Danny Bickson

ICML Conference 2011 Conference Paper

Parallel Coordinate Descent for L1-Regularized Loss Minimization

• Joseph K. Bradley
• Aapo Kyrola
• Danny Bickson
• Carlos Guestrin

UAI Conference 2010 Conference Paper

GraphLab: A New Framework For Parallel Machine Learning

• Yucheng Low
• Joseph E. Gonzalez
• Aapo Kyrola
• Danny Bickson
• Carlos Guestrin
• Joseph M. Hellerstein

Designing and implementing efficient, provably correct parallel machine learning (ML) algorithms is challenging. Existing high-level parallel abstractions like MapReduce are insufficiently expressive while low-level tools like MPI and Pthreads leave ML experts repeatedly solving the same design challenges. By targeting common patterns in ML, we developed GraphLab, which improves upon abstractions like MapReduce by compactly expressing asynchronous iterative algorithms with sparse computational dependencies while ensuring data consistency and achieving a high degree of parallel performance. We demonstrate the expressiveness of the GraphLab framework by designing and implementing parallel versions of belief propagation, Gibbs sampling, Co-EM, Lasso and Compressed Sensing. We show that using GraphLab we can achieve excellent parallel performance on large scale real-world problems.

NeurIPS Conference 2010 Conference Paper

Inference with Multivariate Heavy-Tails in Linear Models

• Danny Bickson
• Carlos Guestrin

Heavy-tailed distributions naturally occur in many real life problems. Unfortunately, it is typically not possible to compute inference in closed-form in graphical models which involve such heavy tailed distributions. In this work, we propose a novel simple linear graphical model for independent latent random variables, called linear characteristic model (LCM), defined in the characteristic function domain. Using stable distributions, a heavy-tailed family of distributions which is a generalization of Cauchy, L\'evy and Gaussian distributions, we show for the first time, how to compute both exact and approximate inference in such a linear multivariate graphical model. LCMs are not limited to only stable distributions, in fact LCMs are always defined for any random variables (discrete, continuous or a mixture of both). We provide a realistic problem from the field of computer networks to demonstrate the applicability of our construction. Other potential application is iterative decoding of linear channels with non-Gaussian noise.