Program

The proceedings are now available here as part of the JMLR Workshop and Conference Proceedings series.

Instructions to presenters can be found here.
The program booklet showing the location of ACML events is available here.

Nov. 4, 2012 (Day 1)

 Tutorial TrackWorkshop Track
08:30-10:10Tutorial 1: Bandit Games
Sebastien Bubeck
Workshop 1: LAWS'12
10:10-10:30Coffee BreakCoffee Break
10:30-11:20Tutorial 1 (continued)LAWS'12 (continued)
11:20-12:30Tutorial 2: Domain Adaptation in Real World Applications
Fei Sha, Ivor W. Tsang, Sinno Pan
LAWS'12 (continued)
12:30-13:30LunchLunch
13:30-15:10Tutorial 2 (continued)Workshop 2: FDMA'12
15:10-15:30Coffee BreakCoffee Break
15:30-17:00Tutorial 3: Probabilistic Modeling of Ranking
Jose A. Lozano, Ekhine Irurozki
FDMA'12
17:00-17:20Coffee Break 
17:20-18:30Tutorial 3 (continued)FDMA'12 (continued)
18:30-21:00Reception

Nov. 5, 2012 (Day 2) and Nov. 6, 2012 (Day 3)

 Nov. 5, 2012
(Day 2)
Nov. 6, 2012
(Day 3)
08:30-09:00Opening SessionBest Paper Session
09:00-10:10Invited Talk 1:
James Rehg (Georgia Tech)
Invited Talk 3:
Bob Williamson (Australian National University and NICTA)
10:10-10:30Coffee BreakCoffee Break
10:30-12:00Paper Session 1Paper Session 4
12:00-14:00Lunch + Poster SessionLunch + Poster Session
14:00-15:10Invited Talk 2:
Dale Schuurmans (University of Alberta)
Paper Session 5
15:10-15:30Coffee BreakCoffee Break
15:30-17:00Paper Session 2Paper Session 6
17:00-17:20Coffee BreakCLOSING
17:20-18:30Paper Session 3 
18:30-21:00Banquet 

Oral Presentation Session 1

10:30AM – 12:00PM (Monday, November 5)

TOPIC: Unsupervised Learning and Deep Learning

Recovering Networks from Distance Data (20 min) Best Student Paper Award
Sandhya Prabhakaran (University of Basel), Karin Metzner (UniversitätsSpital Zürich), Alexander Böhm (LOEWE-Zentrum für Synthetische Mikrobiologie), Volker Roth (University of Basel)

Statistical Models for Exploring Individual Email Communication Behavior (20 min)
Nicholas Navaroli (University of California, Irvine), Christopher DuBois (University of California, Irvine), Padhraic Smyth (University of California, Irvine)

Multiresolution Mixture Modelling using Merging of Mixture Components (10 min)
Prem Adhikari (Aalto University ), Jaakko Hollmén (Aalto Univeristy),

Cumulative Restricted Boltzmann Machines for Ordinal Matrix Data Analysis (10 min)
Truyen Tran (Curtin University), Dinh Phung (Deakin University), Svetha Venkatesh (Deakin University)

Learning Latent Variable Models by Pairwise Cluster Comparison (10 min)
Nuaman Asbeh (Ben-Gurion University of the N), Boaz Lerner (Ben-Gurion University)

A Note on Metric Properties for Some Divergence Measures: The Gaussian Case (10 min)
Karim Abou-Moustafa* (McGill University)* Frank Ferrie (McGill University)


Oral Presentation Session 2

15:30PM – 17:00PM (Monday, November 5)

TOPIC: Feature Selection and Dimension Reduction

Topographic Analysis of Correlated Components (20 min)
Hiroaki Sasaki (Univ. of Electro-Communication), Michael Gutmann ( University of Helsinki ), Hayaru Shouno (Univ. of Electro-Communication), Aapo Hyvarinen (Helsinki Institute for Information Technology)

Sparse Additive Matrix Factorization for Robust PCA and Its Generalization (20 min)
Shinichi Nakajima (Nikon Corporation), Masashi Sugiyama (Tokyo Institute of Technology), S. Derin Babacan (Illinois University)

Spatial Locality-Aware Sparse Coding and Dictionary Learning (10 min)
Jiang Wang (Northwestern University), Junsong Yuan (Nanyang Technological University), Zhuoyuan Chen (Northwestern University), Ying Wu (Northwestern University)

Local Kernel Density Ratio-Based Feature Selection for Outlier Detection (10 min)
Fatemeh Azmandian (Northeastern University), Jennifer Dy (Northeastern University), Javed Aslam (Northeastern University) David Kaeli (Northeastern University)

