Neyman Seminar

The Neyman seminar is the statistics seminar in the Department. Historically, it has been focused on applications of Statistics to other fields. Nowadays, it has a very broad scope, with topics ranging from applications of statistics to theory.

The seminar is held on Wednesdays from 4 to 5 in the Jerzy Neyman room, 1011 Evans.

Details of individual seminar events are published in the campus' event system.

You can sign up to the department's seminars@stat mailing list to receive related announcements.

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Recent & Upcoming Neyman Seminars

Adityanand Guntuboyina, University of California, Berkeley
Sep 20, 2017 4:00pm
1011 Evans Hall
Abstract:
We study two convex optimization based procedures for nonparametric function estimation: trend filtering (or higher order total variation denoising) and the Kiefer-Wolfowitz MLE for Gaussian location mixtures. Trend filtering can be seen as a technique for fitting spline-like functions for nonparametric regression with adaptive knot selection. It can also be seen as a special case of LASSO for a...
Stefanie Jegelka, Massachusetts Institute of Technology
Sep 27, 2017 4:00pm
1011 Evans Hall
Abstract:
Discrete Probability distributions with strong negative dependencies (negative association) occur in a wide range of settings in Machine Learning, from probabilistic modeling to randomized algorithms for accelerating a variety of popular ML models. In addition, these distributions enjoy rich theoretical connections and properties. A prominent example are Determinantal Point Processes. In this...
Peter Bühlmann, ETH Zürich
Oct 4, 2017 4:00pm
1011 Evans Hall
Abstract:
Heterogeneity in potentially large-scale data can be beneficially exploited for causal inference and more robust prediction. The key idea relies on invariance and stability across different heterogeneous regimes or sub-populations. What we term as "anchor regression" opens some novel insights and connections between causality and protection (robustness) against worst case interventions in ...
Michael Anderson, University of California, Berkeley
Oct 11, 2017 4:00pm
1011 Evans Hall
Abstract:
Preanalysis plans (PAPs) have become an important tool for limiting false discoveries in field experiments. We evaluate the properties of an alternate approach which splits the data into two samples: An exploratory sample and a confirmation sample. When hypotheses are homogeneous, we describe an improved split-sample approach that achieves 90% of the rejections of the optimal PAP without...
Anca Dragan, University of California, Berkeley
Oct 18, 2017 4:00pm
1011 Evans Hall
Abstract:
Robots are becoming increasingly more capable of optimizing objective functions for physical tasks, from navigation, to dexterous manipulation, to flight. The ultimate goal is to perform these tasks for us, in our environments. We want cars driving on our roads, or personal robots assisting us with activities of daily living as we age in our own homes. Right now, we tend to be merely obstacles to...
Patrick Kline, University of California, Berkeley
Nov 1, 2017 4:00pm
1011 Evans Hall
Abstract:
We propose a general framework for unbiased estimation of quadratic forms of regression coefficients in linear models with unrestricted heteroscedasticity. Economic applications include variance component estimation in multi-way fixed effects and random coefficient models. The large sample distribution of our estimator is studied in an asymptotic framework where the number of regressors grows in...
Fabrizia Mealli, University of Florence
Nov 8, 2017 4:00pm
1011 Evans Hall
Abstract:
Interference arises when an individual's potential outcome depends on the individual treatment and also on the treatment of others. A common assumption in the causal inference literature in the presence of interference is partial interference, implying that the population can be partitioned in clusters of units whose potential outcomes only depend on the treatment of other units within the...
Na Ji, University of California, Berkeley
Nov 15, 2017 4:00pm
1011 Evans Hall
Abstract:
To understand computation in the brain, one needs to understand the input-output relationships for neural circuits and the anatomical and functional relationships between individual neurons therein. Optical microscopy has emerged as an ideal tool in this quest, as it is capable of recording the activity of neurons distributed over millimeter dimensions with sub-micron spatial resolution. I will...
Michael Hudgens, UNC-Chapel Hill
Nov 29, 2017 4:00pm
1011 Evans Hall
Abstract:
A fundamental assumption usually made in causal inference is that of no interference between individuals (or units), i.e., the potential outcomes of one individual are assumed to be unaffected by the treatment assignment of other individuals. However, in many settings, this assumption obviously does not hold. For example, in infectious diseases, whether one person becomes infected depends on who...
Weijie Su, University of Pennsylvania
Dec 6, 2017 4:00pm
1011 Evans Hall
Abstract:
Stochastic gradient descent (SGD) is an immensely popular approach for optimization in settings where data arrives in a stream or data sizes are very large. Despite an ever-increasing volume of works on SGD, less is known about statistical inferential properties of predictions based on SGD solutions. In this paper, we introduce a novel procedure termed HiGrad to conduct inference on predictions,...