Statistics Seminar Series – Fall 2017

Schedule for Fall 2017

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.


Yoav Benjamini (Tel Aviv University)

“The replicability problems in science: it’s not the p-value’s fault”

Abstract — Significance testing, and the p-value as its symbol, have become the statistical scapegoat for the replicability problems in science.  Instead, I shall argue that the two main statistical obstacles to replicability are (i) unattended inference on the selected, and (ii) ignoring the relevant variability. I shall review current approaches to selective inference, and give but one example of the second obstacle in mouse phenotyping. 


Aurelie Lozano (IBM)

“Sparse + Group-Sparse Dirty Models: Statistical Guarantees without Unreasonable Conditions and a Case for Non-Convexity.”

Abstract — Imposing sparse + group-sparse superposition structures in high-dimensional parameter estimation is known to provide flexible regularization that is more realistic for many real-world problems. For example, such a superposition enables partially-shared support sets in multi-task learning, thereby striking the right balance between parameter overlap across tasks and task specificity. Existing theoretical results on estimation consistency, however, are problematic as they require too stringent an assumption: the incoherence between sparse and group-sparse superposed components. In this talk, we fill the gap between the practical success and suboptimal analysis of sparse + group-sparse models, by providing the first consistency results that do not require unrealistic assumptions. We also study non-convex counterparts of sparse + group-sparse models. Interestingly, we show that these are guaranteed to recover the true support set under much milder conditions and with smaller sample size than convex models, which might be critical in practical applications as illustrated by our experiments.

9/25/17 Flori Bunea (Cornell)

“Structured Sparse Latent Variable Models For Overlapping clustering with Love”

Abstract pdf



Time: 4:00 – 5:00

Room: 717 Hamilton

Jianqing Fan (Princeton)

“Uniform pertubation analysis of eigenspaces and its applications to Community Detection, Ranking and Beyond.”


Spectral methods have been widely used for a large class of challenging problems, ranging from top-K ranking via pairwise comparisons, community detection, factor analysis, among others.

Analyses of these spectral methods require super-norm perturbation analysis of top eigenvectors.  This allows us to UNIFORMLY approximate elements in eigenvectors  by linear functions of the observed random matrix that can be analyzed further.  We first establish such an infinity-norm pertubation bound for top eigenvectors and apply the idea to several challenging problems such as top-K ranking, community detections, Z_2-syncronization and matrix completion.  We show that the spectral methods are indeed optimal for these problems.  We illustrate these methods via simulations.


Long Nguyen (University of Michigan)


Qiaozhu Mei (University of Michigan)


Yuxin Chen (Princeton)


Lingzhou Xue (Penn State)


Alexandre Tsybakov (ENSAE)


University Holiday


Aditya Guntuboyina (Berkeley)

11/20/17 Douglas Nychka (National Center for Atmospheric Research)

Rajarshi Mukherjee (Berkeley)



Richard Olshen (Stanford)


Sarah Heaps (Newcastle University)