Supported by the

The

Northwest Probability Seminars are mini-conferences held at the University of Washington and organized in collaboration with the Oregon State University, the University of British Columbia and the University of Oregon. There is no registration fee.

The Scientific Committee for the NW Probability Seminar 2019 consists of Omer Angel (U British Columbia), Chris Burdzy (U Washington), Zhenqing Chen (U Washington), Yevgeniy Kovchegov (Oregon State U) and David Levin (U Oregon).

HOTEL INFORMATION

**
Hotels
near the University of Washington**.

The talks will take place in Mary Gates Hall 241
(see the **map**).

Parking on UW campus is free on Saturdays only after noon.
See
**
parking information**.

- 10:00 - 10:50
**Coffee**Mary Gates Commons - 10:50 - 11:35
**Jerry Li**, Microsoft Research**Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection**

We study two problems in high-dimensional robust statistics:*robust mean estimation*and*outlier detection*. In robust mean estimation the goal is to estimate the mean $\mu$ of a distribution on $\mathbb{R}^d$ given $n$ independent samples, an epsilon fraction of which have been corrupted by a malicious adversary. In outlier detection the goal is to assign an*outlier score*to each element of a data set such that elements more likely to be outliers are assigned higher scores. Our algorithms for both problems are based on a new outlier scoring method we call QUE-scoring based on*quantum entropy regularization*. For robust mean estimation, this yields the first algorithm with optimal error rates and nearly-linear running time $\tilde O(nd)$ in all parameters. For outlier detection, we evaluate the performance of QUE-scoring via extensive experiments on synthetic and real data, and demonstrate that it often performs better than previously proposed algorithms.

Joint work with Yihe Dong and Samuel B. Hopkins. - 11:40 - 12:25
**Sayan Banerjee**, University of North Carolina, Chapel Hill**Non-parametric change point detection in growing networks**

Motivated by applications of modeling both real world and probabilistic systems such as recursive trees, the last few years have seen an explosion in models for dynamically evolving networks. In this talk, we consider models of growing networks which evolve via new vertices attaching to the pre-existing network according to one attachment function $f$ till the system grows to size $\tau(n) < n$, when new vertices switch their behavior to a different function $g$ till the system reaches size $n$. We explore the effect of this change point on the evolution and final degree distribution of the network. In particular, we consider two cases, the standard model where $\tau(n) = \gamma n$ as well as the quick big bang model when $\tau(n) = n^ \gamma $ for some $0 < \gamma < 1$. In the former case, we obtain deterministic "fluid limits" to track the degree evolution in the sup-norm metric and in the latter case, we show that the effect of the pre-change point dynamics "washes out" when the network reaches size $n$. We also devise non-parametric, consistent estimators to detect the change point. Our methods exploit and develop new techniques connecting inhomogeneous continuous time branching processes (CTBP) to the evolving networks.

Joint work with Shankar Bhamidi and Iain Carmichael. - 12:30 - 2:00
**Lunch**, catered, Mary Gates Commons - 2:00 - 2:50
**Dana Randall**, Georgia Institute of Technology**"Birnbaum Lecture": Title**

Abstract - 3:00 - 3:55
**Coffee**Mary Gates Commons - 3:55 - 4:40
**Delphin Sénizergues**, University of British Columbia**Geometry of the $\alpha$-stable component**

For $\alpha \in (1,2]$, the $\alpha$-stable component arises as the universal scaling limit of a connected component of a critical random graphs with i.i.d. degrees having a given $\alpha$-dependent power-law tail behavior. In this talk, I will try to explain how this object arises in (Conchon-Kerjan, Goldschmidt 2019+), where it is constructed from a biased version of the $\alpha$-stable tree, by gluing a certain number of leaves along their paths to the root. I will then expose some of the results from (Goldschmidt, Haas, Sénizergues 2019+) where we understand the distribution of the object through its discrete "random finite-dimensional marginals". It allows us in particular to describe the object as a gluing of rescaled $\alpha$-stable trees along some random discrete multigraph.

Joint work with Christina Goldschmidt and Bénédicte Haas. - 4:45 - 5:30
**Speaker name**, Affiliation**Title**

Abstract - 6:15 No-host
**dinner**.- Restaurant: TBA