Distributed Machine Learning
Papers from the 2017 AAAI Workshop
Tie-Yan Liu, James Kwok, Chih-Jen Lin, Organizers
AAAI Technical Report WS-17-08
This technical report was published as part of The Workshops of the Thirty-First AAAI Conference on Artificial Intelligence: Technical Reports WS-17-01 – WS-17-15 by The AAAI Press, Palo Alto, California
Contents
Distributed Weighted Parameter Averaging for SVM Training on Big Data
Ayan Das, Raghuveer Chanda, Smriti Agrawal, Sourangshu Bhattacharya
Data Driven Resource Allocation for Distributed Learning
Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Maria Florina Balcan, Alex Smola
Distributed Hessian-Free Optimization for Deep Neural Network
Xi He, Dheevatssa Mudigere, Mikhail Smelyanskiy, Martin Takac
Distributed Inexact Damped Newton Method: Data Partitioning and Work-Balancing
Chenxin Ma, Martin Takac
Scalable Score Computation for Learning Multinomial Bayesian Networks over Distributed Data
Praveen Rao, Anas Katib, Kobus Barnard, Charles Kamhoua, Kevin Kwiat, Laurent Njilla
Parallel Chromatic MCMC with Spatial Partitioning
Jun Song, David Moore
Scalable Classifiers with ADMM and Transpose Reduction
Gavin Taylor, Zheng Xu, Tom Goldstein
Parallel Higher Order Alternating Least Square for Tensor Recommender System
Romain Warlop, Alessandro Lazaric, Jérémie Mary
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