Supervised Dimension Reduction with Topic Models (10 min)
Khoat Than (JAIST), Tu Bao Ho (Japan), Duy Khuong Nguyen (JAIST), Ngoc Khanh Pham (JAIST)

Key Instance Detection in Multi-Instance Learning (10 min)
Guoqing Liu (NTU), Jianxin Wu (NTU), Zhi-Hua Zhou (Nanjing University)


Oral Presentation Session 3

17:20PM – 18:30 PM (Monday, November 5)

TOPIC: Learning in Graphs, Networks, and Structures

A Convex-Concave Relaxation Procedure Based Subgraph Matching Algorithm (20 min)
Zhiyong Liu (Chinese Academy of Sciences), Hong Qiao (Chinese Academy of Sciences)

Learning and Model-Checking Networks of I/O Automata (10 min)
Hua Mao (Aalborg University), Manfred Jaeger (Aaalborg University )

Learning Temporal Associative Rules on Symbolic Time Sequences (10 min)
Mathieu Guillame-Bert (INRIA), James Crowley (INRIA)

Improved sequence classification using adaptive segmental sequence alignment (10 min)
Shahriar Shariat (Rutgers), Vladimir Pavlovic (Rutgers)

A Coupled Indian Buffet Process Model for Collaborative Filtering (10 min)
Sotirios Chatzis (Cyprus University Technology)

Two-way Parallel Class Expression Learning (10 min)
An C. Tran (Massey University, New Zealand), Jens Dietrich (Massey University, New Zealand), Hans W. Guesgen (Massey University, New Zealand), Stephen Marsland (Massey University, New Zealand)


BEST PAPER SESSION

8:30AM – 09:00AM (Tuesday, November 6)

Variational Bayesian Matching
Arto Klami (Aalto University)


Oral Presentation Session 4

10:30AM – 12:00PM (Tuesday, November 6)

TOPIC: Classification and Ranking

Multi-Stage Classifier Design (20 min)
Kirill Trapeznikov (Boston University), Venkatesh Saligrama (Boston University), David Castanon (Boston University)

QBoost: Large Scale Classifier Training with Adiabatic Quantum Optimization (20 min)
Hartmut Neven (Google), Vasil Denchev (Purdue University), Geordie Rose (D-Wave Systems, Inc.), William Macready (D-Wave Systems, Inc.)

Practical Large Scale Classification with Additive Kernels (10 min)
Hao Yang (NTU), Jianxin Wu (NTU)

Max Margin Ratio Machine (10 min)
Suicheng Gu (Temple University), Yuhong Guo (Temple University)

A Ranking-based KNN Approach for Multi-Label Classification (10 min)
Tsung-Hsien Chiang (National Taiwan University), Hung-Yi Lo (Academia Sinica), Shou-de Lin (National Taiwan University)

Learning From Ordered Sets and Applications in Collaborative Ranking (10 min)
Truyen Tran (Curtin University), Dinh Phung (Deakin University), Svetha Venkatesh (Deakin University)


Oral Presentation Session 5

14:00PM – 15:10PM (Tuesday, November 6)

TOPIC: Supervised and Semi-supervised Learning

On Using Nearly-Independent Feature Families for High Precision and Confidence (20 min)
OMID Madani (Google), Manfred Georg (Google), David Ross (Google)

More Is Better: Large Scale Partially-supervised Sentiment Classification (10 min)
Yoav Haimovitch (Technion), Koby Crammer (Technion), Shie Mannor (Technion)

Active Learning with Hinted Support Vector Machine (10 min)
Chun-Liang Li (National Taiwan University), Chun-Sung Ferng (NTU CSIE), Hsuan-Tien Lin (National Taiwan University)

Multi-view Positive and Unlabeled Learning (10 min)
Joey Tianyi ZHOU (Nanyang Technological University), Sinno Jialin Pan (A*STAR), Qi Mao (Nanyang Technological University), Ivor Wai-Hung TSANG (Nanyang Technological University)

Frustratingly Simplified Deployment in WLAN Localization by Learning from Route Annotation (10 min)
Ryoma Kawajiri (The University of Tokyo), Masamichi Shimosaka (The University of Tokyo), Rui Fukui (The University of Tokyo), Tomomasa Sato (The University of Tokyo)


Oral Presentation Session 6

15:30PM – 17:00PM (Tuesday, November 6)

TOPIC: Learning Theory, Reinforcement and Online Learning

Conditional Validity of Inductive Conformal Predictors (20 min)
Vladimir Vovk (Royal Holloway)

Multi-objective Monte-Carlo Tree Search (20 min)
Weijia WANG (LRI), Michele Sebag (CNRS)

