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Poster
Distributed Flexible Nonlinear Tensor Factorization
Shandian Zhe · Kai Zhang · Pengyuan Wang · Kuang-chih Lee · Zenglin Xu · Yuan Qi · Zoubin Ghahramani

Mon Dec 05 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #140
in Monday Posters »

Tensor factorization is a powerful tool to analyse multi-way data. Recently proposed nonlinear factorization methods, although capable of capturing complex relationships, are computationally quite expensive and may suffer a severe learning bias in case of extreme data sparsity. Therefore, we propose a distributed, flexible nonlinear tensor factorization model, which avoids the expensive computations and structural restrictions of the Kronecker-product in the existing TGP formulations, allowing an arbitrary subset of tensor entries to be selected for training. Meanwhile, we derive a tractable and tight variational evidence lower bound (ELBO) that enables highly decoupled, parallel computations and high-quality inference. Based on the new bound, we develop a distributed, key-value-free inference algorithm in the MapReduce framework, which can fully exploit the memory cache mechanism in fast MapReduce systems such as Spark. Experiments demonstrate the advantages of our method over several state-of-the-art approaches, in terms of both predictive performance and computational efficiency.

Keywords: Matrix Factorization · Bayesian Nonparametrics · Large Scale Learning and Big Data · (Other) Bayesian Inference · Gaussian Processes

Author Information

Shandian Zhe (Purdue University)
Kai Zhang (NEC Labs America)
Pengyuan Wang (Yahoo! Research)
Kuang-chih Lee (Alibaba Inc.)
Zenglin Xu (Harbin Institute of Technology Shenzhen)
Yuan Qi (Ant financial service group)
Zoubin Ghahramani (Uber and University of Cambridge)

Zoubin Ghahramani is Professor of Information Engineering at the University of Cambridge, where he leads the Machine Learning Group. He studied computer science and cognitive science at the University of Pennsylvania, obtained his PhD from MIT in 1995, and was a postdoctoral fellow at the University of Toronto. His academic career includes concurrent appointments as one of the founding members of the Gatsby Computational Neuroscience Unit in London, and as a faculty member of CMU's Machine Learning Department for over 10 years. His current research interests include statistical machine learning, Bayesian nonparametrics, scalable inference, probabilistic programming, and building an automatic statistician. He has held a number of leadership roles as programme and general chair of the leading international conferences in machine learning including: AISTATS (2005), ICML (2007, 2011), and NIPS (2013, 2014). In 2015 he was elected a Fellow of the Royal Society.

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