259 citations · 2.9k across the 123 of their papers we have counts for
14 papers · 1 filter
Lectures on Randomized Numerical Linear Algebra
Petros Drineas, Michael W. Mahoney
This chapter is based on lectures on Randomized Numerical Linear Algebra from the 2016 Park City Mathematics Institute summer school on The Mathematics of Data.
Avoiding Synchronization in First-Order Methods for Sparse Convex Optimization
Aditya Devarakonda, Kimon Fountoulakis, James Demmel +1
Parallel computing has played an important role in speeding up convex optimization methods for big data analytics and large-scale machine learning (ML). However, the scalability of…
A Berkeley View of Systems Challenges for AI
Ion Stoica, Dawn Song, Raluca Ada Popa +11
With the increasing commoditization of computer vision, speech recognition and machine translation systems and the widespread deployment of learning-based back-end technologies suc…
LASAGNE: Locality And Structure Aware Graph Node Embedding
Evgeniy Faerman, Felix Borutta, Kimon Fountoulakis +1
In this work we propose Lasagne, a methodology to learn locality and structure aware graph node embeddings in an unsupervised way. In particular, we show that the performance of ex…
Rethinking generalization requires revisiting old ideas: statistical mechanics approaches and complex learning behavior
Charles H. Martin, Michael W. Mahoney
We describe an approach to understand the peculiar and counterintuitive generalization properties of deep neural networks. The approach involves going beyond worst-case theoretical…
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization
Shusen Wang, Farbod Roosta-Khorasani, Peng Xu +1
For distributed computing environment, we consider the empirical risk minimization problem and propose a distributed and communication-efficient Newton-type optimization method. At…