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Autumn Semester 2018

Date / Time Speaker Title Location
27 August 2018
09:00-18:00

Event Details

Research Seminar in Statistics

Title Swiss Statistics Meeting 2018
Speaker, Affiliation ,
Date, Time 27 August 2018, 09:00-18:00
Location Uni KOH
Swiss Statistics Meeting 2018
Uni KOH
28 August 2018
09:00-18:00

Event Details

Research Seminar in Statistics

Title Swiss Statistics Meeting 2018
Speaker, Affiliation ,
Date, Time 28 August 2018, 09:00-18:00
Location Uni KOH
Swiss Statistics Meeting 2018
Uni KOH
29 August 2018
09:00-18:00

Event Details

Research Seminar in Statistics

Title Swiss Statistics Meeting 2018
Speaker, Affiliation ,
Date, Time 29 August 2018, 09:00-18:00
Location Uni KOH
Swiss Statistics Meeting 2018
Uni KOH
4 September 2018
15:00-15:45 		Ludwig Schmidt
University of California, Berkeley
Event Details

Research Seminar in Statistics

Title How robust is current machine learning? A perspective on overfitting and (adversarial) distribution shifts
Speaker, Affiliation Ludwig Schmidt, University of California, Berkeley
Date, Time 4 September 2018, 15:00-15:45
Location HG G 19.1
Abstract Machine learning is now being deployed in safety- and security-critical systems such as autonomous vehicles, medical devices, and large recommender systems. If we want to use machine learning in these scenarios responsibly, we need to understand how reliable our current methodology is. One potential danger in the common ML workflow is the repeated use of the same test set for parameter tuning. To investigate this issue, I will present results of a reprodu-cibility study on the popular CIFAR-10 dataset. Surprisingly, we find no signs of overfitting despite multiple years of adaptive classifier tuning. Nevertheless, our results show that current classifiers are already susceptible to benign shifts in distribution. In the second part of the talk, I will then describe how robust optimization can address some of the challenges arising from distribution shifts in the form of adversarial examples. By exploring the loss landscape of min-max problems in deep neural networks, we can train classifiers with state-of-the art robustness to l_infinity perturbations and small spatial transformations. Based on joint works with Logan Engstrom, Aleksander Madry, Aleksandar Makelov, Benjamin Recht, Rebecca Roelofs, Vaishaal Shankar, Brandon Tran, Dimitris Tsipras, and Adrian Vladu.
How robust is current machine learning? A perspective on overfitting and (adversarial) distribution shiftsread_more
HG G 19.1
6 September 2018
16:15-17:00 		Francois Bachoc
University Paul Sabatier, Toulouse
Event Details

Research Seminar in Statistics

Title Consistency of stepwise uncertainty reduction strategies for Gaussian processes
Speaker, Affiliation Francois Bachoc, University Paul Sabatier, Toulouse
Date, Time 6 September 2018, 16:15-17:00
Location HG G 19.1
Abstract In the first part of the talk, we will introduce spatial Gaussian processes. Spatial Gaussian processes are widely studied from a statistical point of view, and have found applications in many fields, including geostatistics, climate science and computer experiments. Exact inference can be conducted for Gaussian processes, thanks to the Gaussian conditioning theorem. Furthermore, covariance parameters can be estimated, for instance by Maximum Likelihood. In the second part of the talk, we will introduce a class of iterative sampling strategies for Gaussian processes, called 'stepwise uncertainty reduction' (SUR). We will give examples of SUR strategies which are widely applied to computer experiments, for instance for optimization or detection of failure domains. We will provide a general consistency result for SUR strategies, together with applications to the most standard examples.
Consistency of stepwise uncertainty reduction strategies for Gaussian processesread_more
HG G 19.1
14 September 2018
15:15-16:00 		Vanessa Didelez
Leibniz-Institut für Präventionsforschung und Epidemiologie - BIPS
Event Details

