Schedule

See below for the daytime parallel sessions on Tuesday, Wednesday, and Thursday.
The titles link to a local copy of the paper in the conference proceedings.

The complete proceedings can be downloaded here in two parts: Part I (78MB) and Part II (66MB).

For full programme information please consult the conference booklet.

Tuesday 25th September

Tue1A: Bayesian Learning and Graphical Models
10:30 – 12:10

An experimental comparison of hybrid algorithms for Bayesian network structure learning
Maxime Gasse (University of Lyon), Alex Aussem (University of Lyon), Haytham Elghazel (University of Lyon)

Bayesian Network Classifiers with Reduced Precision Parameters
Sebastian Tschiatschek (TU Graz), Peter Reinprecht (TU Graz), Manfred Mücke (Sustainable Computing Research, Austria), Franz Pernkopf (TU Graz)

Combining Subjective Probabilities and Data in Training Markov Logic Networks
Tivadar Papai (University of Rochester), Shalini Ghosh (SRI International), Henry Kautz (University of Rochester)

Score-based Bayesian Skill Learning
Shengbo Guo (Xerox Research Centre Europe), Scott Sanner, Thore Graepel (Microsoft Research Cambridge), Wray Buntine (NICTA)

 

Tue1B: Association Rules and Frequent Patterns
10:30 – 12:10

Discovering Descriptive Tile Trees by Mining Optimal Geometric Subtiles
Nikolaj Tatti (University of Antwerp), Jilles Vreeken (University of Antwerp)

Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees
Matteo Riondato (Department of Computer Science Brown University), Eli Upfal (Department of Computer Science Brown University)

General Algorithms for Mining Closed Flexible Patterns under Various Equivalence Relations
Tomohiro I (Kyushu University), Yuki Enokuma (Kyushu University), Hideo Bannai (Kyushu University), Masayuki Takeda (Kyushu University)

Smoothing Categorical Data
Arno Siebes (University of Utrecht), Rene Kersten (Universiteit Utrecht)

 

Tue1C: Reinforcement Learning and Planning I
10:30 – 12:10

Adaptive Planning for Markov Decision Processes with Uncertain Transition Models via Incremental Feature Dependency Discovery
Nazim Kemal Ure (MIT), Alborz Geramifard (MIT), Girish Chowdhary (MIT), Jonathan How

APRIL: Active Preference Learning-based Reinforcement Learning
Riad Akrour (INRIA), Marc Schoenauer, Michele Sebag (University of Paris Sud)

Autonomous data-driven decision-making in Smart Electricity Markets
Markus Peters (Erasmus University Rotterdam), Wolfgang Ketter (Erasmus University Rotterdam), Maytal Saar-Tsechansky (University of Texas at Austin), John Collins (University of Minnesota)

Bayesian Nonparametric Inverse Reinforcement Learning
Bernard Michini (Mass. Institute of Technology), Jonathan How

 

Tue1D: Industry Track on Big Data
10:30 – 12:10

Large-scale language learning
Slav Petrov (Google)

Finding Low-Dimensional Structure in High-Dimensional Visual Data
David Wipf (Microsoft Research Asia)

 

Tue2A: Graphs, Trees, Sequences and Strings I
14:00 – 15:40

A Family of Feed-forward Models for Protein Sequence Classification
Sam Blasiak (George Mason University), Huzefa Rangwala (George Mason University), Kathryn Laskey (George Mason University)

Nearly exact mining of frequent trees in large networks
Ashraf Kibriya (Katholieke Universiteit Leuven), Jan Ramon (K.U. Leuven)

Reachability Analysis and Modeling of Dynamic Event Networks
Kathy Macropol (UCSB), Ambuj Singh (UCSB)

Size Matters: Finding the Most Informative Set of Window Lengths
Jefrey Lijffijt (Aalto University), Panagiotis Papapetrou (Aalto University), Kai Puolamäki (Aalto University)

 

Tue2B: Rankings and Recommendations
14:00 – 15:40

A Live Comparison of Methods for Personalized Article Recommendation at Forbes.com
Evan Kirshenbaum (HP Labs), George Forman (HP Labs), Michael Dugan (Forbes Media)

Active Evaluation of Ranking Functions based on Graded Relevance
Christoph Sawade (University of Potsdam), Steffen Bickel (Nokia gate5 GmbH), Timo von Oertzen (University of Virginia), Tobias Scheffer (University of Potsdam), Niels Landwehr (University of Potsdam)

Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback
Balázs Hidasi (Gravity R&D Ltd.), Domonkos Tikk (Gravity R&D Ltd.)

