Mean estimation: statistical and algorithmic problems
by Gábor Lugosi (UPF)
We discuss the perhaps most basic problem of statistics: how can one estimate the expected value of a random variable from a sample of independent copies? We argue that the usual empirical mean is far from being optimal and we survey various alternatives and their performance guarantees. The multivariate case presents interesting challenges, both statistical and algorithmic. The talk is based on joint work with Shahar Mendelson.
An introduction to time series mining
by José Antonio Lozano (BCAM)
In this talk we will give an overview on time series mining. Particularly we will concentrate on classical problems in data mining such as clustering, supervised classification and outlier (anomaly) detection. For these problems we will emphasise the differences between working with time series or regular vectors. In addition to that we will present new problems that appear in the area of data mining when working with time series and also point out to the complexity of working with time series streams.
|11:00|||Presentation by Carme Cascante (BGSMath Director)|
|11:15 – 12:15|||Mean estimation: statistical and algorithmic problems, Gábor Lugosi (UPF)|
|12:20 – 13:20|||An introduction to time series mining, José Antonio Lozano (BCAM)|