Schedule for Spring 2020
Seminars are on Mondays
Time: 4:10pm – 5:00pm
Location: Room 903, 1255 Amsterdam Avenue
Tea and Coffee will be served before the seminar at 3:30 PM, 10th Floor Lounge SSW
Cheese and Wine reception will follow the seminar at 5:10 PM in the 10th Floor Lounge SSW
For an archive of past seminars, please click here.
1/27/2020

Anne van Delft (Ruhr University Bochum, Germany) “Spectral domainbased inference for (nonstationary) functionvalued time series” Abstract: 
1/31/2020 *Friday Time: 11:40 am – 12:40pm Location: Room 903 SSW 
Yuqi Gu (University of Michigan) “Uncover Hidden FineGained Scientific Information: Structured Latent Attribute Models.” Abstract: In modern psychological and biomedical research with diagnostic purposes, scientists often formulate the key task as inferring the finegrained latent information under structural constraints. These structural constraints usually come from the domain experts’ prior knowledge or insight. The emerging family of Structured Latent Attribute Models (SLAMs) accommodate these modeling needs and have received substantial attention in psychology, education, and epidemiology. SLAMs bring exciting opportunities and unique challenges. In particular, with highdimensional discrete latent attributes and structural constraints encoded by a design matrix, one needs to balance the gain in the model’s explanatory power and interpretability, against the difficulty of understanding and handling the complex model structure. In the first part of this talk, I present identifiability results that advance the theoretical knowledge of how the design matrix influences the estimability of SLAMs. The new identifiability conditions guide realworld practices of designing diagnostic tests and also lay the foundation for drawing valid statistical conclusions. In the second part, I introduce a statistically consistent penalized likelihood approach to selecting significant latent patterns in the population. I also propose a scalable computational method. These developments explore an exponentially large model space involving many discrete latent variables, and they address the estimation and computation challenges of highdimensional SLAMs arising from largescale scientific measurements. The application of the proposed methodology to the data from an international educational assessment reveals meaningful knowledge structure of the student population. 
2/3/2020 
Dongming Huang (Harvard) “Controlled Variable Selection with More Flexibility”, with subtitle (if applicable) “Relaxing the Assumptions of ModelX Knockoffs” Abstract: The recent modelX knocko_s method selects variables with provable and nonasymptotical error control and with no restrictions or assumptions on the dimensionality of the data or the conditional distribution of the response given the covariates. The one requirement for the procedure is that the covariate samples are drawn independently and identically from a preciselyknown distribution. In this talk, I will show that the exact same guarantees can be made without knowing the covariate distribution fully, but instead knowing it only up to a parametric model with as many as (np) parameters, where p is the dimension and n is the number of covariate samples (including unlabeled samples if available). The key is to treat the covariates as if they are drawn conditionally on their observed value for a sufficient statistic of the model. Although this idea is simple, even in Gaussian models, conditioning on a sufficient statistic leads to a distribution supported on a set of zero Lebesgue measure, requiring techniques from topological measure theory to establish valid algorithms. I will demonstrate how to do this for mediumdimensional Gaussian models, highdimensional Gaussian graphical models, and discrete graphical models. Simulations show the new approach remains powerful under the weaker assumptions. 
2/6/2020 *Thursday Time: 4:10pm – 5:00pm Location: Room 903 SSW 
Song Mei (Stanford University) Title: Generalization error of linearized neural networks: staircase and doubledescent Abstract: Deep learning methods operate in regimes that defy the traditional statistical mindset. Despite the nonconvexity of empirical risks and the huge complexity of neural network architectures, stochastic gradient algorithms can often find the global minimizer of the training loss and achieve small generalization error on test data. As one possible explanation to the training efficiency of neural networks, tangent kernel theory shows that a multilayers neural network — in a proper large width limit — can be well approximated by its linearization. As a consequence, the gradient flow of the empirical risk turns into a linear dynamics and converges to a global minimizer. Since last year, linearization has become a popular approach in analyzing training dynamics of neural networks. However, this naturally raises the question of whether the linearization perspective can also explain the observed generalization efficacy. In this talk, I will discuss the generalization error of linearized neural networks, which reveals two interesting phenomena: the staircase decay and the doubledescent curve. Through the lens of these phenomena, I will also address the benefits and limitations of the linearization approach for neural networks. 
2/7/2020 *Friday Time: 11:40 am – 12:55pm Location: Room 903 SSW 
Sean Jewell (University of Washington) “Estimation and inference for changepoint models.” Abstract: This talk is motivated by statistical challenges that arise in the analysis of calcium imaging data, a new technology in neuroscience that makes it possible to record from huge numbers of neurons at singleneuron resolution. In the first part of this talk, I will consider the problem of estimating a neuron’s spike times from calcium imaging data. A simple and natural model suggests a nonconvex optimization problem for this task. I will show that by recasting the nonconvex problem as a changepoint detection problem, we can efficiently solve it for the global optimum using a clever dynamic programming strategy. In the second part of this talk, I will consider quantifying the uncertainty in the estimated spike times. This is a surprisingly difficult task, since the spike times were estimated on the same data that we wish to use for inference. To simplify the discussion, I will focus specifically on the changeinmean problem, and will consider the null hypothesis that there is no change in mean associated with an estimated changepoint. My proposed approach for this task can be efficiently instantiated for changepoints estimated using binary segmentation and its variants, L0 segmentation, or the fused lasso. Moreover, this framework allows us to condition on much less information than existing approaches, thereby yielding higherpowered tests. These ideas can be easily generalized to the spike estimation problem. This talk will feature joint work with Toby Hocking, Paul Fearnhead, and Daniela Witten. 
2/10/2020

Aki Nishimura (UCLA) “Bayesian sparse regression for largescale observational healthcare analytics.” Abstract: 
2/17/2020 

2/24/2020 
Clayton Scott (University of Michigan)

3/2/2020 
Joseph Williams (Toronto) 
3/9/2020 
Shahin Tavakoli (Warwick)
“HighDimensional Functional Factor Models” Abstract: We set up theoretical foundations for highdimensional approximate factor models for panel of functional time series (FTS). We first establish a representation result stating that if the first r eigenvalues of the covariance operator of a crosssection of N FTS are unbounded as N diverges and if the (r + 1)th one is bounded, then we can represent each FTS as a sum of a common component driven by r factors, common to (almost) all the series, and a weakly crosscorrelated idiosyncratic component (all the eigenvalues of the idiosyncratic covariance operator are bounded as N diverges). Our model and theory are developed in a general Hilbert space setting that allows for panels mixing functional and scalar time series. We then turn to the estimation of the factors, their loadings, and the common components. We derive consistency results in the asymptotic regime where the number N of series and the number T of time observations diverge, thus exemplifying the “blessing of dimensionality” that explains the success of factor models in the context of highdimensional (scalar) time series. Our results encompass the scalar case, for which they reproduce and extend, under weaker conditions, wellestablished results (Bai & Ng 2002). We provide numerical illustrations and an empirical illustration on a dataset of intraday S&P100 and Eurostoxx 50 stock returns, along with their scalar overnight returns. This is joint work with Gilles Nisol and Marc Hallin.

3/16/2020 
Spring Break 
3/23/2020 
Murali Haran (Penn State) 
3/30/2020 
Alexander Volfovsky (Duke) 
4/6/2020 
Guido Imbens (Stanford)

4/13/2020 
Heather Battey (Imperial College) 
4/20/2020 
Joseph Verducci (Ohio) 
4/272020 
Yuting Wei (CMU) 
5/4/2020 
Avi Feller (Berkeley) 