Classification and Network modelling
In collaboration with the European Association for Data Science (EuADS). Session organized by B. Lausen.
|B. Lausen. Ensemble classification.|
|I. Gollini. Latent variable modelling of interdependent ego-networks.|
|A. Caimo. Improving the efficiency of Bayesian computation for network models.|
Statistical Learning in Data Science
In collaboration with the Portuguese Statistical Society (SPE). Session organized by P. Brito.
|M. Cardoso. On clustering validation: the internal perspective.|
|P. Rodrigues. Controversies in Health Data Science.|
|L. Torgo. Data Pre-processing Methods for Forecasting with Spatio-Temporal Data.|
Visualization and analysis of modern data
In collaboration with the Bernoulli Society (BS). Session organized by Po-Ling Loh.
|J. Long. Mapping the Milky Way Halo: Modeling and Classification of Sparsely Sampled Vector Valued Functions.|
|Y. Benjamini. Summarizing linearized prediction models along feature groups.|
|T. McCormick. Using Aggregated Relational Data to feasibly identify network structure without network data.|
In collaboration with the International Society for Business and Industrial Statistics (ISBIS). Session organized by D. Banks.
|D. Banks. Statistical Issues with Agent-Based Models.|
|T. Oliveira. Balanced Incomplete Block Designs: Some applications and visualization.|
|P. Rodrigues. Randomized singular spectrum analysis for long time series.|