Schedule

Please note that the schedule is still tentative, and some slight adjustments might be made before the workshop.

8:30	[Opening]
8:40-9:30	[Invited Talk] Alex Smola, Why your machine learning algorithm is slow
9:30-10:30	[Contributed talk] Xi He, Dheevatsa Mudigere, Mikhail Smelyanskiy, Martin Takác, Distributed Hessian-Free Optimization for Deep Neural Network
	[Contributed talk] Ayan Das, Raghuveer Chanda, Smriti Agrawal, Sourangshu Bhattacharya, Distributed Weighted Parameter Averaging for SVM Training on Big Data
	[Contributed talk] Soham De, Thomas Goldstein, CentralVR: A Framework for Variance-Reduced Distributed Optimization
10:30-11:00	Coffee break
11:00-11:50	[Invited Talk] Joseph E. Gonzalez, RISE to the Next Challenges of AI Systems
11:50-12:30	[Contributed talk] Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Maria-Florina Balcan, Alex Smola, Data Driven Resource Allocation for Distributed Learning
	[Contributed talk] Chenxin Ma, Martin Takác, Distributed Inexact Damped Newton Method: Data Partitioning and Work-Balancing
12:30-14:00	Lunch
14:00-14:50	[Invited talk] Xiangrui Meng, Implementing large-scale matrix factorization on Apache Spark
14:50-15:30	[Contributed talk] Romain Warlop, Alessandro Lazaric, Jérémie Mary, Parallel Higher Order Alternating Least Square for Tensor Recommender System
	[Contributed talk] Jun Song, David A. Moore, Parallel Chromatic MCMC with Spatial Partitioning
15:30-16:00	Coffee break
16:00-16:50	[Invited talk] Christopher Re, Tuning large-scale systems: surprising system-algorithm interactions (presented by Dr. Ioannis Mitliagkas)
16:50-17:30	[Contributed talk] Gavin Taylor, Zheng Xu, Thomas Goldstein, Scalable Classifiers with ADMM and Transpose Reduction
	[Contributed talk] Praveen Rao, Anas Katib, Kobus Barnard, Charles Kamhoua, Kevin Kwiat, Laurent L. Njilla, Scalable Score Computation for Learning Multinomial Bayesian Networks over Distributed Data
17:30	Closing