ParLearning 2015 - 4th 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.)
ORGANIZATION
Sutanay Choudhury, Pacific Northwest National Laboratory, USA
Arindam Pal, TCS Innovation Labs, India
Anand Panangadan, University of Southern California, USA
Yinglong Xia, IBM Research, USA
PROGRAM COMMITTEE
Virendra C. Bhavsar, University of New Brunswick, Canada
Danny Bickson, GraphLab Inc., USA
Peter Boncz, Vrije Universiteit, Netherlands
Zhihui Du, Tsinghua University, China
Dinesh Garg, IBM India Research Laboratory, India
Qirong Ho, Infocomm Research, A*STAR, Singapore
Yihua Huang, Nanjing University, China
Renato Porfirio Ishii, Federal University of Mato Grosso do Sul (UFMS), Brazil
Ananth Kalyanaraman, Washington State University, USA
Dionysis Logothetis, Telefonica, Spain
Debnath Mukherjee, TCS Innovation Labs, India
Huansheng Ning, Beihang University, China
Gautam Shroff, TCS Innovation Labs, India
Aniruddha Sinha, TCS Research, India
Neal Xiong, Georgia State University, USA
Jianting Zhang, City College of New York, USA
Wei Zhang, IBM Research, USA