Program

Contents

Program booklet
Wed, November 16, 2016: Tutorials and Workshops
Thu, November 17, 2016: Main conference
Fri, November 18, 2016: Main conference

Wednesday: Tutorials and Workshops

Time	Workshops and Tutorials
7:40 am	Registration Open - S Block Foyer
8:20 am	Tutorial 1: Mass Estimation: Enabling density-based or distance-based algorithms to do what they cannot do (by Kai Ming Ting) S.1.01	Tutorial 3: Bayesian Nets from the ground up (by Aish Fenton) S.1.02	Workshop 1: Asian Workshop on Reinforcement Learning (AWRL 2016) S.1.03		Workshop 3: ACML Workshop on Learning on Big Data S.1.05
10:00 am	Morning Refreshments
10:20 am	Tutorial 1: Mass Estimation: Enabling density-based or distance-based algorithms to do what they cannot do (by Kai Ming Ting) S.1.01	Tutorial 3: Bayesian Nets from the ground up (by Aish Fenton) S.1.02	Workshop 1: Asian Workshop on Reinforcement Learning (AWRL 2016) S.1.03		Workshop 3: ACML Workshop on Learning on Big Data S.1.05
12:00 pm	Lunch break - Lunch provided
1:00 pm	Industry Keynote Speaker Aish Fenton S.1.04
2:00 pm	Tutorial 2: Recent Advances in Distributed Machine Learning (by Taifeng Wang and Wei Chen) S.1.01	Tutorial 4: Deep Approaches to Semantic Matching for Text (by Yanyan Lan and Jiafeng Guo) S.1.02	Workshop 1: Asian Workshop on Reinforcement Learning (AWRL 2016) S.1.03	Workshop 2: First New Zealand Text Mining Workshop S.1.04	Workshop 3: ACML Workshop on Learning on Big Data S.1.05
3:40 pm	Afternoon Refreshments
4:00 pm	Tutorial 2: Recent Advances in Distributed Machine Learning (by Taifeng Wang and Wei Chen) S.1.01	Tutorial 4: Deep Approaches to Semantic Matching for Text (by Yanyan Lan and Jiafeng Guo) S.1.02	Workshop 1: Asian Workshop on Reinforcement Learning (AWRL 2016) S.1.03	Workshop 2: First New Zealand Text Mining Workshop S.1.04	Workshop 3: ACML Workshop on Learning on Big Data S.1.05
5:40 pm	Afternoon sessions conclude
6:30 pm	Welcome Reception + SC dinner upstairs + Kapa Haka Te Whare Iti - Academy

Thursday: Main conference

Time	Venue S.1.04
7:40 am	Registration Open - S Block Foyer
8:20 am	House-keeping
8:30 am	Keynote Speaker: John Shawe-Taylor Session Chair: Bob Durrant
9:30 am	Morning Refreshments
10:00 am	Session 1: Multilabel Classification, Text & Topic Mining (1) Chaired by: Steven Hoi Non-Linear Smoothed Transductive Network Embedding with Text Information Weizheng Chen, Xia Zhang, Jinpeng Wang, Yan Zhang, Hongfei Yan, Xiaoming Li Long Short-term Memory Network over Rhetorical Structure Theory for Sentence-level Sentiment Analysis Xianghua Fu, Wangwang Liu, Yingying Xu, Chong Yu, Ting Wang Progressive Random k-Labelsets for Cost-Sensitive Multi-Label Classification Hsuan-Tien Lin, Yu-Ping Wu Enhancing Topic Modeling on Short Texts with Crowdsourcing Xiaoyan Yang, Shanshan Ying, Wenzhe Yu, Rong Zhang, Zhenjie Zhang
11:10 am	Poster Session - Lunch
12:00 pm	Invited Speaker: Albert Bifet Session Chair: Geoff Holmes
12:45 pm	Session 2: Kernel Methods Chaired by: Bernhard Pfahringer Multiple Kernel Learning with Data Augmentation Khanh Nguyen, Trung Le, Vu Nguyen, Tu Nguyen, Dinh Phung Cost Sensitive Online Multiple Kernel Classification Doyen Sahoo, Steven Hoi, Peilin Zhao Localized Multiple Kernel Learning---A Convex Approach Yunwen Lei, Alexander Binder, Urun Dogan, Marius Kloft Multi-view Kernel Completion Sahely Bhadra, Samuel Kaski, Juho Rousu Linearized Alternating Direction Method of Multipliers for Constrained Nonconvex Regularized Optimization Linbo Qiao, Bofeng Zhang, Jinshu Su, Xicheng Lu
2:10 pm	Afternoon Refreshments Poster winner announced
2:30 pm	Session 3: Learning Theory Chaired by: John Shawe-Taylor Random Fourier Features For Operator-Valued Kernels Romain Brault, Markus Heinonen, Florence d'Alché Buc Secure Approximation Guarantee for Cryptographically Private Empirical Risk Minimization Toshiyuki Takada, Hiroyuki Hanada, Yoshiji Yamada, Jun Sakuma, Ichiro Takeuchi Learning from Survey Training Samples: Rate Bounds for Horvitz-Thompson Risk Minimizers Stephan Clemencon, Patrice Bertail, Guillaume Papa Learnability of Non-I.I.D. Wei Gao, Xin-Yi Niu, Zhi-Hua Zhou
3:40 pm	10 min break
3:50 pm	Session 4: Multilabel Classification, Text & Topic Mining (2) Chaired by: Wray Buntine Modelling Symbolic Music: Beyond the Piano Roll Christian Walder Improving Distributed Word Representation and Topic Model by Word-Topic Mixture Model Xianghua Fu, Ting Wang, Jing Li, Chong Yu, Wangwang Liu Collaborative Topic Regression for Online Recommender Systems: An Online and Bayesian Approach Chenghao Liu, Tao Jin, Steven Hoi, Peilin Zhao, Jianling Sun Fast Collaborative Filtering from Implicit Feedback with Provable Guarantees Sayantan Dasgupta
5:20 pm	Bus to Hobbiton
6:15 pm	Hobbiton tour starts
	ACML 2016 Banquet Dinner (return bus will pick up attendees at 9:30 pm)

