Micky Vidne: October 27th. s(MC)^2 or Hesitant Particle Filter.

In my talk I will describe a recent extension of the Sequential Monte Carlo (SMC) method. SMCs (particle filters) are a commonly used method to estimate a latent dynamical process from sequential noise-contaminated observations. SMCs are extremely powerful but suffer from sample impoverishment, a situation in which very few different particles represent the distribution of interest. I will describe our attempt to circumvent this fundamental problem by adding an extra MCMC step in the SMC algorithm. I will illustrate the usefulness of this algorithm by considering a toy neuroscience example.

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