Conference Program


Nov. 13, 2011 (Day 1)
Nov. 14, 2011 (Day 2)
Nov. 15, 2011 (Day 3)
08:30-18:30
Registration
Registration
Registration
09:00-09:10
Opening


09:10-10:30
Tutorial 1: Prof. Wee Sun Lee
Partially Observable Markov Decision Process
Invited Talk 2: Prof. Yann LeCun
Learning Feature Hierarchies
Invited Talk 3 Prof. Peter A. Flach
Scale Matters: on the many uses of calibration in machine learning
10:30-10:50
Coffee Break
Coffee Break
Coffee Break
10:50-12:00
Tutorial 1: Prof. Wee Sun Lee
Session 1 Reinforcement Learning & Online Learning
Session 5 Clustering & Dimension Reduction
12:00-13:00
Lunch
Lunch
Lunch
13:00-14:20
Tutorial 2: Prof. Koji Tsuda
Learning from Graph Data: Graph Kernels, Graph Mining and Recent Developments
Session 2 Classification, Ranking, Structure
Session 6 Learning in Graphs and Networks & Multi-label Classification
14:20-14:30
Break
Break
Break
14:30-15:40
Tutorial 2: Prof. Koji Tsuda
Session 3 Unsupervised Learning
Session 7 Computational Learning Theory & Applications
15:40-16:00
Coffee Break
Coffee Break

16:00-17:20
Invited Talk 1 Prof. Oren Ezioni
Open Information Extraction at Web Scale
Session 4 Supervised and Semi-supervised Learning
Closing
17:20-17:30
Break
Break

17:30-18:30
Poster
Poster

18:30-21:00
Reception
Banquet



Invited Talks and Tutorials


Tutorial 1: Prof. Wee Sun Lee
- Learning from Graph Data: Graph Kernels, Graph Mining and Recent Developments
Nov. 13 (Day 1) 09:10-12:00 Hosted by Prof. Chih-Jen Lin
Tutorial 2: Prof. Koji Tsuda
- Partially Observable Markov Decision Process
Nov. 13 (Day 1) 13:00-15:40 Hosted by Prof. Qiang Yang
Invited talk 1: Prof. Oren Etzioni
- Open Information Extraction at Web Scale
Nov. 13 (Day 1) 16:00-17:20 Hosted by Prof. Chia-Hui Chang

Invited talk 2: Prof. Yann LeCun
- Learning Feature Hierarchies
Nov. 14 (Day 2) 09:10-10:30 Hosted by Prof. Chun-Nan Hsu

Invited talk 3: Prof. Peter A. Flach
- Scale Matters: on the many uses of calibration in machine learning
Nov. 15 (Day 3) 09:10-10:30 Hosted by Prof. Chih-Jen Lin



Session 1-Reinforcement Learning & Online Learning (*: presenter)
Session Chair: Prof. Yuh-Jye Lee
- Improving Policy Gradient Estimates with Independence InformationJervis Pinto*, Alan Fern, Tim Bauer and Martin Erwig- Nonlinear Online Classification Algorithm with Probability MarginMingmin Chi* and Huijun He- Continuous Rapid Action Value EstimatesAdrien Couetoux, Mario Milone, Matyas Brendel*, Hassen Doghmenand Michele Sebag



Session 2-Classification, Ranking, Structure (*: presenter)
Session Chair: Prof. Chun-Nan Hsu
- Learning to Locate Relative OutliersShukai Li and Ivor Tsang*- Microbagging Estimators: An Ensemble Approach to Distance-weighted Classifiers
Blaine Nelson*, Battista Biggio and Pavel Laskov
- Bayesian inference for statistical abduction using Markov chain Monte Carlo
Masakazu Ishihata* and Taisuke Sato
- Support Vector Machines Under Adversarial Label Noise
Battista Biggio, Blaine Nelson* and Pavel Laskov




Session 3-Unsupervised Learning (*: presenter)
Session Chair: Prof. Ivor Tsang
- A General Linear Non-Gaussian State-Space Model: Identifiability, Identification, and Applications
Kun Zhang* and Aapo Hyvarinen- Unsupervised Multiple Kernel LearningJialei Wang, Jinfeng Zhuang and Steven Hoi*- Quadratic Weighted Automata: Spectral Algorithm and Likelihood MaximizationRaphael Bailly*

Session 4-Supervised and Semi-supervised Learning (*: presenter)
Session Chair: Prof. Chih-Jen Lin
- Approximate Model Selection for Large Scale LSSVMLizhong Ding and Shi-Zhong Liao*- Learning low-rank output kernelsFrancesco Dinuzzo*, Kenji Fukumizu- Learning Rules from Incomplete Examples via Implicit Mention ModelsJanardhan Rao Doppa, Shahed Sorower, Mohammad NasrEsfahani, Walker Orr, Thomas G. Dietterich*, Xiaoli Fern, Prasad Tadepalli and Jed Irvine


Session 5-Clustering & Dimension Reduction (*: presenter)
Session Chair: Prof. Masashi Sugiyama
- Mixed-Variate Restricted Boltzmann MachinesTruyen Tran*, Dinh Phung and Svetha Venkatesh- Robust Generation of Dynamical Patterns in Human Motion by a Deep Belief NetsSainbayar Sukhbaatar*, Takaki Makino, Kazuyuki Aihara and Takashi Chikayama- Computationally Efficient Sufficient Dimension Reduction via Squared-Loss Mutual InformationMakoto Yamada*, Gang Niu, Jun Takagi and Masashi Sugiyama


Session 6-Learning in Graphs and Networks & Multi-label Classification (*: presenter)
Session Chair: Prof. Steven Hoi
- Learning Attribute-weighted Voter Model over Social NetworksYuki Yamagishi*, Kazumi Saito, Kouzou Ohara, Masahiro Kimura and Hiroshi Motoda- Multi-label Classification with Error-correcting CodesChun-Sung Ferng and Hsuan-Tien Lin*- Estimating Diffusion Probability Changes for AsIC-SIS Model from Information Diffusion ResultsAkihiro Koide*, Kazumi Saito, Kouzou Ohara, Masahiro Kimura and Hiroshi Motoda- Multi-label Active Learning with Auxiliary LearnerChen-Wei Hung and Hsuan-Tien Lin*

Session 7-Computational Learning Theory & Applications (*: presenter)
Session Chair: Prof. Wee Sun Lee
- Acceleration technique for boosting classification and its application to face detectionMasanori Kawakita*, Ryota Izumi, Jun'ichi Takeuchi, Yi Hu, Tetsuya Takamori and Hirokazu Kameyama- Summarization of Yes/No Questions Using a Feature Function ModelJing He and Decheng Dai*- Mapping kernels defined over countably infinite mapping systems and their applicationsKilho Shin*


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