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 high-dimensional properties of regression trees and forests in a setting motivated by the work of Berk et al. (2013) on valid post-selection 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. Marloes Maathuis

    ETHZ
    Friday, March 4, 2016
    Time 15:15 - Room MA10

    Title: High-dimensional consistency in score-based and hybrid structure learning

    Abstract

    The main approaches for learning Bayesian networks can be classified as constraint-based, score-based or hybrid methods. Although high-dimensional consistency results are available for the constraint-based PC algorithm, such results have been lacking for score-based 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 score-based 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 high-dimensional 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. 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 non-stationary 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 Two-Step 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 two-step (plug-in) empirical likelihood ratio is not asymptotically distribution-free, that is, Wilks' phenomenon breaks down. In this paper we suggest a general approach to restore Wilks' phenomenon in two-step semiparametric empirical likelihood inferences. The main insight consists in using as the moment function in the estimating equation the influence function of the plug-in sample moment. The proposed method is general, leads to distribution-free inference and it is less sensitive to the first-step 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 Box-Ljung 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 AIR-TRAFFIC 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 (non-rigid) 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. Jonathan Rougier

    University of Bristol
    Friday, July 1st, 2016
    Time 15:15 - 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 out-performs 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 out-performs 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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