Probability Estimation for Multi-Class Classification based on Label Ranking
Weiwei Cheng (University of Marburg), Eyke Hullermeier (Philipps-Universitat Marburg)

 

Tue2C: Ensemble Methods
14:00 – 15:40

Boosting Nearest Neighbors for the Efficient Estimation of Posteriors
Roberto D’Ambrosio (Biocampus, Roma), Richard Nock (U. Antilles-Guyane), Wafa Bel Haj Ali (I3S – CNRS – U. Nice), Frank Nielsen (Sony CS Labs, Inc.), Michel Barlaud (Institut Universitaire de France)

Diversity Regularized Ensemble Pruning
Nan Li (Nanjing University, China), Yang Yu (Nanjing University), Zhi-Hua Zhou (Nanjing University)

Ensembles on Random Patches
Gilles Louppe (University of Liège), Pierre Geurts (University of Liège)

Multi-Task Boosting by Exploiting Task Relationships
Yu Zhang (HKUST), Dit-Yan Yeung (HKUST)

 

Tue2D: Industry Track on Big Data
14:00 – 15:40

Demand management for on-street parking: data, analysis, and actions
Onno Zoeter (Xerox Research Centre Europe)

Trusting cloudy data
Simon Shiu (HP Labs)

 

Tue3A: Dimensionality Reduction, Feature Selection and Extraction
16:10 – 17:50

Embedding Monte Carlo search of features in tree-based ensemble methods
Francis Maes (University of Liège), Pierre Geurts (University of Liège), Louis Wehenkel (University of Liège)

Hypergraph Spectra for Semi-supervised Feature Selection
Zhihong Zhang (University of York), Edwin Hancock (University of York), Xiao Bai (Beihang University)

Learning Neighborhoods for Metric Learning
Jun Wang (University of Geneva), Adam Woznica (University of Geneva), Alexandros Kalousis (University of Geneva)

PCA, Eigenvector Localization and Clustering for Side-Channel Attacks on Cryptographic Hardware Devices
Dimitrios Mavroeidis (Radboud University Nijmegen), Lejla Batina, Twan Van Laarhoven (Radboud University), Elena Marchiori

 

Tue3B: Multi-Relational Mining and Learning
16:10 – 17:50

Author Name Disambiguation using a Categorical Distribution Similarity
Shaohua Li (NTU), Gao Cong (NTU), Chunyan Miao (NTU)

Lifted Online Training of Relational Models with Stochastic Gradient Methods
Babak Ahmadi (Fraunhofer IAIS), Kristian Kersting (University of Bonn), Sriraam Natarajan (Wake Forest University)

Scalable Relation Prediction Exploiting both Intrarelational Correlation and Contextual Information
Xueyan Jiang (Ludwig-Maximilian University of Munich), Volker Tresp (Siemens & Ludwig Maximilian University of Munich), Yi Huang (Siemens), Maximilian Nickel (Ludwig-Maximilian University of Munich), Hans-Peter Kriegel (Ludwig-Maximilian University of Munich)

Relational Differential Prediction
Houssam Nassif (U of Wisconsin Madison), Vitor Santos Costa (Universidade do Porto), Elizabeth Burnside, David Page

 

Tue3C: Semi-Supervised and Transductive Learning
16:10 – 17:50

Bidirectional Semi-Supervised Learning with Graphs
Tomoharu Iwata (NTT), Kevin Duh (NAIST)

Graph-Based Transduction with Confidence
Matan Orbach (Technion), Koby Crammer (Technion)

Maximum Consistency Preferential Random Walks
Deguang Kong (UTA ), Chris Ding

Semi-Supervised Multi-label Classification: A Simultaneous Large-margin, Subspace Learning Approach
Yuhong Guo (Temple University), Dale Schuurmans (University of Alberta)

 

Tue3D: Demo Spotlights
16:10 – 17:50

VIKAMINE – Open-Source Subgroup Discovery, Pattern Mining, and Analytics
Martin Atzmueller (University of Kassel), Florian Lemmerich (University of Wuerzburg)

Association Rule Mining Following the Web Search Paradigm
Radek Škrabal (University of Economics, Prague), Milan Šimůnek (University of Economics, Prague), Stanislav Vojíř (University of Economics, Prague), Andrej Hazucha (University of Economics, Prague), Tomáš Marek (University of Economics, Prague), David Chudán (University of Economics, Prague), Tomáš Kliegr (University of Economics, PR)

OutRules: A Framework for Outlier Descriptions in Multiple Context Spaces
Emmanuel Müller (Karlsruhe Institute of Technology), Fabian Keller (Karlsruhe Institute of Technology), Sebastian Blanc (Karlsruhe Institute of Technology), Klemens Böhm (Karlsruhe Institute of Technology)