Friday: Main conference

Time	Venue S.1.04
8:20 am	Registration Open - S Block Foyer
8:30 am	Keynote Speaker: Vincent Tseng Session Chair: Albert Bifet
9:30 am	Morning Refreshments
10:00 am	Session 5: Best Papers Chaired by: Masashi Sugiyama Best Paper: Unifying Topic, Sentiment & Preference in an HDP-Based Rating Regression Model for Online Reviews Zheng Chen, Yong Zhang , Yue Shang , Xiaohua Hu Best Student Paper: Simulation and Calibration of a Fully Bayesian Marked Multidimensional Hawkes Process with Dissimilar Decays Kar Wai Lim, Young Lee, Leif Hanlen, Hongbiao Zhao Best Paper Runner-up: A Bayesian Nonparametric Approach for Multi-label Classification Vu Nguyen, Sunil Gupta, Santu Rana, Cheng Li, Svetha Venkatesh Best Student Paper Runner-up: Hierarchical Probabilistic Matrix Factorization with Network Topology for Multi-relational Social Network Haoli Bai, Zenglin Xu, Bin Liu, Yingming Li
11:10 am	Poster Session - Lunch
12:00 pm	Invited Speaker: Tie-Yan Liu Session Chair: Bernhard Pfahringer
12:45 pm	Session 6: Manifold & Metric Learning Chaired by: Stephen Marsland A Unified Probabilistic Framework for Robust Manifold Learning and Embedding Qi Mao, Li Wang, Ivor W. Tsang Non-redundant Multiple Clustering by Nonnegative Matrix Factorization Sen Yang, Lijun Zhang Learning Feature Aware Metric Han-Jia Ye, De-Chuan Zhan, Xue-Min Si, Yuan Jiang Multitask Principal Component Analysis Ikko Yamane, Florian Yger, Maxime Berar, Masashi Sugiyama Learning Distance Metrics for Multi-Label Classification Henry Gouk, Bernhard Pfahringer, Michael Cree
2:10 pm	Afternoon Refreshments Poster winner announced
2:30 pm	Session 7: Deep Learning Approaches Chaired by: Eibe Frank Bank of Weight Filters for Deep CNNs Suresh Kirthi Kumaraswamy, PS Sastry, Kalpathi Ramakrishnan Deep Gate Recurrent Neural Network Yuan Gao, Dorota Glowacka Collaborative Recurrent Neural Networks for Dynamic Recommender Systems Young-Jun Ko, Lucas Maystre, Matthias Grossglauser Echo State Hoeffding Tree Learning Diego Marron, Jesse Read, Albert Bifet, Talel Abdessalem, Eduard Ayguade, José Herrero
3:40 pm	10 min break
3:50 pm	Session 8: Feature Selection & Dimensionality Reduction Chaired by: Bob Durrant Proper Inner Product with Mean Displacement for Gaussian Noise Invariant ICA Liyan Song, Haiping Lu An Efficient Approach for Multi-Sentence Compression Elahe Shafiei, Mohammad Ebrahimi, Raymond K. Wong, Fang Chen Geometry-aware stationary subspace analysis Inbal Horev, Florian Yger, Masashi Sugiyama EcoICA: Skewness-based ICA via Eigenvectors of Cumulant Operator Liyan Song, Haiping Lu
5:00 pm	Conference close