Research Seminar

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Spring Semester 2016

Note: The highlighted event marks the next occurring event.

Date / Time Speaker Title Location
22 February 2016
Nadler Boaz
Weizmann Institute of Science
Unsupervised Ensemble Learning   HG  G 19.1 
Abstract: Abstract: In various applications, one is given the advice or predictions of several classifiers of unknown reliability, over multiple questions or queries. This scenario is different from the standard supervised setting where classifier accuracy can be assessed from available labeled training or validation data, and raises several questions: given only the predictions of several classifiers of unknown accuracies, over a large set of unlabeled test data, is it possible to a) reliably rank them, and b) construct a meta-classifier more accurate than any individual classifier in the ensemble? In this talk we'll show that under various independence assumptions between classifier errors, this high dimensional data hides simple low dimensional structures. In particular, we'll present simple spectral methods to address the above questions, and derive new unsupervised spectral meta-learners. We'll prove these methods are asymptotically consistent when the model assumptions hold, and also present their empirical success on a variety of unsupervised learning problems.
4 March 2016
Ryan Tibshirani
Carnegie Mellon University, USA
Trend Filtering: Some Recent Advances and Challenges  HG G 19.1 
Abstract: I will discuss trend filtering, a newly proposed tool of Steidl et al. (2006), Kim et al. (2009) for nonparametric regression. The trend filtering estimate is defined as the minimizer of a penalized least squares criterion, in which the penalty term sums the absolute kth order discrete derivatives over the input points. I will give an overview of some interesting connections between these estimates and adaptive spline estimation, in particular, a connection to locally adaptive regression splines of Mammen and van de Geer (1997). If time permits, I will discuss some extensions of trend filtering, namely, to high-dimensional data and (separately) to graph-based data. I will also discuss some of the challenges I see in each of these settings. This represents joint work with Veeranjaneyulu Sadhanala, Yu-Xiang Wang, James Sharpnack, and Alex Smola.
18 March 2016
Jonathan Rosenblatt

Title T.B.A. HG G 19.1 
8 April 2016
Rainer von Sachs
Université catholique de Louvain
Functional mixed effect models for spectra of subject-replicated time series  HG G 19.1 
Abstract: Abstract: In this work in progress we treat a functional mixed effects model in the setting of spectral analysis of subject-replicated time series data. We assume that the time series subjects share a common population spectral curve (functional fixed effect), additional to some random subject-specific deviation around this curve (functional random effects), which models the variability within the population. In contrast to existing work we allow this variability to be non-diagonal, i.e. there may exist explicit corre- lation between the different subjects in the population. To estimate the common population curve we project the subject-curves onto an appropriate orthonormal basis (such as a wavelet basis) and continue working in the coefficient domain instead of the functional domain. In a sampled data model, with discretely observed noisy subject-curves, the model in the co- efficient domain reduces to a finite-dimensional linear mixed model. This allows us, for estimation and prediction of the fixed and random effect coefficients, to apply both traditional linear mixed model meth- ods and, if necessary by the spatially variable nature of the spectral curves, work with some appropriate non-linear thresholding approach. We derive some theoretical properties of our methodology highlighting the influence of the correlation in the subject population. To illustrate the proposed functional mixed model, we show some examples using simulated time series data, and an analysis of empirical subject-replicated EEG data. We conclude with some possible extensions, among which we allow situations where the data show po- tential breakpoints in its second order (spectral) structure over time. The presented work is joint with Joris Chau (ISBA, UCL).
29 April 2016
Christian Brownless
Universität Pompeu Fabra
Title T.B.A. HG G 19.1 
20 May 2016
Asger Lunde
Aarhus University
Title T.B.A. HG G 19.1 

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