Knowledge Discovery through Symbolic Regression with HeuristicLab
Gabriel Kronberger (FH OÖ), Stefan Wagner (University of Applied Sciences Upper Austria), Michael Kommenda (University of Applied Sciences Upper Austria), Andreas Beham (University of Applied Sciences Upper Austria), Andreas Scheibenpflug (University of Applied Sciences Upper Austria), Michael Affenzeller (University of Applied Sciences Upper Austria)

An Aspect-Lexicon Creation and Evaluation Tool for Sentiment Analysis Researchers
Mus’ab Husaini (Sabanci University), Ahmet Kocyigit (Sabanci University), Dilek Tapucu (Sabanci University), Berrin Yanıkoğlu (Sabanci University), Yücel Saygın (Sabanci University)

ASV Monitor: Creating Comparability of Machine Learning Methods for Content Analysis
Andreas Niekler (HTWK Leipzig), Patrick Jähnichen (University of Leipzig), Gerhard Heyer (University of Leipzig)

TopicExplorer: Exploring Document Collections with Topic Models
Alexander Hinneburg (University Halle), Rico Preis (Informatik, Martin-Luther-University Halle-Wittenberg), René Schröder (Martin-Luther-University Halle-Wittenberg)

Extracting Trajectories through an Efficient and Unifying Spatio-Temporal Pattern Mining System
Hai Phan (LIRMM, TETIS – U. Montpellier2), Dino Ienco (IRSTEA), Pascal Poncelet (LIRMM – Univ. Montpellier 2), Maguelonne Teisseire (Irstea, Montpellier, France)

Scientific workflow management with ADAMS
Peter Reutemann (University of Waikato), Joaquin Vanschoren (Leiden University)

ClowdFlows: A Cloud Based Scientific Workflow Platform
Janez Kranjc (Jožef Stefan Institute), Vid Podpečan (Jožef Stefan Institute), Nada Lavrač (Jožef Stefan Institute)

 

Wednesday 26th September

Wed1A: Distance-Based Methods and Kernels
10:30 – 12:10

Classifying Stem Cell Differentiation Images by Information Distance
Xianglilan Zhang (University of Waterloo), Hongnan Wang, Tony Collins, Zhigang Luo, Ming Li

Distance Metric Learning Revisted
Qiong Cao (University of Exeter), Yiming Ying (University of Exeter), Peng Li

Geodesic Analysis on the Gaussian RKHS hypersphere
Nicolas Courty (Univeristy of Bretagne Sud), Thomas Burger (CEA), Pierre-François Marteau (University of Bretagne Sud)

The Bitvector Machine: A Fast and Robust Machine Learning Algorithm for Non-Linear Problems
Stefan Edelkamp (Universität Bremen), Martin Stommel (University of Bremen)

 

Wed1B: Time Series and Temporal Data Mining
10:30 – 12:10

Community Trend Outlier Detection using Soft Temporal Pattern Mining
Manish Gupta (UIUC), Jing Gao (SUNY at Buffalo), Yizhou Sun (UIUC), Jiawei Han (UIUC)

Data structures for detecting rare variations in time series
Caio Valentim (PUC-RIO), Davi Sotelo (PUC-RIO), Eduardo Laber (PUC-RIO)

Invariant Time-Series Classification
Josif Grabocka (University of Hildesheim), Alexandros Nanopoulos (ISMLL, Germany), Lars  Schmidt-Thieme (University of Hildesheim)

Learning Bi-clustered Vector Autoregressive Models
Tzu-Kuo Huang (Carnegie Mellon University), Jeff Schneider (Carnegie Mellon University)

 

Wed1C: Spatial and Geographical Data Mining
10:30 – 12:10

Inferring Geographic Coincidence in Ephemeral Social Networks
Honglei Zhuang (Tsinghua University), Alvin Chin (Nokia Research Center Beijing), Sen Wu (Tsinghua University), Wei Wang (Nokia Research Center Beijing), Xia Wang (Nokia Research Center Beijing), Jie Tang (Tsinghua University)

Socioscope: Spatio-Temporal Signal Recovery from Social Media
Jun-Ming Xu (University of Wisconsin-Madison), Aniruddha Bhargava (University of Wisconsin-Madison), Robert Nowak (University of Wisconsin-Madison), Xiaojin Zhu (University of Wisconsin-Madison)

Location Affiliation Networks: Bonding Social and Spatial Information
Konstantinos Pelechrinis (University of Pittsburgh), Prashant Krishnamurthy (University of Pittsburgh)

