“Variational Perspectives on Machine Learning: Algorithms, Inference, and Fairness”Machine learning plays a key role in shaping the decisions made by a growing number of institutions.…

Principal components analysis (PCA) is a well-known technique for approximating a data set represented by a matrix by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets…

Selective Inference is the problem of testing hypotheses that are chosen or suggested by the
data. Inference after variable selection in high-dimensional linear regression is a common
example of…

Tuesday, February 4, 2014 at 4:15pm
Upson Hall, B17
ORIE Colloquium: John Duchi (UC Berkeley) - Machine Learning: a Discipline of Resource Tradeoffs
Joint colloquium with Computer Science.
How…

Abstract: In this talk, we show how to transform any optimization problem that arises from fitting a machine learning model into one that (1) detects and removes contaminated data from the…