Giulia Clerici
Ph.D. in Machine Learning
About me
I recently received my Ph.D. in Computer Science at Università degli Studi di Milano, where I also got my Master's Degree in Computer Science and my Bachelor's Degree in Sound and Music Computing.
My research interests lie in Machine Learning and Multiarmed Bandits for nonstationary environments with a particular focus on Music Recommender Systems. The goal of my research is to propose new models and algorithms with provable guarantees and develop practical experiments to benchmark my findings with state-of-the-art algorithms.
During my Ph.D.I was supervised by Prof. Nicolò Cesa-Bianchi.
You can find my CV here.
News
Our paper has been published on TMLR as a featured paper! Linear Bandits with memory. Authors: Giulia Clerici, Pierre Laforgue, Nicolò Cesa-Bianchi.
I'm going to NeurIPS 2023 and I'm volunteering for the Women in Machine Learning (WiML) workshop. See you in New Orleans!
Our paper has been accepted at SMC 2023! Citation is not Collaboration: Music-Genre Dependence of Graph-related Metrics in a Music Credits Network. Authors: Giulia Clerici, Marco Tiraboschi. See you in Stockholm!
I'm volunteering at the STOC 2022 conference in Rome.
We presented our work on Music-Genre Dependence of Graph-Related Metrics in a Music Collaboration Network at the SpotiGen conference in Milan.
I have been selected as Breakout Program and Logistics co-chair for the Women in Machine Learning (WiML) @ ICML 2022! Find out more on the website of the event.
Our paper has been accepted at AISTATS 2022! A Last Switch Dependent Analysis of Satiation and Seasonality in Bandits. Authors: Pierre Laforgue, Giulia Clerici, Nicolò Cesa-Bianchi, Ran Gilad-Bachrach.
Find more about my role of teaching assistant for the course Statistical Methods for Machine Learning held by Prof. Nicolò Cesa-Bianchi.
