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EPFL Statistics Seminar

Mr. Stefan Wager
Stanford University
Friday, January 29, 2016
Time 14:00  Room CM5
Title: Statistical Estimation with Random Forests
Abstract
Random forests, introduced by Breiman (2001), are among the most widely used machine learning algorithms today, with applications in fields as varied as ecology, genetics, and remote sensing. Random forests have been found empirically to fit complex interactions in high dimensions, all while remaining strikingly resilient to overfitting. In principle, these qualities also ought to make random forests good statistical estimators. However, our current understanding of the statistics of random forest predictions is not good enough to make random forests usable as a part of a standard applied statistics pipeline: in particular, we lack robust consistency guarantees and asymptotic inferential tools. In this talk, I will present some recent results that seek to overcome these limitations. The first half of the talk develops a Gaussian theory for random forests in low dimensions that allows for valid asymptotic inference, and applies the resulting methodology to the problem of heterogeneous treatment effect estimation. The second half of the talk then considers highdimensional properties of regression trees and forests in a setting motivated by the work of Berk et al. (2013) on valid postselection inference: at a high level, we find that the amount by which a random forest can overfit to training data scales only logarithmically in the ambient dimension of the problem. This talk is based on joint work with Susan Athey, Bradley Efron, Trevor Hastie, and Guenther Walther.

Prof. Tatyana Krivobokova
University of Göttingen
Friday, March 11, 2016
Time 15:15  Room MA10
Title: Partial least squares for dependent data
Abstract
The partial least squares algorithm for dependent data realisations is considered. Consequences of ignoring the dependence in the data for the performance of the algorithm are studied theoretically and numerically. It is shown that ignoring nonstationary dependence structures can lead to inconsistent estimation. A simple modification of the algorithm for dependent data is proposed and consistency of the corresponding estimators is shown. A protein dynamics example illustrates the superior predictive power of the method. This is the joint work with Marco Singer, Axel Munk and Bert de Groot.

Prof. Ingrid van Keilegom
Université Catholique de Louvain
Friday, March 18, 2016
Time 15:15  Room MA10
Title: Wilks' Phenomenon in TwoStep Semiparametric Empirical Likelihood Inference
Abstract
In both parametric and certain nonparametric statistical models, the empirical likelihood ratio satisfies a nonparametric version of Wilks' theorem. For many semiparametric models, however, the commonly used twostep (plugin) empirical likelihood ratio is not asymptotically distributionfree, that is, Wilks' phenomenon breaks down. In this paper we suggest a general approach to restore Wilks' phenomenon in twostep semiparametric empirical likelihood inferences. The main insight consists in using as the moment function in the estimating equation the influence function of the plugin sample moment. The proposed method is general, leads to distributionfree inference and it is less sensitive to the firststep estimator than alternative bootstrap methods. Several examples and a simulation study illustrate the generality of the procedure and its good finite sample performance. (jont work with Francesco Bravo and Juan Carlos Escanciano)

Prof. Konstantinos Fokianos
University of Cyprus
Friday, May 13, 2016
Time 15:15  Room MA10
Title: Consistent testing for pairwise dependence in time series
Abstract
We consider the problem of testing pairwise dependence for stationary time series. For this, we suggest the use of a BoxLjung type test statistic which is formed after calculating the distance covariance function among pairs of observations. The distance covariance function is a suitable measure for detecting dependencies between observations as it is based on the distance between the characteristic function of the joint distribution of the random variables and the product of the marginals. We show that, under the null hypothesis of independence and under mild regularity conditions, the test statistic converges to a normal random variable. The results are complemented by several examples.
This is a joint work with M. Pitsillou.

