Giulia Denevi is a Post Doc at the Computational Statistics and Machine Learning Department at the Italian Institute of Technology. She received a PhD in Machine Learning and Applied Mathematics from the University of Genoa and the Italian Institute of Technology in 2019. Her research interests are in the area of Meta-Learning, Lifelong Learning, Online Learning, Statistical Learning Theory, Optimization and Inverse Problems.
Online-Within-Online Meta-Learning. G. Denevi, D. Stamos, C. Ciliberto, M. Pontil. NeurIPS, 2019.
Learning-To-Learn Stochastic Gradient Descent with Biased Regularization. G. Denevi, C. Ciliberto, R. Grazzi, M. Pontil. ICML, 2019.
Learning-To-Learn Around A Common Mean. G. Denevi, C. Ciliberto, D. Stamos, M. Pontil. NeurIPS, 2018.
Incremental Learning-To-Learn with Statistical Guarantees. G. Denevi, C. Ciliberto, D. Stamos, M. Pontil. UAI, 2018.
Iterative Algorithms For a Non-Linear Inverse Problem in Atmospheric LiDAR. G. Denevi, S. Garbarino, A. Sorrentino. In Inverse Problems, 33, 101239, 2017.