Session 1, Tuesday morning: Bayesian Learning 1

Three approaches for making the Naive Bayes classifier discrimination-free
Sicco Verwer, Toon Calders

Nonparametric Bayesian Clustering Ensembles
Pu Wang, Carlotta Domeniconi, Kathryn Laskey

Large Margin Learning of Bayesian Classifiers based on Gaussian Mixture Models
Franz Pernkopf, Michael Wohlmayr

Variational Bayesian mixture of robust CCA models
Jaakko Viinikanoja, Arto Klami, Samuel Kaski

Graphical Multi-Way Models
Ilkka Huopaniemi, Tommi Suvitaival, Matej Oresic, Samuel Kaski

Session 2, Tuesday morning: Rules and Patterns 1

Mining Top-k Frequent Itemsets Through Progressive Sampling
Andrea Pietracaprina, Matteo Riondato, Eli Upfal, Fabio Vandin

Integrating Constraint Programming and Itemset Mining
Siegfried Nijssen, Tias Guns

Using Background Knowledge to Rank Itemsets
Nikolaj Tatti, Michael Mampaey

NDPMine: Efficiently Mining Discriminative Numerical Features for Pattern-Based Classification
Hyungsul Kim, Sangkyum Kim, Tim Weninger, Jiawei Han, Tarek Abdelzaher

Adverse Drug Reaction Mining in Pharmacovigilance data using Formal Concept Analysis
Jean Villerd, Yannick Toussaint, Agnès Lillo-Le Louët

Session 3, Tuesday morning: Ensemble Learning

Leveraging Bagging for Evolving Data Streams
Albert Bifet, Geoff Holmes, Bernhard Pfahringer

Learning with Ensemble of Randomized Trees: New Insights
Vincent Pisetta, Pierre-Emmanuel Jouve, Djamel Zighed

Learning with Randomized Majority Votes
Alexandre Lacasse, Franois Laviolette, Mario Marchand, Francis Turgeon-Boutin

Bagging for biclustering: application to microarray data
Blaise Hanczar, Mohamed Nadif

Recognition of instrument timbres in real polytimbral audio recordings
Elzbieta Kubera, Alicja Wieczorkowska, Zbigniew Ras, Magdalena Skrzypiec

Session 4, Tuesday early afternoon: Kernel Methods

A Unifying View of Multiple Kernel Learning
Marius Kloft, Ulrich Rckert, Peter Bartlett

Constructing Nonlinear Discriminants from Multiple Data Views
Tom Diethe, David Roi Hardoon, John Shawe-Taylor

Learning Vector Fields with Spectral Filtering
Lorenzo Rosasco, Luca Baldassarre, Annalisa Barla, Alessando Verri

Example-dependent Basis Vector Selection for Kernel-based Classifiers
Antti Ukkonen, Marta Arias

Characteristic Kernels on Structured Domains Excel in Robotics and Human Action Recognition
Somayeh Danafar, Arthur Gretton, Jürgen Schmidhuber

Session 5, Tuesday early afternoon: Web Mining and Collaborative Tagging

Induction of Concepts in Web Ontologies through Terminological Decision Trees
Nicola Fanizzi, Claudia d'Amato, Floriana Esposito

Demand-Driven Tag Recommendation
Guilherme Menezes, Jussara Almeida, Fabiano Belm, Marcos Gonalves,
Ansio Lacerda, Edleno Moura, Gisele Pappa, Adriano Veloso, Nivio
Ziviani

Learning to Tag From Noisy Labels
Edith Law, Burr Settles, Tom Mitchell

CREST: Efficient Confident Search in Large Review Corpora
Theodoros Lappas, Dimitrios Gunopulos

Large Scale Image Annotation: Learning to Rank with Joint Word-Image Embeddings
Jason Weston, Samy Bengio, Nicolas Usunier

Session 6, Tuesday early afternoon: Topic Models and Dimensionality Reduction

A Segmented Topic Model based on the Two-parameter Poisson-Dirichlet process
Lan Du, Wray Buntine, Huidong Jin

Topic Models Conditioned on Relations
Mirwaes Wahabzada, Zhao Xu, Kristian Kersting

Learning the Ephemeral Through the Persistent
Maksims Ovsjanikovs, Ye Chen

Learning an Affine Transformation for Non-linear Dimensionality Reduction
Pooyan Khajehpour Tadavani, Ali Ghodsi

Sparse Unsupervised Dimensionality Reduction Algorithms
Wenjuan Dou, Guang Dai, congfu Xu, Zhihua Zhang

