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SEMINARIOS ONLINE

Seminario Online Antonio Manuel Durán

Antonio Manuel Durán.PREMIO DE INVESTIGACIÓN SOCIEDAD CIENTÍFICA INFORMÁTICA DE ESPAÑA-FUNDACIÓN BBVAInvestigadores Jóvenes Informáticos

Time series data mining and its applications in real-world problems

FECHA: 04 DE OCTUBRE DE 2021

HORA:  12:00H

                      El registro se cerrará 20 minutos antes del inicio del seminario

Abstract:

Currently, information systems such as sensors produce a large amount of data, which is expected to experience exponential growth in the coming years. These data are often treated as time series, which are chronologically collected data representing a time-varying function. Time series appear in a wide range of scientific fields, such as hydrology, paleoclimatology or air traffic, among others. This seminar exposes the preprocessing, segmentation and prediction of time series, which are considered one of the main tasks of time series data mining. Machine learning (ML) techniques and bio-inspired algorithms are detailed throughout a set of real-world applications.

Seminario Online Antonio Linero

Linero, Antonio

Bayesian Decision Tree Ensembling Strategies for Nonparametric Problems

FECHA: 17 DE SEPTIEMBRE DE 2021

HORA:  17:00H

Abstract:

In this talk we will make the case for using Bayesian decision tree ensembles, such as Bayesian additive regression trees (BART), for addressing some fully-nonparametric problems. We present models for density regression and survival analysis, and argue that our approaches are both easier to use and more effective than more standard Bayesian nonparametric solutions (such as those based on mixture models). On the applied side, we show how to use our models to extract interesting features across several datasets. On the theoretical side, we also show that our models attain minimax-optimal rates of convergence of the posterior in high-dimensional settings. Throughout the talk we will emphasize the flexibility and ease-of-use of our approach: we obtain excellent results on all simulated and real data analyses using heuristically chosen «default» priors, and our software tools make it quite straight-forward for researchers (both in-principle and in-practice) to embed our ensembles in larger models

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