 THIS SEMINAR IS CANCELLED DUE TO STRIKE IN AIRTRAFFIC CONTROLLERS 
Prof. Charles Taylor
University of Leeds
Friday, May 27, 2016
Time 15:15  Room MA10
Title: Nonparametric transformations for directional and shape data
Abstract
For i.i.d. data (x_i, y_i), in which both x and y lie on a sphere, we consider flexible (nonrigid) regression models, in which solutions can be obtained for each location of the manifold, with (local) weights which are are function of distance. By considering terms in a series expansion, a ``local linear'' model is proposed for rotations, and we explore an iterative procedure with connections to boosting. Further extensions to general shape matching are discussed.

Prof. Geoffrey G. Decrouez
National Research University, Higher School of Economics, Moscow
Monday, July 18, 2016
Time 15:15  Room MA10
Title: Finite sample properties of the mean occupancy counts and probabilities
Abstract
For a probability distribution P on an at most countable alphabet, we give finite sample bounds for the expected occupancy counts and probabilities. In particular, both upper and lower bounds are given in terms of the right tail of the counting measure of P. Special attention is given to the case where the right tail is bounded by a regularly varying function. In this case, it is shown that our general results lead to an optimalrate control of the expected occupancy counts and probabilities with explicit constants. Our results are also put in perspective with Turing's formula and recent concentration bounds to deduce confidence regions.
Prof. Marloes Maathuis
ETHZFriday, March 4, 2016
Time 15:15  Room MA10
Title: Highdimensional consistency in scorebased and hybrid structure learning
Abstract
The main approaches for learning Bayesian networks can be classified as constraintbased, scorebased or hybrid methods. Although highdimensional consistency results are available for the constraintbased PC algorithm, such results have been lacking for scorebased and hybrid methods, and most hybrid methods are not even proved to be consistent in the classical setting where the number of variables remains fixed. We study the scorebased Greedy Equivalence Search (GES) algorithm, as well as hybrid algorithms that are based on GES. We show that such hybrid algorithms can be made consistent in the classical setting by using an adaptive restriction on the search space. Moreover, we prove consistency of GES and adaptively restricted GES (ARGES) for certain sparse highdimensional scenarios. ARGES scales well to large graphs with thousands of variables, and our simulation studies indicate that both ARGES and GES generally outperform the PC algorithm.
Joint work with Preetam Nandy and Alain Hauser
Prof. Philip B. Stark
University of California, BerkeleyFriday, June 24, 2016
Time 15:15  Room MA12
Title: Simple Random Sampling: Not So Simple
Abstract
A simple random sample (SRS) of size $k$ from a population of size $n$ is a sample drawn at random in such a way that every subset of $k$ of the $n$ items is equally likely to be selected. The theory of inference from SRSs is fundamental in statistics; many statistical techniques and formulae assume that the data are an SRS. True SRSs are rare; in practice, people tend to draw samples by using pseudorandom number generators (PRNGs) and algorithms that map a set of pseudorandom numbers into a subset of the population. Most statisticians take for granted that the software they use "does the right thing," producing samples that can be treated as if they are SRSs. In fact, the PRNG algorithm and the algorithm for drawing samples using the PRNG matter enormously. Some widely used methods are particularly bad. They cannot generate all subsets of size $k$; the subsets they do generate may not have equal frequencies; and they are numerically inefficient. Using such methods introduces bias and makes standard uncertainty calculations meaningless.
Joint work with Kellie Ottoboni, Department of Statistics, University of California, Berkeley
Prof. Jonathan Rougier
University of BristolFriday, July 1st, 2016
Time 16:15 please note unusual time!  Room MA12
Title: Ensemble averaging and mean squared error
Abstract
In fields such as climate science, it is common to compile an ensemble of different simulators for the same underlying process. It is an interesting observation that the ensemble mean often outperforms at least half of the ensemble members in mean squared error (measured with respect to observations). This despite the fact that the ensemble mean is typically 'less physical' than the individual ensemble members (the state space not being convex). In fact, as demonstrated in the most recent IPCC report, the ensemble mean often outperforms all or almost all of the ensemble members. It turns out that that this is likely to be a mathematical result based on convexity and asymptotic averaging, rather than a deep insight about climate simulators. I will outline the result and discuss its implications.
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