26T00:00:00+00:002026-01-26T00:00:00+00:00https://mauriziofilippone.github.io/news/paper-accepted-iclrThe paper “Optimizing Data Augmentation through Bayesian Model Selection” has been accepted at ICLR 2026! (link)]]>Maurizio Filipponemaurizio.filippone@kaust.edu.saPaper ‘TENDE: Transfer Entropy Neural Diffusion Estimation’ accepted at AISTATS 20262026-01-22T00:00:00+00:002026-01-22T00:00:00+00:00https://mauriziofilippone.github.io/news/paper-accepted-aistatsThe paper “TENDE: Transfer Entropy Neural Diffusion Estimation” has been accepted at AISTATS 2026! (link)]]>Maurizio Filipponemaurizio.filippone@kaust.edu.sa