Max Welling from UCI will give a talk on May 11 at 12:30 (lunch served at 12:00) in CS 302. He will also host a discussion group at 4:30pm. See end of this email for instructions on how to schedule a meeting with Max. Title: On Herding Dynamical Weights and Fractal Attractors Abstract: Learning the parameters of a Markov Random Field is intractable. To circumvent part of this intractability, I propose to give up on the idea of trying to obtain point estimates. Inspired by the concept of "dynamical synapses", a dynamical system is introduced that generates sequences of pseudo-samples that are guaranteed to satisfy the moment constraints of the associated maximum likelihood problem. This dynamical system is deterministic, yet non-periodic with Lyaponov exponents all equal to zero, and its attractor set has fractal properties. I will discuss how to leverage these ideas for classification and estimation and show experimental results for fully observed and restricted Boltzman machines. Title: On the Role of Smoothing in Topic Models (with Arthur Acuncion and Padhriac Smyth) Discussion Group @ 4:30PM, Location TBD Abstract: Latent Dirichlet analysis, or topic modeling, is a flexible latent variable framework for modeling high-dimensional, sparse count data. Various learning algorithms have been developed in recent years, including collapsed Gibbs sampling, variational inference, and maximum a posteriori estimation, and this variety motivates the need for careful empirical comparisons between these approaches. We first highlight the close connections between these approaches. We find that the main differences are attributable to the amount of smoothing applied to the counts. When the hyperparameters are optimized, the differences in performance among the algorithms diminish significantly. The ability of these algorithms to achieve similarly accurate solutions gives us the freedom to select computationally efficient approaches. On text corpora with thousands of documents, accurate topic models can be learned in several seconds, using the insights gained from this comparative study. If you'd like to meet Max on Monday, please: 1. Visit http://wass.princeton.edu. 2. Sign in using your OIT login. 3. Click on "Make an appointment." 4. Enter "mlvisit" where it says "Calendar owner's netid." 5. Reserve a spot on May 11 under "Meeting with Max Welling."