IS&T Seminar: Optimizing Trade-offs for Scalable Machine Learning

Speaker:  Joseph K. Bradley, Carnegie Mellon University

Abstract:  Modern machine learning applications require large models, lots of data, and complicated optimization.  I will discuss scaling machine learning by decomposing learning problems into simpler sub-problems.  This decomposition allows us to trade off accuracy, computational complexity, and potential for parallelization, where a small sacrifice in one can mean a big gain in another.  Moreover, we can tailor our decomposition to our model and data in order to optimize these trade-offs.

I will present two examples.  First, I will discuss parallel optimization for regression, where the goal is to model or predict a label given many other measurements.  Our Shotgun algorithm parallelizes coordinate descent, a seemingly sequential method.  Shotgun theoretically achieves near-linear speedups and empirically is one of the fastest methods for multicore sparse regression.  Second, I will discuss parameter learning for Probabilistic Graphical Models, a powerful class of models of probability distributions.  In both examples, our analysis provides strong theoretical guarantees which guide our very practical implementations.

Biography:  Joseph Bradley is a Ph.D. candidate in Machine Learning at Carnegie Mellon University, advised by Carlos Guestrin.  His thesis is on learning large-scale Probabilistic Graphical Models, focusing on methods which decompose problems to take advantage of parallel computation. Previously, he received a B.S.E. in Computer Science from Princeton University.

For more information contact the technical host Reid Porter, rporter@lanl.gov, 665-7508. 

Downloand announcement here.

Hosted by the Information Science and Technology Institute (ISTI)