Giovanni Motta: Oct. 24th

Fitting evolutionary factor models to multivariate EEG data

Current approaches for fitting stationary (dynamic) factor models to multivariate time series are based on principal components analysis of the covariance (spectral) matrix. These approaches are based on the assumption that the underlying process is temporally stationary which appears to be restrictive because, over long time periods, the parameters are highly unlikely to remain constant. Our alternative approach is to model the time-varying covariances (auto-covariances) via nonparametric estimation, which imposes very little structure on the moments of the underlying process. Because of identification issues, only parts of the model parameters are allowed to be time-varying. More precisely, we consider two specifications: First, the latent factors are stationary while the loadings are time-varying. Second, the latent factors admit a dynamic representation with time-varying autoregressive coefficients while the loadings are constant over time. Estimation of the model parameters is accomplished by application of evolutionary principal components and local polynomials. We illustrate our approach through applications to multichannel EEG data.

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.

David Pfau: Dec. 20th

David will be giving a fly-by view of a number of cool papers from NIPS.

First is Empirical Models of Spiking in Neural Populations by Macke, Büsing, Cunningham, Yu, Shenoy and Mahani, where they evaluate the relative merits of GLMs with pairwise coupling and state space models on multielectrode recording in motor cortex.

Next, Quasi-Newton Methods for Markov Chain Monte Carlo by Zhang and Sutton looks at how to use approximate second-order methods like L-BFGS for MCMC while still preserving detailed balance.

Then, Demixed Principal Component Analysis is an extension of PCA which demixes the dependence of different latent dimensions on different observed parameters, and is used to analyze neural data from PFC

Finally, Learning to Learn with Compound Hierarchical-Deep Models, which combines a deep neural network for learning visual features with a hierarchical nonparametric Bayesian model for learning object categories to make one cool-looking demo.

Eftychios Pnevmatikakis: Sept. 8

I will present the recent paper on computationally efficient methods for nonparametric modeling of neural spiking activity with point processes by Coleman and Sarma. A copy can be found here.