Session 7, Tuesday late afternoon: Reinforcement Learning 1

Smarter Sampling for Bayesian Reinforcement Learning
Pablo Castro, Doina Precup

Incorporating Domain Models into Bayesian Optimization for
Reinforcement Learning
Aaron Wilson, Alan Fern, Prasad Tadepalli

Gaussian processes for sample efficient reinforcement learning with RMAX-like exploration
Tobias Jung, Peter Stone

Exploration in Relational Worlds
Tobias Lang, Marc Toussaint, Kristian Kersting

Evolutionary Dynamics of Regret Minimization
Gerrit Jan van Ahee, Tomas Klos, Karl Tuyls

Session 8, Tuesday late afternoon: Multi-task, Multi-domain and Large-scale Learning

Shift-invariant Grouped Multi-task Learning for Gaussian Processes
Yuyang Wang, Roni Khardon, Pavlos Protopapas

A Framework to Choose Amongst Models and Datasets for Transfer Learning
Erheng Zhong, Wei Fan, Qiang Yang, Olivier Verscheure, Jiangtao Ren

Predictive Distribution Matching SVM for Multi-Domain Learning
Chun-Wei Seah, Ivor W. Tsang, Yew-Soon Ong, Kee-Khoon Lee

Porting Decision Tree Algorithms to Multicore using FastFlow
Marco Aldinucci, Salvatore Ruggieri, Massimo Torquati

Large Scale Support Vector Learning with Structural Kernels
Aliaksei Severyn, Alessandro Moschitti

Session 9, Tuesday late afternoon: Link Prediction and Graph Classification

Learning Algorithms for Link Prediction based on Chance Constraints
Janardhan Rao Doppa, Jun Yu, Prasad Tadepalli, Lise Getoor

Graph Regularized Transductive Classification on Heterogeneous Information Networks
Ming Ji, Yizhou Sun, Marina Danilevsky, Jiawei Han

Fast and Scalable Algorithms for Semi-supervised Link Prediction on Static and Dynamic Graphs
Rudy Raymond, Hisashi Kashima

Directed Graph Learning via Co-linkage Similarity and Biased Random Walk
Hua Wang, Heng Heng, Chris Ding

Predicting labels for dyadic data
Aditya Menon, Charles Elkan

Session 10, Wednesday morning: Sparse Learning

Classification with Sums of Separable Functions
Jochen Garcke

Proximal Methods for Structured Sparsity Regularization
Lorenzo Rosasco, sofia mosci, matteo santoro, silvia villa, alessando verri

Learning Sparse Gaussian Markov Network Structure using a Greedy Coordinate Ascent Approach
Katya Scheinberg, Irina Rish

Efficient and Numerically Stable Sparse Learning
Sihong Xie, Wei Fan, Olivier Verscheure, Jiangtao Ren

k-Version-Space Multi-Class Classification based on k-Consistency Tests
Evgueni Smirnov, Georgi Nalbantov, Nikolay Nikolaev

Session 11, Wednesday morning: Social Networks

Selecting Information Diffusion Models over Social Networks for
Behavioral Analysis
Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda

Finding Critical Nodes for Inhibiting Diffusion of Complex Contagions
in Social Networks
Christopher Kuhlman, Anil Kumar, Madhav Marathe, Daniel Rosenkrantz, S. Ravi

xSocial: Analysis of Large Multi-Modal Social Networks Patterns and a Generator
Nan Du, Hao Wang, Christos Faloutsos

Surprising Patterns for the Call Duration Distribution of Mobile Phone Users
Pedro Olmo Vaz de Melo, Leman Akoglu, Christos Faloutsos, Antonio Loureiro

Virus Propagation on Time-Varying Networks: Theory and Immunization Algorithms
B. Aditya Prakash, Hanghang Tong, Nicholas Valler, Michalis Faloutsos,
Christos Faloutsos

Session 12, Wednesday morning: Bayesian Learning 2

Expectation Propagation for Bayesian Multi-task Feature Selection
Daniel Hernández-Lobato, José Miguel Hernández Lobato, Thibault
Helleputte, Pierre Dupont

A Geometric View of Conjugate Priors
Arvind Agarwal, Hal Daume

Permutation Testing Improves Bayesian Network Learning
Ioannis Tsamardinos, Giorgos Borboudakis

An Efficient and Scalable Algorithm for Local Bayesian Networks Discovery
Sergio Rodrigues De Morais, Alexandre Aussem

