Statistics for Structures Seminar

Place: (usually) University of Amsterdam, Science park 105-107 KdVI, room F3.20

Time: The seminar is (usually) every second Friday between 15:00-16:00 (before the Bayes club)

The Statistics for Structures Seminar is an informal seminar focusing on the recent development of statistics on structural data. The talks concern methodological, theoretical, and applied findings. The focus of the seminar is both on discussing the existing literature and presenting new results in the topic.

Source of the picture: http://www.unc.edu/~unclng/Internet_History.htm

Schedule of the seminar

 Date Speaker Title Location February 2415:00-16:00 Jarno HartogUniversity of Amsterdam TBA TBA room March 1715:00-16:00 Loic SchwallerLeiden University TBA TBA room April 715:00-16:00 Peter BloemVrije Universiteit Amsterdam TBA TBA room May 12 15:00-16:00 Eduard Belitser Vrije Universiteit Amsterdam TBA TBA room May 24 15:00-16:00 TBA TBA TBA room October 715:00-16:00 Guus RegtsUniversity of Amsterdam Approximation algorithms for graph polynomials and partition functions UvA ScP 105-107 KdVI room F3.20 November 415:00-16:00 Marco GrzegorczykRijksuniversiteit Groningen Bayesian inference of semi-mechanistic network models UvA ScP 105-107 KdVI room F3.20 November 2515:00-16:00 Joris MooijUniversity of Amsterdam Automating Causal Discovery and Prediction UvA ScP 105-107 KdVI room F3.20 December 915:00-16:00 Stephanie van der PasLeiden University Bayesian community detection UvA ScP 105-107 KdVI room F3.20

Abstracts

Guus Regts: Approximation algorithms for graph polynomials and partition functions

The correlation decay method, pioneered by Weitz in 2006, is a method that yields efficient (polynomial time) deterministic approximation algorithms for computing partition functions of several statistical models. While the method yields deterministic algorithms it has a probabilistic flavour. In this talk I will sketch how this method works for the hardcore model, i.e., for counting independent sets in bounded degree graphs. After that I will discuss a different method pioneerd by Barvinok based on Taylor approximations of the logarithm of the partition function and on the location of zeros of the partition function. I will explain how this approach can give polynomial time approximation algorithms for computing several partition functions on bounded degree graphs.
This is based on joint work with Viresh Patel (UvA)

Marco Grzegorczyk: Bayesian inference of semi-mechanistic network models

A topical and challenging problem for statistics and machine learning is to infer the structure of complex systems of interacting units.In many scientific disciplines such systems are represented by interaction networks described by systems of differential equations. My presentation is about a novel semi-mechanistic Bayesian modelling approach for infering the structures and parameters of these interaction networks from data. The inference approach is based on gradient matching and a non-linear Bayesian regression model. My real.-world applications stem from the topical field of computational systems biology, where researchers aim to reconstruct the structure of biopathways or regulatory networks from postgenomic data. My focus is on investigating to which extent certain factors influence the network reconstruction accuracy. To this end, I compare not only (i) different methods for model selection, including various Bayesian information criteria and marginal likelihood approximation methods, but also (ii) different ways to approximate the gradients of the observed time series. Finally, I cross-compare the performance of the new method with a set of state-of-the art network reconstruction networks, such as Bayesian networks. Within the comparative evaluation studies I employ ANOVA schemes to disambiguate to which extents confounding factors impact on the network reconstruction accuracies.

Joris Mooij: Automating Causal Discovery and Prediction

The discovery of causal relationships from experimental data and the construction of causal theories to describe phenomena are fundamental pillars of the scientific method. How to reason effectively with causal models, how to learn these from data, and how to obtain causal predictions has been traditionally considered to be outside of the realm of statistics. Therefore, most empirical scientists still perform these tasks informally, without the help of mathematical tools and algorithms. This traditional informal way of causal inference does not scale, and this is becoming a serious bottleneck in the analysis of the outcomes of large-scale experiments nowadays. In this talk I will describe formal causal reasoning methods and algorithms that can help to automate the process of scientific discovery from data.

