Jonathan Huggins: May 1st

Jonathan Huggins will present his joint work with Frank Wood. Here is an abstract:

We develop a class of non-parametric Bayesian models we call infinite structured explicit duration hidden Markov models (ISEDHMMs). ISEDHMMs are HMMs that possess an unbounded number of states, encode state dwell-time distributions explicitly, and have constraints on what state transitions are allowed. The ISEDHMM framework generalizes explicit duration finite HMMs, infinite HMMs, left-to-right HMMs, and more (all are recoverable by specific choices of ISEDHMM parameters).  This suggests that ISEDHMMs should be applicable to data-analysis problems in a variety of settings.

Some interesting papers from AISTATS 2010

Here are a few potentially interesting papers from AISTATS this year. All pdf's available from

http://jmlr.csail.mit.edu/proceedings/papers/v9/ - in no particular order:

Improving posterior marginal approximations in latent Gaussian models

by Botond Cseke, Tom Heskes

Dirichlet Process Mixtures of Generalized Linear Models

by Lauren Hannah, David Blei, Warren Powell

Simple Exponential Family PCA

by Jun Li, Dacheng Tao

Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials

by Mark Schmidt, Kevin Murphy

Reduced-Rank Hidden Markov Models

by Sajid Siddiqi, Byron Boots, Geoffrey Gordon

Detecting Weak but Hierarchically-Structured Patterns in Networks

by Aarti Singh, Robert Nowak, Robert Calderbank

Inference of Sparse Networks with Unobserved Variables. Application to Gene Regulatory Networks

by Nikolai Slavov

On the relation between universality, characteristic kernels and RKHS embedding of measures

by Bharath Sriperumbudur, Kenji Fukumizu, Gert Lanckriet

State-Space Inference and Learning with Gaussian Processes

by Ryan Turner, Marc Deisenroth, Carl Rasmussen

Parallelizable Sampling of Markov Random Fields

by James Martens, Ilya Sutskever

Approximation of hidden Markov models by mixtures of experts with application to particle filtering

by Jimmy Olsson, Jonas Strojby

Negative Results for Active Learning with Convex Losses

by Steve Hanneke, Liu Yang