Research Seminar in Statistics

Title Assumptions for Mendelian Randomisation Studies with Multiple Instruments
Speaker, Affiliation Vanessa Didelez, Leibniz-Institut für Präventionsforschung und Epidemiologie - BIPS
Date, Time 14 September 2018, 15:15-16:00
Location HG G 19.1
Abstract Mendelian randomisation (MR) refers to situations where a genetic predisposition can be exploited as an instrumental variable (IV) to estimate the causal effect of a modifiable risk factor or exposure on an outcome of interest. For example, the ALDH2 gene is associated with alcohol consumption, and has therefore successfully been used as an IV to estimate the causal effect of alcohol on outcomes related to coronary heart disease. MR analyses have become very popular especially recently with the increased availa-bility of GWAS data. This gives rise to a number of challenges, especially around the theme of multiple IVs as it is common that several SNPs are found to be associated with an exposure of interest. However, the validity of such multiple IVs can often not been established in a convincing way and numerous methods that claim to allow for multiple but partially invalid IVs have been put forward in the last few years. In this talk I will propose and investigate a formal notion of „valid IV“ in the context of multiple and potentially invalid IVs - this has been neglected by all of the previous literature but turns out to be crucial to assess the plausbility of the various available methods.
Assumptions for Mendelian Randomisation Studies with Multiple Instrumentsread_more
HG G 19.1
14 September 2018
16:30-17:15 		Armeen Taeb
Electrical Engineering California Institute of Technology
Event Details

Research Seminar in Statistics

Title False Discovery and Its Control For Low Rank Matrices
Speaker, Affiliation Armeen Taeb, Electrical Engineering California Institute of Technology
Date, Time 14 September 2018, 16:30-17:15
Location HG G 19.1
Abstract Models specified by low-rank matrices are ubiquitous in contemporary applications. In many of these problem domains, the row/column space structure of a low-rank matrix carries information about some underlying phenomenon, and it is of interest in inferential settings to evaluate the extent to which the row/column spaces of an estimated low-rank matrix signify discoveries about the phenomenon. However, in contrast to variable selection, we lack a formal framework to assess true/false discoveries in low-rank estimation; in particular, the key source of difficulty is that the standard notion of a discovery is a discrete one that is ill-suited to the smooth structure underlying low-rank matrices. We address this challenge via a \emph{geometric} reformulation of the concept of a discovery, which then enables a natural definition in the low-rank case. We describe and analyze a generalization of the Stability Selection method of Meinshausen and B\"uhlmann to control for false discoveries in low-rank estimation, and we demonstrate its utility compared to previous approaches via numerical experiments.
False Discovery and Its Control For Low Rank Matricesread_more
HG G 19.1
28 September 2018
15:15-16:00 		Brendan McCabe
University of Liverpool
Event Details

Research Seminar in Statistics

Title Approximate Bayesian Forecasting
Speaker, Affiliation Brendan McCabe, University of Liverpool
Date, Time 28 September 2018, 15:15-16:00
Location HG G 19.1
Abstract Approximate Bayesian Computation (ABC) has become increasingly prominent as a method for conducting parameter inference in a range of challenging statistical problems, most notably those characterized by an intractable likelihood function. In this paper, we focus on the use of ABC not as a tool for parametric inference, but as a means of generating probabilistic forecasts; or for conducting what we refer to as ‘approximate Bayesian forecasting’. The four key issues explored are:
i) the link between the theoretical behavior of the ABC posterior and that of the ABC-based predictive;
ii) the use of proper scoring rules to measure the (potential) loss of forecast accuracy when using an approximate rather than an exact predictive;
iii) the performance ofapproximate Bayesian forecasting in state space models; and
iv) the use of forecasting criteria to inform the selection of ABC summaries in empirical settings. The primary finding of the paper is that ABC can provide a computationally efficient means of generating probabilistic forecasts that are nearly identical to those produced by the exact predictive, and in a fraction of the time required to produce predictions via an exact methods.
Approximate Bayesian Forecastingread_more
HG G 19.1
2 November 2018
15:15-16:00 		Haakon Bakka
King Abdullah University of Science and Technology
Event Details

Research Seminar in Statistics

Title Non-stationary Gaussian models with physical barriers
Speaker, Affiliation Haakon Bakka, King Abdullah University of Science and Technology
Date, Time 2 November 2018, 15:15-16:00
Location HG E 33.5
Abstract When modeling spatial data near the coast, we need to consider which assumptions to make on the Gaussian field with respect to the coastline, i.e. what kind of boundary effect to assume. One possibility is to have no boundary effect, modeling both water and land, but with observation and prediction locations only in water, leading to a model with a stationary correlation structure. However, a stationary field smooths over islands and peninsulas, inappropriately assuming that observations on two sides of land are highly correlated. Other approaches in the literature range from simple use of Dirichlet or Neumann boundary conditions, to being quite complex and costly. In this talk I showcase a new approach, the Barrier model, implemented in R-INLA, that is intuitive in the way correlation follows the coastline, and is as easy to set up and do inference with as a stationary field, with computational complexity O(n sqrt(n)). I compare this model to two others, showing significant improvement at reconstructing a test function. A real data application shows that the Barrier model smooths around peninsulas, and that inference is numerically stable. I also detail how the stochastic partial differential equations (SPDE) approach was used to construct the Barrier model.
Non-stationary Gaussian models with physical barriersread_more
HG E 33.5
16 November 2018
15:15-16:00 		Zijian Guo
Rutgers, The State University of New Jersey
Event Details