A Stochastic Bandit Algorithm for Scratch Games (20 min)
Raphaël Féraud (Orange Labs), Tanguy Urvoy (Orange Labs)

AIC and BIC based approaches for SVM parameter value estimation with RBF kernels (10min)
Sergey Demyanov (The University of Melbourne), James Bailey (The University of Melbourne) ,Ramamohanarao Kotagiri (The University of Melbourne), Christopher Leckie (The University of Melbourne)

Online Rank Aggregation (10 min)
Shota Yasutake (Kyushu University), Kohei Hatano (Kyusyu University ), Eiji Takimoto (Kyushu University), Masayuki Takeda (Kyushu University)

Online Learning of a Dirichlet Process Mixture of Generalized Dirichlet Distributions for Simultaneous Clustering and Localized Feature Selection (10 min)
Wentao Fan (Concordia University), Nizar Bouguila (Concordia University)


Poster Sessions

Poster Presentation Session 1

Monday 5 November 12:00PM – 14:00 PM (Library, SMU)

P1 Recovering Networks from Distance Data (Best Student Paper Award)
Sandhya Prabhakaran (University of Basel), Karin Metzner (UniversitätsSpital Zürich), Alexander Boehm (LOEWE-Zentrum fuer Synthetische Mikrobiologie), Volker Roth (University of Basel)

P2 Statistical Models for Exploring Individual Email Communication Behavior
Nicholas Navaroli (University of California, Irvine), Christopher DuBois (University of California, Irvine), Padhraic Smyth (University of California, Irvine)

P3 Multiresolution Mixture Modelling using Merging of Mixture Components
Prem Adhikari (Aalto University ), Jaakko Hollmén (Aalto Univeristy)

P4 Cumulative Restricted Boltzmann Machines for Ordinal Matrix Data Analysis
Truyen Tran (Curtin University), Dinh Phung (Deakin University), Svetha Venkatesh (Deakin University)

P5 Learning Latent Variable Models by Pairwise Cluster Comparison
Nuaman Asbeh (Ben-Gurion University of the N), Boaz Lerner (Ben-Gurion University)

P6 A Note on Metric Properties for Some Divergence Measures: The Gaussian Case
Karim Abou-Moustafa (McGill University), Frank Ferrie (McGill University)'''

P7 Topographic Analysis of Correlated Components
Hiroaki Sasaki (Univ. of Electro-Communication), Michael Gutmann ( University of Helsinki ), Hayaru Shouno, Aapo Hyvarinen (Helsinki Institute for Information Technology)

P8 Sparse Additive Matrix Factorization for Robust PCA and Its Generalization
Shinichi Nakajima (Nikon Corporation), Masashi Sugiyama (Tokyo Institute of Technology), S. Derin Babacan (Illinois University)

P9 Spatial Locality-Aware Sparse Coding and Dictionary Learning
Jiang Wang (Northwestern University), Junsong Yuan (Nanyang Technological University), Zhuoyuan Chen (Northwestern University), Ying Wu (Northwestern University)

P10 Local Kernel Density Ratio-Based Feature Selection for Outlier Detection
Fatemeh Azmandian (Northeastern University), Jennifer Dy (Northeastern University), Javed Aslam (Northeastern University), David Kaeli (Northeastern University)

P11 Supervised Dimension Reduction with Topic Models
Khoat Than (JAIST), Tu Bao Ho (Japan), Duy Khuong Nguyen (JAIST), Ngoc Khanh Pham (JAIST)'''

P12 Key Instance Detection in Multi-Instance Learning
Guoqing Liu (NTU), Jianxin Wu (NTU), Zhi-Hua Zhou (Nanjing University)

P13 A Convex-Concave Relaxation Procedure Based Subgraph Matching Algorithm
Zhiyong Liu (Chinese Academy of Sciences), Hong Qiao (Chinese Academy of Sciences)

P14 Learning and Model-Checking Networks of I/O Automata
Hua Mao (Aalborg University), Manfred Jaeger (Aaalborg University )

P15 Learning Temporal Associative Rules on Symbolic Time Sequences
Mathieu Guillame-Bert (INRIA), James Crowley (INRIA)

P16 Improved sequence classification using adaptive segmental sequence alignment
Shahriar Shariat (Rutgers), Vladimir Pavlovic (Rutgers)

P17 A Coupled Indian Buffet Process Model for Collaborative Filtering
Sotirios Chatzis (Cyprus University Technology)

P18 Two-way Parallel Class Expression Learning
An C. Tran (Massey University, New Zealand), Jens Dietrich (Massey University, New Zealand), Hans W. Guesgen (Massey University, New Zealand), Stephen Marsland (Massey University, New Zealand)

