<< October 2018 >>

Tuesday, October 2, 2018

Seminar 217, Risk Management: Predicting Portfolio Return Volatility at Median Horizons

Seminar: Risk Seminar | October 2 | 11 a.m.-12:30 p.m. | 1011 Evans Hall

Speaker: Dangxing Chen, UC Berkeley

Commercially available factor models provide good predictions of short-horizon (e.g. one day or one week) portfolio volatility, based on estimated portfolio factor loadings and responsive estimates of factor volatility. These predictions are of significant value to certain short-term investors, such as hedge funds. However, they provide limited guidance to long-term investors, such as Defined...   More >

The challenge of big data and data science for the social sciences: Berkeley Distinguished Lectures in Data Science

Lecture: Other Related Seminars | October 2 | 4:10-5 p.m. | 190 Doe Library

Henry Brady, Dean, Goldman School of Public Policy; Henry Brady, Dean, Goldman School of Public Policy

The 2005 National Science Foundation workshop report on "Cyberinfrastructure for the Social and Behavioral Sciences" (Fran Berman and Henry Brady) argued that the methods of doing research in the social sciences would be transformed by big data and data science and that the social sciences should be centrally involved in studying the impacts of big data and data science on society. In "The...   More >

Wednesday, October 3, 2018

Concentration from Geometry in High Dimension

Seminar: Probability Seminar | October 3 | 3-4 p.m. | 1011 Evans Hall

Santosh Vempala, Georgia Tech

Department of Statistics

The concentration of Lipschitz functions around their expectation is a classical topic that continues to be very active. We will discuss some recent progress, including:
1- A tight log-Sobolev inequality for isotropic logconcave densities
2- A unified and improved large deviation inequality for convex bodies
3- An extension of the above to Lipschitz functions (generalizing the Euclidean...   More >

Statistical challenges in casualty estimation

Seminar: Neyman Seminar | October 3 | 4-5 p.m. | 1011 Evans Hall

Kristian Lum, Human Rights Data Analysis Group

Department of Statistics

An accurate understanding of the magnitude and dynamics of casualties during a conflict is important for a variety of reasons, including historical memory, retrospective policy analysis, and assigning culpability for human rights violations. However, during times of conflict and their aftermath, collecting a complete or representative sample of casualties can be difficult if not impossible. One...   More >

Center for Computational Biology Seminar: Dr. Quaid Morris, Professor, Donnelly Centre for Cellular and Biomolecular Research, University of Toronto

Seminar: Other Related Seminars | October 3 | 4:30-5:30 p.m. | 101 Life Sciences Addition

Center for Computational Biology

Title: Making sense of the “noise” in cancer data

Tuesday, October 9, 2018

Seminar 217, Risk Management: Robust Learning: Information Theory and Algorithms

Seminar: Risk Seminar | October 9 | 11 a.m.-12:30 p.m. | 1011 Evans Hall

Speaker: Jacob Steinhardt, Stanford

This talk will provide an overview of recent results in high-dimensional robust estimation. The key question is the following: given a dataset, some fraction of which consists of arbitrary outliers, what can be learned about the non-outlying points? This is a classical question going back at least to Tukey (1960). However, this question has recently received renewed interest for a combination of...   More >

Letters of recommendation in Berkeley undergraduate admissions: Program evaluation and natural language processing: Berkeley Distinguished Lectures in Data Science

Lecture: Other Related Seminars | October 9 | 4:10-5 p.m. | 190 Doe Library

Jesse Rothstein, Professor, Public Policy and Economics, UC Berkeley

In Fall 2015 and 2016, UC Berkeley asked many freshman applicants to submit letters of recommendation as part of their applications. This was highly controversial. Proponents argued that letters would aid in the identification of disadvantaged students who had overcome obstacles that were not otherwise apparent from their applications, while opponents argued that disadvantaged students were...   More >

Wednesday, October 10, 2018

Large deviations of subgraph counts for sparse Erd\H{o}s--R\'enyi graphs

Seminar: Probability Seminar | October 10 | 3-4 p.m. | 1011 Evans Hall

Nicholas Cook, UCLA

Department of Statistics

For each fixed integer $\ell\ge 3$ we establish the leading order of the exponential rate function for the probability that the number of cycles of length $\ell$ in the Erd\H{o}s--R\'enyi graph $G(N,p)$ exceeds its expectation by a constant factor, assuming $N^{-1/2}\ll p\ll 1$ (up to log corrections) when $\ell\ge 4$, and $N^{-1/3}\ll p\ll 1$ in the case of triangles. We additionally obtain the...   More >

To persist or not to persist?