Bayesian Knowledge Corroboration with Logical Rules and User Feedback
Gjergji Kasneci, Jurgen Gael, Ralf Herbrich, Thore Graepel

Session 13, Wednesday afternoon: Relational Learning

First-Order Bayes-Ball
Wannes Meert, Nima Taghipour, Hendrik Blockeel

Temporal Maximum Margin Markov Network
Xiaoqian Jiang, Dong Bing, Latanya Sweeney

Entropy and Margin Maximization for Structured Output Learning
Patrick Pletscher, Cheng Soon Ong, Joachim Buhman

Conditional Ranking on Relational Data
Tapio Pahikkala, Willem Waegeman, Antti Airola, Tapio Salakoski,
Bernard De Baets

Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models
Sriraam Natarajan, Tushar Khot, Daniel Lowd, Prasad Tadepalli,
Kristian Kersting, Jude Shavlik

Session 14, Wednesday afternoon: Graph Mining

Online Structural Graph Clustering using Frequent Subgraph Mining
Madeleine Seeland, Tobian Girschick, Fabian Buchwald, Stefan Kramer

Latent Structure Pattern Mining
Andreas Maunz, Christoph Helma, Tobias Cramer, Stefan Kramer

Relational Retrieval Using a Combination of Path-Constrained Random Walks
Ni Lao, William Cohen

Coniunge et Impera: Multiple-graph mining for Query-log analysis
Ilaria Bordino, Debora Donato, Ricardo Baeza-Yates

Relation Extraction under Distant Supervision with External Sources
Sebastian Riedel, Limin Yao

Session 15, Wednesday afternoon: Active and Adversarial Learning

Asking Generalized Queries to Ambiguous Oracle
Jun Du, Charles Ling

Complexity Bounds for Batch Active Learning in Classification
Philippe Rolet, Olivier Teytaud

A Unified Approach to Active Dual Supervision for Labeling Features and Examples
Joshua Attenberg, Prem Melville, Foster Provost

Mining Adversarial Patterns via Regularized Loss Minimization
Wei Liu, Sanjay Chawla

Online Learning in Adversarial Lipschitz Environments
Odalric-Ambrym Maillard, Remi Munos

Session 16, Thursday morning: Reinforcement Learning 2

Feature Selection for Reinforcement Learning: Evaluating Implicit
State-Reward Dependency
Hirotaka Hachiya, Masashi Sugiyama

Adaptive Bases for Reinforcement Learning
Dotan Di Castro, Shie Mannor

Dimension Reduction and Its Application to Model-based Exploration in Continuous Spaces
Ali Nouri, Michael Littman

Efficient Planning in Large POMDPs through Policy Graph Based Factorized Approximations
Joni Pajarinen, Jaakko Peltonen, Ari Hottinen, Mikko Uusitalo

Learning from Demonstration Using MDP Induced Metrics
Francisco Melo, Manuel Lopes

Session 17, Thursday morning: Rankings and Partial Orders

Kantorovich distances between rankings with applications to rank aggregation
Stephan Clemencon, Jeremie Jakubowicz

Predicting Partial Orders: Ranking with Abstention
Weiwei Cheng, Eyke Hullermeier

Conditional Ranking on Relational Data
Tapio Pahikkala, Willem Waegeman, Antti Airola, Tapio Salakoski,
Bernard De Baets

Fast Active Exploration for Link-Based Preference Learning using Gaussian Processes
Zhao Xu, Kristian Kersting, Thorsten Joachims

Adapting Decision DAGs for Multipartite Ranking
José Quevedo, Elena Montas, Oscar Luaces, Juan José del Coz

Session 18, Thursday morning: Spectral Analysis and Graph Clustering

Euclidean Distances, Soft and spectral Clustering on Weighted Graphs
Franois Bavaud

Improved MinMax Cut Graph Clustering with Non-negative Relaxation
Feiping Nie, Chris Ding, Dijun Luo, Heng Heng

A Game-Theoretic Framework to Identify Overlapping Communities in Social Networks
Wei Chen, Zhenming Liu, Xiaorui Sun, Yajun Wang

Laplacian Spectrum Learning
Pannagadatta Shivaswamy, Tony Jebara

On The Eigenvectors of p-Laplacian
Dijun Luo, Chris Ding, Heng Heng, Feiping Nie

Session 19, Thursday early afternoon: Supervised Learning

Automatic Model Adaptation for Complex Structured Domains
Geoffrey Levine, Gerald DeJong, Li-Lun Wang, Dan Roth, Rajhans
Samdani, Shankar Vembu