Research Seminar in Statistics

Title Semi-supervised Inference for Explained Variance in High-dimensional Linear Regression and Its Applications
Speaker, Affiliation Zijian Guo, Rutgers, The State University of New Jersey
Date, Time 16 November 2018, 15:15-16:00
Location HG G 19.1
Abstract We consider statistical inference for the explained variance $\beta^{\intercal}\Sigma \beta$ under the high-dimensional linear model $Y=X\beta+\epsilon$ in the semi-supervised setting, where $\beta$ is the regression vector and $\Sigma$ is the design covariance matrix. A calibrated estimator, which efficiently integrates both labelled and unlabelled data, is proposed. It is shown that the estimator achieves the minimax optimal rate of convergence in the general semi-supervised framework. The optimality result characterizes how the unlabelled data affects the minimax optimal rate. Moreover, the limiting distribution for the proposed estimator is established and data-driven confidence intervals for the explained variance are constructed. We further develop a randomized calibration technique for statistical inference in the presence of weak signals and apply the obtained inference results to a range of important statistical problems, including signal detection and global testing, prediction accuracy evaluation, and confidence ball construction. The numerical performance of the proposed methodology is demonstrated in simulation studies and an analysis of estimating heritability for a yeast segregant data set with multiple traits.
Semi-supervised Inference for Explained Variance in High-dimensional Linear Regression and Its Applicationsread_more
HG G 19.1
7 December 2018
15:00-15:45 		Pascaline Descloux
Université de Genève
Event Details

Research Seminar in Statistics

Title Model selection with Lasso-Zero
Speaker, Affiliation Pascaline Descloux, Université de Genève
Date, Time 7 December 2018, 15:00-15:45
Location HG G 19.1
Abstract In the problem of variable selection in high-dimensional linear regression, the Lasso is known to require a strong condition on the design matrix and the true support of the regression coefficients in order to recover the true set of important variables. This difficulty being attributed to an excessive amount of shrinkage, multistage procedures and nonconcave penalties have been introduced to address this issue. We rather propose another approach called Lasso-Zero, based on the limit solution of Lasso as its tuning parameter tends to zero, in other words where Lasso's shrinkage effect is the weakest. Since this provides an overfitted model, Lasso-Zero relies on the generation of several random noise dictionaries concatenated to the design matrix. The obtained coefficients are thresholded by a parameter tuned by Quantile Universal Thresholding (QUT). We prove that under some beta-min condition, a simplified version of Lasso-Zero recovers the true model under a weaker condition on the design matrix than Lasso, and that it controls the FDR in the orthonormal case if it is tuned by QUT. Numerical experiments show that Lasso-Zero outperforms its competitors in terms of FDR/TPR tradeoff and exact model recovery.
Model selection with Lasso-Zero read_more
HG G 19.1
7 December 2018
16:15-17:00 		Denis Chetverikov
ETH Zürich
Event Details

Research Seminar in Statistics

Title On cross-validated lasso
Speaker, Affiliation Denis Chetverikov, ETH Zürich
Date, Time 7 December 2018, 16:15-17:00
Location HG G 19.1
Abstract In this paper, we derive a rate of convergence of the Lasso estimator when the penalty parameter λ for the estimator is chosen using K-fold cross-validation; in particular, we show that in the model with the Gaussian noise and under fairly general assumptions on the candidate set of values of λ, the prediction norm of the estimation error of the cross-validated Lasso estimator is with high probability bounded from above up to a constant by (slogp/n)1/2⋅log7/8(pn), where n is the sample size of available data, p is the number of covariates, and s is the number of non-zero coefficients in the model. Thus, the cross-validated Lasso estimator achieves the fastest possible rate of convergence up to a small logarithmic factor log7/8(pn). In addition, we derive a sparsity bound for the cross-validated Lasso estimator; in particular, we show that under the same conditions as above, the number of non-zero coefficients of the estimator is with high probability bounded from above up to a constant by slog5(pn). Finally, we show that our proof technique generates non-trivial bounds on the prediction norm of the estimation error of the cross-validated Lasso estimator even if the assumption of the Gaussian noise fails; in particular, the prediction norm of the estimation error is with high-probability bounded from above up to a constant by (slog2(pn)/n)1/4 under mild regularity conditions.
On cross-validated lassoread_more
HG G 19.1

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