Stephanie van der Pas: Bayesian community detection

In the stochastic block model, nodes in a graph are partitioned into classes ('communities') and it is assumed that the probability of the presence of an edge between two nodes solely depends on their class labels. We are interested in recovering the class labels, and employ the Bayesian posterior mode for this purpose. We present results on weak consistency (where the fraction of misclassified nodes converges to zero) and strong consistency (where the number of misclassified nodes converges to zero) of the posterior mode , in the 'dense' regime where the probability of an edge occurring between two nodes remains bounded away from zero, and in the 'sparse' regime where this probability does go to zero as the number of nodes increases.

Previous Seminar Talks

 Date Speaker Title Location October 1615:00-16:00 Ervin TánczosEindhoven University of Technology Adaptive Sensing for Recovering Structured Sparse Sets UvA ScP 904 room C0.110 October 2315:00-16:00 Moritz SchauerUniversity of Amsterdam Working with graphs in Julia UvA ScP 904 room A1.04 November 615:00-16:00 Fengnan GaoLeiden University On the Estimation of the Preferential Attachment Network Model UvA ScP 904 room A1.04 March 1815:00-16:00 Paulo SerraUniversity of Amsterdam Dimension Estimation using Random Connection Models UvA ScP 904 room F1.02 April 1515:00-16:00 Rui Castro Eindhoven University of Technology Distribution-Free Detection of Structured Anomalies: Permutation and Rank-Based Scans UvA ScP 904 room G2.10 April 2215:00-16:00 Koen van OostenLeiden University Achieving Optimal Misclassification Proportion in Stochastic Block Model UvA ScP 904 room G2.10 May 1315:00-16:00 Wessel van WieringenVrije University Amsterdam A tale of two networks: two GGMs and their differences UvA ScP 904 room A1.04 June 315:00-16:00 Pariya BehrouziRijksuniversiteit Groningen Detecting Epistatic Selection in the Genome of RILs via a latent Gaussian Copula Graphical Model UvA ScP 904 room A1.10

 Date Speaker Title Location March 13 15:30 Chao GaoYale University Rate-optimal graphon estimation Leiden University room 401 April 1 15:00-17:00 Moritz SchauerUniversity of Amsterdam Botond SzaboUniversity of Amsterdam A graphical perspective on Gauss-Markov process priors Detecting community structures in networks VU Amsterdam room WN-M607 April 1714:00-16:00 Bartek KnapikVrije University Johannes Schmiedt-HieberLeiden University Point process modelling for directed interaction networks Detecting community structures in networks UvA ScP 904 room A1.10 April 2414:00-16:00 Fengnan GaoLeiden University Stephanie van der PasLeiden University A quick survey in random graph models Stochastic block models UvA ScP 904 room A1.10 May 115:00-16:00 Kolyan RayLeiden University Estimating Sparse Precision Matrix UvA ScP 904 room A1.10 May 815:30-17:30 Gino B. KpogbezanLeiden University Jarno HertogUniversity of Amsterdam Variational Bayesian SEM for undirected Network recovery using external data Kernel-based regression UvA ScP 904 room D1.116

 Date Speaker Title Location March 13 15:30 Leila MohammadiLeiden University A nonparametric view of network models Leiden University room 312 April 4 15:00-16:00 Aad van der VaartLeiden University Harry van ZantenUniversity of Amsterdam Stochastic block models Regression on graphs UvA ScP 904room A 1.06

