Lorenzo Rosasco

Lorenzo Rosasco
Lorenzo
Rosasco
Investigator

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Lorenzo Rosasco is  associate professor at  the University of Genova, Italy. He also holds a  research scientist at the Massachusetts Institute of Technology (MIT) and a collaborator position at the Istituto Italiano di Tecnologia (IIT). He is leading the efforts to establish the Laboratory for Computational and Statistical Learning (LCSL),  born from a collaborative agreement between IIT and MIT. He received his PhD from the University of Genova in 2006 where he worked under the supervision of Alessandro Verri and Ernesto De Vito in the SLIPGURU. He was a  visiting student with Tomaso Poggio at the Center for Biological and Computational Learning (CBCL) at MIT, and with Steve Smale at the Toyota Technological Institute at Chicago (TTI-Chicago.) Between  2006 and 2009 he was a postdoctoral fellow at CBCL working with Tomaso Poggio. His research focuses on studying theory and algorithms for machine learning. Dr. Rosasco has developed and analyzed methods to learn from small as well as large samples of high dimensional data, using analytical and probabilistic tools, within a multidisciplinary approach drawing concepts and techniques primarily from computer science but also from statistics, engineering and applied mathematics.

Projects

CBMM Publications

M. Rando, Molinari, C., Villa, S., and Rosasco, L., An Optimal Structured Zeroth-order Algorithm for Non-smooth Optimization, in 37th Conference on Neural Information Processing Systems (NeurIPS 2023), 2023.
A. Banburski, Liao, Q., Miranda, B., Rosasco, L., Hidary, J., and Poggio, T., Dynamics & Generalization in Deep Networks -Minimizing the Norm, in NAS Sackler Colloquium on Science of Deep Learning, Washington D.C., 2019.
N. Muecke, Neu, G., and Rosasco, L., Beating SGD Saturation with Tail-Averaging and Minibatching, Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, 2019.
N. Pagliana and Rosasco, L., Implicit Regularization of Accelerated Methods in Hilbert Spaces, Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, 2019.
J. Mutch, Anselmi, F., Tacchetti, A., Rosasco, L., Leibo, J. Z., and Poggio, T., Invariant Recognition Predicts Tuning of Neurons in Sensory Cortex, in Computational and Cognitive Neuroscience of Vision, Springer, 2017, pp. 85-104.
M. Nickel, Rosasco, L., and Poggio, T., Holographic Embeddings of Knowledge Graphs, in Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), Phoenix, Arizona, USA, 2016.
F. Anselmi, Rosasco, L., and Poggio, T., On invariance and selectivity in representation learning, Information and Inference: A Journal of the IMA, p. iaw009, 2016.
S. Voinea, Zhang, C., Evangelopoulos, G., Rosasco, L., and Poggio, T., Word-level Invariant Representations From Acoustic Waveforms, in INTERSPEECH 2014 - 15th Annual Conf. of the International Speech Communication Association, Singapore, 2014.
C. Zhang, Voinea, S., Evangelopoulos, G., Rosasco, L., and Poggio, T., Phone Classification by a Hierarchy of Invariant Representation Layers, in INTERSPEECH 2014 - 15th Annual Conf. of the International Speech Communication Association, Singapore, 2014.
C. Zhang, Evangelopoulos, G., Voinea, S., Rosasco, L., and Poggio, T., A Deep Representation for Invariance and Music Classification, in ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing, Florence, Italy, 2014.
S. Villa, Rosasco, L., Poggio, T., Schölkopf, B., Luo, Z., and Vovk, V., On Learnability, Complexity and Stability, in Empirical Inference, Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 59 - 69.
G. D. Canas, Poggio, T., and Rosasco, L., Learning manifolds with k-means and k-flats, in Advances in Neural Information Processing Systems 25 (NIPS 2012), 2012.