ParLearning 2017 - 6th International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics

Scaling up machine-learning (ML), data mining (DM) and reasoning algorithms from Artificial Intelligence (AI) for massive datasets is a major technical challenge in the times of "Big Data". The past ten years has seen the rise of multi-core and GPU based computing. In distributed computing, several frameworks such as Mahout, GraphLab and Spark continue to appear to facilitate scaling up ML/DM/AI algorithms using higher levels of abstraction. We invite novel works that advance the trio-fields of ML/DM/AI through development of scalable algorithms or computing frameworks. Ideal submissions would be characterized as scaling up X on Y, where potential choices for X and Y are provided below.
Scaling up
recommender systems
gradient descent algorithms
deep learning
sampling/sketching techniques
clustering (agglomerative techniques, graph clustering, clustering heterogeneous data)
classification (SVM and other classifiers)
SVD
probabilistic inference (bayesian networks)
logical reasoning
graph algorithms and graph mining
On
Parallel architectures/frameworks (OpenMP, OpenCL, Intel TBB)
Distributed systems/frameworks (GraphLab, Hadoop, MPI, Spark etc.)