Abstracts and slides

Ervin Tánczos: Adaptive Sensing for Recovering Structured Sparse Sets

Consider the problem of recovering the support of a sparse signal, that is we are given an unknown s-sparse vector $x$, whose non-zero elements are $\mu>0$ and we are tasked with recovering the support of $x$. Suppose each coordinate of $x$ is measured independently with additive standard normal noise. In case the support can be any s-sparse set, we know that $\mu$ needs to scale as $\sqrt{\log n}$ for us to be able to reliably recover the support. However, in some practical settings the support set has a certain structure. For instance in gene-expression studies the signal support can be viewed a submatrix of the gene-expression matrix, or when searching for network anomalies the support set can be viewed as a star in the network graph. In such cases we might be able to recover the support of weaker signals. This question has been recently addressed by various authors. Now consider a setting where instead of measuring every coordinate of $x$ the same way, we can collect observations sequentially using our knowledge accumulated from previous observations. This setup is usually referred to as active learning" or adaptive sensing". We aim to characterize the difficulty of accurately recovering structured support sets using adaptive sensing, and also provide near optimal procedures for support recovery. In particular we are interested in the gains adaptive sensing provides over non-adaptive sensing in these situations. We consider two measurement models, namely coordinate-wise observations and compressive sensing. Our results show that adaptive sensing strategies can improve on non-adaptive ones both by better mitigating the effect of measurement noise, and capitalizing on structural information to a larger extent.

Moritz Schauer: Working with graphs in Julia

Julia is an emerging technical programming language, which has some properties which make it especially interesting for the implementation of Bayesian methods. In this talk I give an introduction into the graph-related functionality Julia provides. After a demonstration how to create and display graphs in Julia using the package Graphs.jl, I show how to perform Bayesian inference on a "smooth" function defined on a graph in Julia.

Fengnan Gao: On the Estimation of the Preferential Attachment Network Model

The preferential attachment (PA) network is a popular way of modeling the social networks, the collaboration networks and etc. The PA network model is an evolving network where new nodes keep coming in. When a new node comes in, it establishes only one connection with an existing node. The random choice on the existing node is via a multinormial distribution with probability weights based on a preferential function $f$ on the degrees. f is assumed apriori nondecreasing, which means the nodes with high degrees are more likely to get new connections, i.e. "the rich get richer". We proposed an estimator on f, that maps the natural numbers to the positive real line. We show, with techniques from branching process, our estimator is consistent. If $f$ is affine, meaning $f(k) = k + delta$, it is well known that such a model leads to a power-law degree distribution. We proposed a maximum likelihood estimator for delta and establish a central limit result on the MLE of delta.

Paulo Serra: Dimension Estimation using Random Connection Models

In statistics we often want to discover (sometimes impose) structure on observed data, and dimension plays a crucial role in this task. The setting that I will consider in this talk is the following: some high-dimensional data has been collected but it (potentially) lives in some lower dimensional space (this lower dimension is called the intrinsic dimension of the dataset); the objective is to estimate the intrinsic dimension of the high-dimensional dataset.
Why would we want to to this? Dimensionality reduction techniques (e.g., PCA, manifold learning) usually rely on knowledge about intrinsic dimension. Knowledge about dimension is also important to try to avoid the curse of dimensionality. From a computational perspective, the dimension of a dataset has impact in terms of the amount of space needed to store data (compressibility). The speed of algorithms is also commonly affected by the dimension of input data. One can also envision situations where we have access to some regression data, but the design points are unknown (this occurs, for example, in graphon estimation problems); the dimension of the design space has a large impact on the rate with which the regression function can be recuperated.
Our approach relies on having access to a certain graph: each vertex represents an obser- vation, and there is an edge between two vertices if the corresponding observations are close in some metric. We model this graph as a random connection model (a model from continuum percolation), and use this to propose estimators for the intrinsic dimension based on the dou- bling property of the Lebesgue measure. I will give some conditions under which the dimension can be estimated consistently, and some bounds on the probability of correctly recuperating an integer dimension. I will also show some numerical results and compare our estimators with some competing approaches from the literature.
This is joint work with Michel Mandjes.

