Extremely happy and excited to share that our paper “Graph Mixture Density Networks” has been accepted for publication at ICML 2021! Huge congrats to Federico Errica for his second ICML paper and to Alessio Micheli who shares with me Federico’s …
Congratulations to Francesco Crecchi that just defended his Ph.D. thesis on “Deep Learning Safety under Non-Stationarity Assumptions”, jointly supervised by me and Battista Biggio. Francesco is my first Ph.D. student to graduate, so that doubles the celebrations on my side. …
Our group had 4 papers recently accepted to the upcoming International Joint Conference on Neural Networks! Much work on deep learning for graphs, including a novel edge-based model, an efficient graph generation approach and an explanation method for the chemical …
The new edition of the Reinforcement Learning course will kickoff on Monday 29/03/2021 h. 16.00. This is a course offered to M.Sc. Students of the AI Curriculum (recognition as 3 Free-choice CFU) and Ph.D. students. For furher information please check …
Another big graduation day today, with plenty of contributions from our group! Congratulations to all students for their achievement. Check them out below. Pleanty of cool stuff including distillation-based continual learning, medical image analysis and generation, reinforcement learning and quantum …
Excellent result by our group in the upcoming NeurIPS 2020 workshops with four accepted papers.Congrats to Antonio Carta, Francesco Landolfi, Danilo Numeroso and Matteo Ronchetti! Preprints coming up..
Big graduation day in the ML family with 3 newly graduated students (two cum laude!) on topics ranging from neuro-probabilistic models for graphs, interpretability of graph neural networks for chemistry and cognitive architectures for creativity. Congratulations! Daniele Arioli, CognAC: a …
Congratulations to Daniele Castellana for having his paper accepted at COLING 2020. Check it out if you are interested in higher-order neural networks for parse trees using tensor decompositions (soon on the Arxiv!).
Review effort does not always come unnoticed.. Happy to be listed in the top third of the ICML reviewers.
Couldn’t think of a better venue for my 99th research paper than the Journal of Machine Learning Research. Check out our work on deep and probabilistic learning for graphs. Terrific job by Federico Errica!