Description 
Abstract: Since the impressive advances in the area of compressed sensing, dimensionality reduction by random projections for machine learning and data mining gains a renewed interest. In direct analogy, compressive learning means to carry out learning tasks efficiently on cheaply compressed versions of high dimensional massive data sets that have a sparse representation. This talk will discuss conditions and guarantees for compressive learning to succeed, which do not require the data to have a sparse representation but instead exploit the natural structure of the learning problem. In particular, we give tight risk bounds in classification and regression settings, which have a clear interpretation and reveal meaningful structural properties of the problem that make it solvable effectively in a small dimensional random subspace. We will also demonstrate that performance gains are achievable by combining several compressive learners into an ensemble.
Speaker Bio: Dr. Ata Kaban is currently senior lecturer in Computer Science at the University of Birmingham. She recieved her PhD in Computer Science from the University of Paisley, supervised by Mark Girolami. She also holds a PhD in Musicology. Her research interests are: statistical machine learning, data mining  with emphasis on high dimensional data spaces; algorithmic learning theory; probabilistic modelling of data, and Bayesian inference; high dimensional phenomena, measure concentration, random matrix theory; dimensionality reduction, random projections; largescale heuristic blackbox optimisation.
Speaker's Webpage: http://www.cs.bham.ac.uk/~axk/
Slides for the talk: PDF
Video of the talk here.
