Abstract: In many applications, it is of interest to cluster
subjects based on very high-dimensional data, often in the presence
of missing data. Although discrete mixture models are routinely
used, we demonstrate pitfalls in high-dimensional...
Abstract: In today’s data-driven society, human beings still
make most critical decisions and yet they are increasingly
utilizing recommendations produced by statistical and machine
learning methods. Given the prevalence of this approach in
many...
Abstract: An abundant literature addresses missing data in an
inferential framework: estimating parameters and their variance
from incomplete tables. Here, we consider supervised-learning
settings: predicting a target when missing values appear in...
Abstract: There is a very extensive literature of statistical
methods for the analysis of data with missing values. I'll provide
a historical overview, including ad-hoc approaches, statistical
models and conditions for ignoring the missingness...
Many challenging problems in modern applications amount to
finding relevant results from an enormous output space of potential
candidates, for example, finding the best matching product from a
large catalog or suggesting related search phrases on a...
Classical algorithms typically provide "one size fits all"
performance, and do not leverage properties or patterns in their
inputs. A recent line of work aims to address this issue by
developing algorithms that use machine learning predictions
to...
Suppose you are monitoring discrete events in real time. Can you
predict what events will happen in the future, and when? Can you
fill in past events that you may have missed? A probability model
that supports such reasoning is the neural Hawkes...
A brief review will be provided first on how deep learning has
disrupted speech recognition and language processing industries
since 2009. Then connections will be drawn between the techniques
(deep learning or otherwise) for modeling speech and...
There are three core orthogonal problems in reinforcement
learning: (1) Crediting actions (2) generalizing across rich
observations (3) Exploring to discover the information necessary
for learning. Good solutions to pairs of these problems are...