Rui Castro (TU/e): Distribution-Free Detection of Structured Anomalies: Permutation and Rank-Based Scans

The scan statistic is by far the most popular method for anomaly detection, being popular in syndromic surveillance, signal and image processing and target detection based on sensor networks, among other applications. The use of scan statistics in such settings yields an hypothesis testing procedure, where the null hypothesis corresponds to the absence of anomalous behavior. If the null distribution is known calibration of such tests is relatively easy, as it can be done by Monte-Carlo simulation. However, when the null distribution is unknown the story is less straightforward. We investigate two procedures: (i) calibration by permutation and (ii) a rank-based scan test, which is distribution-free and less sensitive to outliers. A further advantage of the rank-scan test is that it requires only a one-time calibration for a given data size making it computationally much more appealing than the permutation-based test. In both cases, we quantify the performance loss with respect to an oracle scan test that knows the null distribution. We show that using one of these calibration procedures results in only a very small loss of power in the context of a natural exponential family. This includes for instance the classical normal location model, popular in signal processing, and the Poisson model, popular in syndromic surveillance. Numerical experiments further support our theory and results (joint work with Ery Arias-Castro, Meng Wang (UCSD) and Ervin Tánczos (TU/e)).

Koen van Oosten: Achieving Optimal Misclassification Proportion in Stochastic Block Model

Community detection is a fundamental statistical problem in network data analysis. Many algorithms have been proposed to tackle this problem. Most of these algorithms are not guaranteed to achieve the statistical optimality of the problem, while procedures that achieve information theoretic limits for general parameter spaces are not computationally tractable. In my talk I present a computationally feasible two-stage method that achieves optimal statistical performance in misclassification proportion for stochastic block model under weak regularity conditions. This two-stage procedure consists of a refinement stage motivated by penalized local maximum likelihood estimation. This stage can take a wide range of weakly consistent community detection procedures as initializer, to which it applies and outputs a community assignment that achieves optimal misclassification proportion with high probability.

Wessel van Wieringen: A tale of two networks: two GGMs and their differences

The two-sample problem is addressed from the perspective of Gaussian graphical models (GGMs), in exploratory and confirmatory fashion. The former amounts to the estimation of a precision matrix for each group. First, this is done group-wise by means of penalized maximum likelihood with an algebraically proper l2-penalty, for which an analytic expression of the estimator and its properties are derived. To link the groups the ridge penalty is then augmented with an fused term, which penalizes the difference between the group precisions. The confirmatory part concentrates on the situation in which partial correlations are systematically smaller/larger (in an absolute sense) in one of the groups. Data in both groups again are assumed to follow a GGM but now their partial correlations are proportional, differing by a multiplier (common to all partial correlations). The multiplier reflects the overall strength of the conditional dependencies. As before model parameters are estimated by means of penalized maximum likelihood, now using a ridge-like penalty. A permutation scheme to test for the multiplier differing from zero is proposed. A re-analysis of publicly available gene expression data on the Hedgehog pathway in normal and cancer prostate tissue combines both strategies to show its activation in the disease group.

Pariya Behrouzi: Detecting Epistatic Selection in the Genome of RILs via a latent Gaussian Copula Graphical Model

Recombinant Inbred Lines (RILs) derived from divergent parental lines can display extensive segregation distortion and long-range linkage disequilibrium (LD) between distant loci on same or different chromosomes. These genomic signatures are consistent with epistatic selection having acted on entire networks of interacting parental alleles during inbreeding. The reconstruction of these interaction networks from observations of pair-wise marker-marker correlations or pair-wise genotype frequency distortions is challenging as multiple testing approaches are under-powered and true long-range LD is difficult to distinguish from drift, particularly in small RIL panels. Here we develop an efficient method for reconstructing an underlying network of genomic signatures of high-dimensional epistatic selection from multi-locus genotype data. The network captures the conditionally dependent short- and long-range LD structure of RIL genomes and thus reveals aberrant marker-marker associations that are due to epistatic selection rather than gametic linkage. The network estimation relies on penalized Gaussian copula graphical models, which accounts for the large number of markers p and the small number of individuals n. We overcome the p >> n problem by using a penalized maximum likelihood technique that imposes an l1 penalty on the precision matrix of the latent process inside the EM estimation. A multi-core implementation of our algorithm makes it feasible to estimate the graph in high-dimensions (max markers approximately 3000). We demonstrate the efficiency of the proposed method on simulated datasets as well as on genotyping data in A.thaliana and Maize.