Competitive online generalized linear regression under square loss
Fedor Zhdanov, Vladimir Vovk

Online Knowledge-Based Support Vector Machines
Gautam Kunapuli, Amina Shabbeer, Kristin Bennett, Richard Maclin, Jude Shavlik

Regret Analysis for Performance Metrics in Multi-Label Classification: The Case of Hamming and Subset Zero-One Loss
Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hallermeier

Many-to-Many Graph Matching: a Continuous Relaxation Approach
Mikhail Zaslavskiy, Francis Bach, Jean-Philippe Vert

Session 20, Thursday early afternoon: Rules and Patterns 2

A Concise Representation of Association Rules using Minimal Descriptive Rules
Iyad Batal, Milos Hauskrecht

A measure of robustness of association rules
Yannick Le Bras, Patrick Meyer, Philippe Lenca, Stphane Lallich

Fast Extraction of Locally Optimal Patterns based on Consistent Pattern Function Variations
Frederic Pennerath

Maximal Exceptions with Minimal Descriptions

Matthijs van Leeuwen

Fast, Effective Molecular Feature Mining by Local Optimization
Albrecht Zimmermann, Bjoern Bringmann, Ulrich Rckert

Session 21, Thursday early afternoon: Learning and Mining in Dynamic Domains

Weighted Symbols-based Edit Distance for String-Structured Image Classification
Cecile Barat, Christophe Ducottet, Elisa Fromont, Anne-Claire Legrand,
Marc Sebban

Adaptive Parallel/Serial Sampling Mechanisms for Particle Filtering in Dynamic Bayesian Networks
Eva Besada-Portas, Sergey Plis, Jesus de la Cruz, Terran Lane

Hidden Conditional Ordinal Random Fields for Sequence Classification
Minyoung Kim, Vladimir Pavlovic

Classification and Novel Class Detection of Data Streams in A Dynamic Feature Space
Mohammad Masud, Qing Chen, Jing Gao, Latifur Khan, Jiawei Han, Bhavani
Thuraisingham

On Classifying Drifting Concepts in P2P Networks
Hock Hee Ang, Vivekanand Gopalkrishnan, Wee Keong Ng, Steven C.H. Hoi

Session 22, Thursday late afternoon: MDL, Outliers and Anomalies

Summarising Data by Clustering Items
Michael Mampaey, Jilles Vreeken

ITCH: Information-theoretic Cluster Hierarchies
Christian Boehm, Frank Fiedler, Annahita Oswald, Claudia Plant, Bianca
Wackersreuther, Peter Wackersreuther

Synchronization Based Outlier Detection
Junming Shao, Christian Boehm, Qinli Yang, Claudia Plant

On Detecting Clustered Anomalies using SCiForest
Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou

Software-Defect Localisation by Mining Dataflow-Enabled Call Graphs
Frank Eichinger, Klaus Krogmann, Roland Klug, Klemens Bhm

Session 23, Thursday late afternoon: Trajectory Mining and Process Mining

Collective traffic forecasting
Marco Lippi, Paolo Frasconi, Matteo Bertini

Unsupervised Trajectory Sampling
Nikos Pelekis, Ioannis Kopanakis, Costas Panagiotakis, Ioannis Theodoridis

Clustering Vessel Trajectories with Alignment Kernels under Trajectory Compression
Gerben De Vries, Maarten van Someren

Exploration-Exploitation of Eye Movement Enriched Multiple Feature Spaces for Content-Based Image Retrieval
Zakria Hussain, Alex Leung, Kitsuchart Pasupa, David Roi Hardoon,
Peter Auer, John Shawe-Taylor

Process Mining meets Abstract Interpretation
Josep Carmona, Jordi Cortadella

Session 24, Thursday late afternoon: Semi-supervised Learning

Accelerating Spectral Clustering with Partial Supervision
Dimitrios Mavroeidis

A Cluster-Level Semi-Supervision Model for Interactive Clustering
Avinava Dubey, Indrajit Bhattacharya, Shantanu Godbole

Semi-supervised Projection Clustering with Transferred Centroid Regularization
Bin TONG, Hao Shao, Binhui Chou, Einoshin Suzuki

Constrained Parameter Estimation for Semi-Supervised Learning: The Case of the Nearest Mean Classifier
Marco Loog

Semi-Supervised Abstraction-Augmented String Kernel for Multi-Level Bio-Relation Extraction
Pavel Kuksa, Yanjun Qi, Bing Bai, Ronan Collobert, Jason Weston