Seminar: Neyman Seminar | October 10 | 4-5 p.m. | 1011 Evans Hall

Sebastian Schreiber, UC Davis

Department of Statistics

Two long standing, fundamental questions in biology are "Under what conditions do populations persist or go extinct? When do interacting species coexist?" The answers to these questions are essential for guiding conservation efforts and identifying mechanisms that maintain biodiversity. Mathematical models play an important role in identifying these mechanisms and, when coupled with empirical...   More >

Tuesday, October 16, 2018

Seminar 217, Risk Management: Asymptotic Spectral Analysis of Markov Chains with Rare Transitions: A Graph-Algorithmic Approach

Seminar: Risk Seminar | October 16 | 11 a.m.-12:30 p.m. | 1011 Evans Hall

Speaker: Tingyue Gan, UC Berkeley

Parameter-dependent Markov chains with exponentially small transition rates arise in modeling complex systems in physics, chemistry, and biology. Such processes often manifest metastability, and the spectral properties of the generators largely govern their long-term dynamics. In this work, we propose a constructive graph-algorithmic approach to computing the asymptotic estimates of eigenvalues...   More >

Wednesday, October 17, 2018

The Lovász theta function for random regular graphs

Seminar: Probability Seminar | October 17 | 3-4 p.m. | 1011 Evans Hall

Jess Banks, UC Berkeley

Department of Statistics

The Lovász theta function is a classic semidefinite relaxation of graph coloring. In this talk I'll discuss the power of this relaxation for refuting colorability of uniformly random degree-regular graphs, as well as for distinguishing this distribution from one with a `planted' disassoratative community structure. We will see that the behavior of this refutation scheme is consistent with the...   More >

Learning in Google Ads, Machines and People

Seminar: Neyman Seminar | October 17 | 4-5 p.m. | 1011 Evans Hall

Department of Statistics

This talk is in two parts, both of which discuss interesting uses of experiments in Google search ads. In part 1 I discuss how we can inject randomness into our system to get causal inference in a machine learning setting. In part 2. I talk about experiment designs to measure how users learn in response to ads on Google.com.

Friday, October 19, 2018

4th Annual CDAR Symposium 2018

Conference/Symposium: Risk Seminar | October 19 | 8:30 a.m.-6:30 p.m. | Memorial Stadium, University Club

Our conference will feature new developments in data science, highlighting applications to finance and risk management. Confirmed speakers include Jeff Bohn, Olivier Ledoit, Ulrike Malmendier, Steven Kou, Ezra Nahum, Roy Henriksson, and Ken Kroner.

or or by emailing Sang Oum at soum@berkeley.edu by October 12.

Tuesday, October 23, 2018

Seminar 217, Risk Management: Proliferation of Anomalies and Zoo of Factors – What does the Hansen–Jagannathan Distance Tell Us?

Seminar: Risk Seminar | October 23 | 11 a.m.-12:30 p.m. | 1011 Evans Hall

Speaker: Xiang Zhang, SWUFE

Recent research finds that prominent asset pricing models have mixed success in evaluating the cross-section of anomalies, which highlights proliferation of anomalies and zoo of factors. In this paper, I investigate that how is the relative pricing performance of these models to explain anomalies, when comparing their misspecification errors– the Hansen–Jagannathan (HJ) distance measure. I find...   More >

Optimal robot action for and around people: Berkeley Distinguished Lectures in Data Science

Lecture: Other Related Seminars | October 23 | 4:10-5 p.m. | 190 Doe Library

Anca Dragan, Professor, Electrical Engineering and Computer Sciences, UC Berkeley

Estimation, planning, control, and learning are giving us robots that can generate good behavior given a specified objective and set of constraints. What I care about is how humans enter this behavior generation picture, and study two complementary challenges: 1) how to optimize behavior when the robot is not acting in isolation, but needs to coordinate or collaborate with people; and 2) what to...   More >

Wednesday, October 24, 2018

Constructing (2+1)-dimensional KPZ evolutions

Seminar: Probability Seminar | October 24 | 3-4 p.m. | 1011 Evans Hall

Alex Dunlap, Stanford University

Department of Statistics

The (d+1)-dimensional KPZ equation
$\partial_t h = \nu \Delta h + \frac{\lambda}{2}|\nabla h|^2 + \sqrt{D}\dot{W},$
in which \dot{W} is a space--time white noise, is a natural model for the growth of d-dimensional random surfaces. These surfaces are extremely rough due to the white noise forcing, which leads to difficulties in interpreting the nonlinear term in the equation. In...   More >

Safe Learning in Robotics

Seminar: Neyman Seminar | October 24 | 4-5 p.m. | 1011 Evans Hall

Claire Tomlin, UC Berkeley

Department of Statistics

A great deal of research in recent years has focused on robot learning. In many applications, guarantees that specifications are satisfied throughout the learning process are paramount. For the safety specification, we present a controller synthesis technique based on the computation of reachable sets, using optimal control and game theory. In the first part of the talk, we will review these...   More >

Wednesday, October 31, 2018

Rigidity and tolerance for perturbed lattices

Seminar: Probability Seminar | October 31 | 3-4 p.m. | 1011 Evans Hall

Yuval Peres, Microsoft Research

Department of Statistics

Consider a perturbed lattice {v+Y_v} obtained by adding IID d-dimensional Gaussian variables {Y_v} to the lattice points in Z^d.
Suppose that one point, say Y_0, is removed from this perturbed lattice; is it possible for an observer, who sees just the remaining points, to detect that a point is missing?