[Forum SIS] Seminari SOYER e POLSON al CNR IMATI - Milano - 3 Maggio

[fabrizio a mi.imati.cnr.it]

Segnalo i seguenti seminari che si svolgeranno presso

CNR IMATI, via Alfonso Corti 12, Milano, Aula Expo

Giovedì 3 maggio 2018, ore 15:00 - 16:30

1) Refik Soyer, School of Business, George Washington University 
ore 15:00-15:45

BAYESIAN MODELING OF NON GAUSSIAN MULTIVARIATE TIME SERIES

Abstract: Modeling of multivariate non Gaussian time series of correlated
observations is considered. In so doing, we focus on time series from
multivariate counts and durations.

Dependence among series arises as a result of sharing a common dynamic
environment. We discuss characteristics of the resulting multivariate time
series models and develop Bayesian inference for them using particle
filtering and Markov chain Monte Carlo methods.

We illustrate application of the proposed approach using conditionally
multivariate Poisson and gamma time series.

Joint work with Tevfik Aktekin, University of New Hampshire and Nicholas 
Polson, University of Chicago

2) Nicholas Polson, Booth School of Business, University of Chicago 
ore 15:45-16:30

DEEP LEARNING: A BAYESIAN PERSPECTIVE

Deep learning is a form of machine learning for nonlinear high dimensional
pattern matching and prediction. By taking a Bayesian probabilistic 
perspective, we provide a number of insights into more efficient algorithms
for optimisation and hyper-parameter tuning. Traditional high-dimensional
data reduction techniques, such as principal component analysis (PCA), partial 
least squares (PLS), reduced rank regression (RRR), projection pursuit
regression (PPR) are all shown to be shallow learners. Their deep learning
counterparts exploit multiple deep layers of data reduction which provide
predictive performance gains. Stochastic gradient descent (SGD) training 
optimisation and Dropout (DO) regularization provide estimation and variable
selection. Bayesian regularization is central to finding weights and 
connections in networks to optimize the predictive bias-variance trade-off. 
To illustrate our methodology, we provide an analysis of international 
bookings on Airbnb. Finally, we conclude with directions for future research.

Joint work with Vladimir Sokolov, Sistems Engineering and Operations 
Research, George Mason University
-- 
Fabrizio Ruggeri                    fabrizio AT mi.imati.cnr.it
CNR IMATI                           tel +39 0223699532
Via Bassini 15                      fax +39 0223699538
I-20133 Milano (Italy)              web.mi.imati.cnr.it/fabrizio