**Max Welling**

Donald Bren School of Information and Computer Science

University of California Irvine

**"Statistical Inference using Weak Chaos and Infinite Memory"**

**Abstract: **

We describe a class of deterministic weakly chaotic dynamical systems with infinite memory. These ``herding systems'' combine learning and inference into one algorithm, where moments or data-items are converted directly into an arbitrarily long sequence of pseudo-samples. This sequence has infinite range correlations and as such is highly structured. We show that its information content, as measured by sub-extensive entropy, can grow as fast as K log(N), which is faster than the usual 1/2 K log(N) for exchangeable sequences generated by random posterior sampling from a Bayesian model. In continuous spaces we show that a kernel version of herding generates samples form a density that, when used to compute Monte Carlo sums, converges at a rate O(1/T) (as opposed to O(1/sqrt(T)) for random samples). More generally, we advocate the application of the rich theoretical framework of nonlinear dynamical systems, chaos theory and fractal geometry to statistical learning.

**Bio:**

Max Welling is a Professor of Computer Science at UC Irvine with a joint appointment in the statistics department. He is associate director of the Center for Machine Learning and Intelligent Systems, associate editor for TPAMI and JCGS journals. He received multiple grants from NSF, NIH and ONR-MURI among which an NSF career grant in 2005. He was recipient of the Dean’s midcareer award for research in 2008. He was conference chair for the Conference on AI and Statistics in 2009. Before joining UCI he held postdoctoral positions at Caltech (’98-’00), University College London (’00-’01) and the University of Toronto (’01-’03). He received his PhD in ’98 in theoretical physics.

His research focuses on large scale statistical learning. He has made contributions in approximate inference in graphical models, hierarchical models of complex cells, products of expert models, algorithms for learning image taxonomies, visual object recognition, information retrieval, text models, image denoising, and statistical shape analysis. He has over 70 publications in machine learning.

You are cordially invited to attend a reception in the lounge next to Klaus 1324 before the seminar to chat informally with faculty and students. PIZZA will be provided.

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