P19 Data Thresholding for Large-scale Sparse Linear Classification
Gia Vinh Anh Pham and Laurent El Ghaoui (Department of EECS, UC Berkeley)

P20 Exploiting Rank-Learning Models to Predict the Diffusion of Preferences on Social Networks
Chin-Hua Tsai, Hung-Yi Lo, Shou-De Lin (National Taiwan University)

P21 Efficient AUC Maximization by Approximate Reduction of Ranking SVMs
Daiki Suehiro, Daiki Suehiro, Daiki Suehiro (Department of Informatics, Kyushu University)


Poster Presentation Session 2

Tuesday 6 November 12:00PM – 14:00 PM (Library, SMU)

P22 Variational Bayesian Matching
Arto Klami (Aalto University)

P23 Multi-Stage Classifier Design
Kirill Trapeznikov (Boston University), Venkatesh Saligrama (Boston University), David Castanon (Boston University)

P24 QBoost: Large Scale Classifier Training with Adiabatic Quantum Optimization
Hartmut Neven (Google), Vasil Denchev (Purdue University), Geordie Rose (D-Wave Systems, Inc.), William Macready (D-Wave Systems, Inc.)

P25 Practical Large Scale Classification with Additive Kernels
Hao Yang (NTU), Jianxin Wu (NTU)

P26 Max Margin Ratio Machine
Suicheng Gu (Temple University), Yuhong Guo (Temple University)

P27 A Ranking-based KNN Approach for Multi-Label Classification
Tsung-Hsien Chiang (National Taiwan University), Hung-Yi Lo (Academia Sinica), Shou-de Lin (National Taiwan University)

P28 Learning From Ordered Sets and Applications in Collaborative Ranking
Truyen Tran (Curtin University), Dinh Phung (Deakin University), Svetha Venkatesh (Deakin University)

P29 On Using Nearly-Independent Feature Families for High Precision and Confidence
OMID Madani (Google), Manfred Georg (Google), David Ross (Google)

P30 More Is Better: Large Scale Partially-supervised Sentiment Classification
Yoav Haimovitch (Technion), Koby Crammer (Technion), Shie Mannor (Technion)

P31 Active Learning with Hinted Support Vector Machine
Chun-Liang Li (National Taiwan University), Chun-Sung Ferng (NTU CSIE), Hsuan-Tien Lin (National Taiwan University)

P32 Multi-view Positive and Unlabeled Learning
Joey Tianyi ZHOU (Nanyang Technological University), Sinno Jialin Pan (A*STAR), Qi Mao (Nanyang Technological University), Ivor Wai-Hung TSANG (Nanyang Technological University)

P33 Frustratingly Simplified Deployment in WLAN Localization by Learning from Route Annotation
Ryoma Kawajiri (The University of Tokyo), Masamichi Shimosaka (The University of Tokyo), Rui Fukui (The University of Tokyo), Tomomasa Sato (The University of Tokyo)

P34 Conditional validity of inductive conformal predictors
Vladimir Vovk (Royal Holloway)

P35 Multi-objective Monte-Carlo Tree Search
Weijia WANG (LRI), Michele Sebag (CNRS)

P36 A Stochastic Bandit Algorithm for Scratch Games
Raphaël Féraud (Orange Labs), Tanguy Urvoy (Orange Labs)

P37 AIC and BIC based approaches for SVM parameter value estimation with RBF kernels
Sergey Demyanov (The University of Melbourne), James Bailey (The University of Melbourne), Ramamohanarao Kotagiri (The University of Melbourne), Christopher Leckie (The University of Melbourne)

P38 Online Rank Aggregation
Shota Yasutake (Kyushu University), Kohei Hatano (Kyusyu University ), Eiji Takimoto (Kyushu University), Masayuki Takeda (Kyushu University)

P39 Online Learning of a Dirichlet Process Mixture of Generalized Dirichlet Distributions for Simultaneous Clustering and Localized Feature Selection
Wentao Fan (Concordia University), Nizar Bouguila (Concordia University)

P40 Distribution Changes Based Financial Time Series Forecasting using Data Mining and Machine Learning Methods
Goce Ristanoski, James Bailey (The University of Melbourne, Melbourne, Australia)

P41 Learning Interpretable Models from Distributed Data
Artur Andrzejak, Felix Langner, Silvestre Zabala, See-Kiong Ng (Ruprecht-Karls-University of Heidelberg)

P42 A generalized dependent normalized measure framework for Bayesian nonparametric modelling
Changyou Chen (The Australian National University)