Agenda

Speaker: Lorenzo Schiavon, DEC - Univerdità Ca' Foscari Venezia

Link Zoom: https://unive.zoom.us/j/85153268624?pwd=MzBhdlA2M1B2dThJQ2Y5T0EwUE5PZz09

Abstract:
A challenging task in learning low-dimensional representations of high-dimensional objects is the lack of knowledge about the dimension of the latent space and the relative impact of its components. To address this, overfitted factor models with increasing shrinkage priors have been proposed, enabling the adaptive removal of unnecessary components by truncating latent matrices. In this talk, we discuss the limitations of current specifications and gradually compose a framework incorporating novel methods to address these deficiencies. We introduce a general class of infinite factor models with sparse patterns, which can be structured based on exogenous information, thus filling gaps in existing infinite factor models by accommodating grouped variables and non-exchangeable structures. This framework is further extended to general matrix decomposition models, presenting a more scalable computational strategy inspired by gradient boosting. Practical benefits of sparse modeling in infinite factor models are demonstrated through diverse applications to football tracking data.

Bio Sketch:
Lorenzo Schiavon is junior associate professor (RTDA) at the Department of Economics of Ca' Foscari University of Venice. Previously he was a research associate at the University of Padova where is also obtained a PhD in Statistics.