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.

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Recent & Upcoming Neyman Seminars

Provably Secure Machine Learning
Jacob Steinhardt, Stanford University
Jan 29, 2018 4:00pm
1011 Evans Hall
Abstract:
Deployed machine learning systems create a new class of computer security vulnerabilities where, rather than attacking the integrity of the software itself, malicious actors exploit the statistical nature of the learning algorithms. For instance, attackers can add fake training data, or strategically manipulate input covariates at test time. Attempts so far to defend against these...
Statistical Inference for Finite Alphabet Structures
Merle Behr, University of Göttingen
Jan 31, 2018 4:00pm
1011 Evans Hall
Abstract:
A challenging problem in cancer genetics is that tumors often consist of a few different groups of cells, so called clones, where each clone has different mutations, like copy-number (CN) variations. In whole genome sequencing the mutations of the different clones get mixed up, according to their relative unknown proportion in the tumor. However, CN's of single clones can only take values in a...
Applied Statistics at Tesla
Swupnil Sahai, Tesla (Speaker), Andrej Karpathy, Tesla (Speaker)
Feb 7, 2018 4:00pm
1011 Evans Hall
Abstract:
From estimating the time to failure of battery modules for Reliability Engineering to predicting lane lines from images for Autopilot, statistics plays a vital role in building all of Tesla’s products. In this talk, we present the ways in which Tesla is changing the future of sustainable energy and discuss how statisticians will help us get there.
Testing for two-stage experiments in the presence of interference
Guillaume Basse, Harvard University
Feb 15, 2018 4:00pm
1011 Evans Hall
Abstract:
Many important causal questions concern interactions between units, also known as interference. Examples include interactions between individuals in households, students in schools, and firms in markets. Standard analyses that ignore interference can often break down in this setting: estimators can be badly biased, while classical randomization tests can be invalid. In this talk, I present recent...
Recent Advances in Algorithmic High-Dimensional Robust Statistics
Ilias Diakonikolas, USC
Feb 21, 2018 4:00pm
1011 Evans Hall
Abstract:
Fitting a model to a collection of observations is one of the quintessential problems in machine learning. Since any model is only approximately valid, an estimator that is useful in practice must also be robust in the presence of model misspecification. It turns out that there is a striking tension between robustness and computational efficiency. Even for the most basic high-dimensional tasks,...
Algorithmic Regularization in Over-parameterized Matrix Recovery and Neural Networks with Quadratic Activations
Tengyu Ma, Facebook AI Research
Feb 28, 2018 4:00pm
1011 Evans Hall
Abstract:
Over-parameterized models are widely and successfully used in deep learning, but their workings are far from understood. In many practical scenarios, the learned model generalizes to the test data, even though the hypothesis class contains a model that completely overfits the training data and no regularization is applied. In this talk, we will show that such phenomenon occurs in...
Statistics and Data Science: the Prediction and Modeling Cultures
Roderick Little, University of Michigan
Mar 5, 2018 4:00pm
102 Moffitt Undergraduate Library
Abstract:
I recently taught a course entitled "Seminal Papers and Controversies in Statistics", and Leo Breiman's (2001) article "Statistical Modeling: The Two Cultures" was a very popular paper with students. The paper contrasts the machine learning culture, with it's focus on prediction, with more classical parametric modeling approach to statistics. I am more in the parametric modeling camp, but...
Mediation analysis for count and zero-inflated count data
Jing Cheng, UCSF
Mar 14, 2018 4:00pm
1011 Evans Hall
Abstract:
In health studies, the outcome is often a count or zero-inflated (ZI) count such as the number of decayed, missing and filled teeth (dmft) or surfaces (dmfs); many subjects have zeros because they have not had any cavities. To aid in understanding the underlying mechanisms of diseases and treatments, we developed a series of statistical methods for mediation analyses specifically for count or ZI...
A Unified Theory of Regression Adjustment for Design-based Inference
Joel Middleton, UC Berkeley
Mar 21, 2018 4:00pm
1011 Evans Hall
Abstract:
Under the Neyman causal model, a well-known result is that OLS with treatment-by-covariate interactions cannot harm asymptotic precision of estimated treatment effects in completely randomized experiments. But do such guarantees extend to experiments with more complex designs? This paper proposes a general framework for addressing this question and defines a class of generalized regression...
Solving composite minimization problems arising in statistics and engineering, with applications to phase retrieval
John C. Duchi, Stanford University
Apr 3, 2018 4:00pm
60 Evans Hall
Abstract:
We consider minimization of stochastic functionals that are compositions of a (potentially) non-smooth convex function h and smooth function c. We develop two stochastic methods--a stochastic prox-linear algorithm and a stochastic (generalized) sub- gradient procedure--and prove that, under mild technical conditions, each converges to stationary points of the stochastic objective. Additionally,...
Global Testing Against Sparse Alternatives under Ising Models
Rajarshi Mukherjee, UC Berkeley
Apr 18, 2018 4:00pm
1011 Evans Hall
Abstract:
We study the effect of dependence on detecting sparse signals. In particular, we focus on global testing against sparse alternatives for the magnetizations of an Ising model and establish how the interplay between the strength and sparsity of a signal determines its detectability under various notions of dependence (i.e. the coupling constant of the Ising model). The impact of dependence can be...
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