Chao Gao: Rate Optimal Graphon Estimation

Network analysis is becoming one of the most active research areas in statistics. Significant advances have been made recently on developing theories, methodologies and algorithms for analyzing networks. However, there has been little fundamental study on optimal estimation. In this paper, we establish optimal rate of convergence for graphon estimation. Link: http://arxiv.org/abs/1410.5837

Botond Szabo: Detecting community structures in networks

I will review different algorithms for community detection described in: NEWMAN, M. E. J. (2004). Detecting community structure in networks. Eur. Phys. J. B 38 321-330. NEWMAN, M. E. J. (2006). Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E (3) 74 036104, 19. MR2282139 MR2282139 (2007j:82115)

Moritz Schauer: A graphical perspective on Gauss-Markov process priors

In this short talk I look at the connections between Gauss-Markov process priors on a line and Gaussian Markov Random fields on a tree via the midpoint displacement procedure. The Markov-property of the prior corresponds to a sparsity constraint for the prior precision on the tree which allows to solve the Gaussian inverse problem under quasi-linear time and space constraints using a divide and conquer algorithm. This leads to the notion of computationally desirable sparsity properties connecting Gramian matrix stemming from an Gaussian inverse problem and the prior precision matrix.

Johannes Schmiedt-Hieber: High-dimensional covariance estimation

I am going to talk about the paper: Ravikumar, Pradeep, Martin J. Wainwright, Garvesh Raskutti and Bin Yu High-dimensional covariance estimation by minimizing l1-penalized log-determinant divergence, EJS, 2011

Bartek Knapik: Point process modelling for directed interaction networks

I will present the paper: Perry, Patrick O.; Wolfe, Patrick J. Point process modelling for directed interaction networks. J. R. Stat. Soc. Ser. B. Stat. Methodol. 75 (2013), no. 5, 821-849

Fengnan Gao: A quick survey in random graph models

We will review several important random graph models, their definitions and important results on them. The models include Erdős–Rényi model, configuration model and preferential attachment model. We will focus on preferential attachment model. Most of the presentation is based on Remco van der Hofstad's lecture notes http://www.win.tue.nl/~rhofstad/NotesRGCN.pdf

Stephanie van der Pas: Stochastic block models

I will review the paper: Antoine Channarond, Jean-Jacques Daudin, and Stéphane Robin. Classification and estimation in the Stochastic Blockmodel based on the empirical degrees. Electron. J. Statist. Volume 6 (2012), 2574-2601. Link: http://projecteuclid.org/euclid.ejs/1357913089

Kolyan Ray: Estimating Sparse Precision Matrix

I will present the paper: Cai, Tony, Weidong Liu and Harrison H. Zhou. Estimating Sparse Precision Matrix: Optimal Rates of Convergence and Adaptive Estimation Link: http://arxiv.org/abs/1212.2882

Gino B. Kpogbezan: Variational Bayesian SEM for undirected Network recovery using external data

Recently we developed a Bayesian structural equation model (SEM) framework with shrinkage priors for undirected network reconstruction. It was shown that Bayesian SEM in combination with variational Bayes is particularly attractive as it performs well, is computationally very fast and a flexible framework. A posteriori variable selection is feasible in our Bayesian SEM and so is the use of shrinkage priors. These shrinkage priors depend on all regression equations allowing borrowing of information across equations and improve inference when the number of features is large. An empirical Bayes procedure is used to estimate our hyperparameters. We also showed in simulations that our approach can outperform popular (sparse) methods. Here, we focus on addressing the problem of incorporating external data and/or prior information into network inference. In many settings information regarding network connectivity is often available. It is then natural to take such information into account during network reconstruction. Based on Bayesian SEM we propose a new model that focuses on the use of external data. It performs better than that of our Bayesian SEM when the external information is relevant, and as good when it is not.

Jarno Hertog: Kernel-based regression

I will discuss the basic kernel approach to regression in the context of graphs and present an number of methods to construct such kernels as in section 8.4 of Statistical Analysis of Network Data by Eric D. Kolaczyk.