Columbia University Statistics Student Seminar
Schedule for Spring 2015
Seminars are on Wednesdays
Time: 12:00pm – 1:00pm
Location: Room 903 SSW, 1255 Amsterdam Avenue
If you are one of the great speakers, please click here to find out information for speakers.
Our aim is, via the talks of the speakers, to provide the students with opportunities to explore different research potentials in a relatively casual environment.
Previous schedules: Fall 2014
Dr. David Pettey (SIG)
“The Wholesaler Marketplace: Handling Retail Order Flow”
SIG recently (2014) entered the Wholesaler equity business. This presentation will show in part how we tried to independently piece together what this business was about, and how we could get rough independent estimates on the potential profitability of competitors. One approach we used was to restrict ourselves to publically available data, see how much we could learn and what bounds we could reasonably put on the profitability. The focus of the talk will be how you can independently use publically available data to understand certain lesser known segments of the marketplace.
Prof. Guang Cheng (Purdue University)
“Nonparametric Bernstein-Von Mises Phenomenon: A Tuning Prior Perspective”
In this talk, we investigate statistical inference on infinite-dimensional parameters in a Bayesian framework. The main contribution is to demonstrate that nonparametric Bernstein-von Mises theorem can be established in a very general class of nonparametric regression models under a novel tuning prior (indexed by a non-random hyperparameter). Surprisingly, this type of prior ingeniously connects two important classes of statistical methods: nonparametric Bayes and smoothing spline. The intrinsic connection with smoothing spline greatly facilitates both theoretical analysis and practical implementation for nonparametric Bayesian inference. For example, we can employ generalized cross validation to select a proper tuning prior, under which the constructed credible regions/intervals are frequentist valid.
|2/04/2015||Prof. Tony Jebara (Columbia)|
|2/11/2015||Heng Yang (CUNY)|
|2/18/2015||Prof. Christopher Rothe (Columbia)|
|2/25/2015||Dr. Daniel Soudry (Columbia)|
Prof. David Blei (Columbia)
“Scaling and Generalizing Variational Inference”
Latent variable models have become a key tool for the modern statistician, letting us express complex assumptions about the hidden structures that underlie our data. Latent variable models have been successfully applied in numerous fields including natural language processing, computer vision, population genetics, and many others.
The central computational problem in latent variable modeling is posterior inference, the problem of approximating the conditional distribution of the latent variables given the observations. Inference is essential to both exploratory and predictive tasks. Modern
Bayesian statistics, however, has not yet reached this potential. First, statisticians and scientists regularly encounter massive data sets, but existing algorithms do not scale well. Second, most approximate inference algorithms are not generic; each must be adapted to the specific model at hand. This requires significant model-specific analysis, which precludes us from easily exploring a variety of models.
In this talk I will discuss our recent research on addressing these two limitations. First I will describe stochastic variational inference, an approximate inference algorithm for handling massive data sets. Stochastic inference is easily applied to a large class of
This is joint work based on these two papers:
M. Hoffman, D. Blei, J. Paisley, and C. Wang. Stochastic variational
R. Ranganath, S. Gerrish and D. Blei. Black box variational inference.
|3/11/2015||Ju Sun (Columbia)|
|4/01/2015||Prof. Peter F. Halpin (NYU)|
|4/08/2015||Dr. Alekh Agarwal (Microsoft Research)|
|4/15/2015||Dr. Lars Buesing (Columbia)|