Pedestrian Quantity Estimation with Trajectory Patterns
Thomas Liebig (Fraunhofer IAIS), Zhao Xu (Fraunhofer IAIS), Michael May (Fraunhofer Institute), Stefan Wrobel (Fraunhofer IAIS)

 

Wed1D: Nectar Track
10:30 – 12:10

Learning Submodular Functions
Maria-Florina Balcan, Nick Harvey

Modelling Input Varying Correlations Between Multiple Responses
Andrew Wilson (University of Cambridge), Zoubin Ghahramani (University of Cambridge)

 

Wed2A: Social Network Mining I
14:00 – 15:40

Discovering Links among Social Networks
Francesco Buccafurri (University of Reggio Calabria), Gianluca Lax (University of Reggio Calabria), Antonino Nocera (University of Reggio Calabria), Domenico Ursino (University of Reggio Calabria)

Efficient Bi-objective Team Formation in Social Networks
Mehdi Kargar (York University), Aijun An (York University), Morteza Zihayat (York University)

Feature-Enhanced Probabilistic Models for Diffusion Network Inference
Liaoruo Wang (Cornell University), Stefano Ermon (Cornell University), John Hopcroft (Cornell University)

Influence Spread in Large-Scale Social Networks ? A Belief Propagation Approach
Huy Nguyen (University of Houston), Rong Zheng (University of Houston)

 

Wed2B: Large-Scale, Distributed and Parallel Mining and Learning I
14:00 – 15:40

Learning compact class codes for fast inference in large multi class classification
Moustapha Cisse (LIP6/CNRS), Thierry Artières (LIP6 – UPMC), Patrick Gallinari (Université Pierre et Marie Curie, France)

ParCube: Sparse Parallelizable Tensor Decompositions
Evangelos Papalexakis (Carnegie Mellon University), Christos Faloutsos (Carnegie Mellon University), Nicholas Sidiropoulos (University of Minnesota)

Stochastic Coordinate Descent Methods for Regularized Smooth and Nonsmooth Losses
Qing Tao (IA), Kang Kong (IAT), Dejun Chu (AOA), Gaowei Wu

Sublinear Algorithms for Penalized Logistic Regression in Massive Datasets
Haoruo Peng (Tsinghua University), Zhengyu Wang (Tsinghua University), Edward Chang, Shuchang Zhou (Google Research Beijing), Zhihua Zhang

 

Wed2C: Online Learning and Data Streams
14:00 – 15:40

Adaptive Two-View Online Learning for Math Topic Classification
Tam Nguyen (NTU), Kuiyu Chang (NTU), Siu Cheung Hui (NTU)

BDUOL: Double Updating Online Learning on a Fixed Budget
Peilin Zhao (NTU), Steven C.H. Hoi (NTU)

Improved counter based algorithms for frequent pairs mining in transactional data streams
Konstantin Kutzkov (IT University of Copenhagen)

Mirror Descent for Metric Learning: A Unified Approach
Gautam Kunapuli (Univ. of Wisconsin-Madison), Jude Shavlik (Univ. of Wisconsin-Madison)

 

Wed2D: Nectar Track
14:00 – 15:40

Matrix Factorization as Search
Kristian Kersting (University of Bonn), Christian Bauckhage, Christian Thurau, Mirwaes Wahabzada (Fraunhofer IAIS)

Metal binding in proteins: machine learning complements X-ray absorption spectroscopy
Marco Lippi, Andrea Passerini (Università degli Studi di Trento), Marco Punta, Paolo Frasconi (Universita degli Studi di Firenze)

 

Wed3A: Data Mining Process
16:10 – 17:25

A Framework for Evaluating the Smoothness of Data-Mining Results
Gaurav Misra, Behzad Golshan, Evimaria Terzi (Boston University)

Coupled Bayesian Sets algorithm for semi-supervised learning and information extraction.
Saurabh Verma (IT-BHU), Estevam Hruschka

Policy iteration based on a learned transition model
Vivek Ramavajjala (UC San Diego), Charles Elkan (University of California San Diego)

 

Wed3B: Sensor Data
16:10 – 17:25

MDL-based Analysis of Time Series at Multiple Time-Scales
Ugo Vespier (LIACS), Arno Knobbe (Universiteit Leiden), Siegfried Nijssen (Katholieke Universiteit Leuven, Belgium), Joaquin Vanschoren

Separable Approximate Optimization of Support Vector Machines for Distributed Sensing
Sangkyun Lee (TU Dortmund), Marco Stolpe (TU Dortmund), Katharina Morik (University of Dortmund)

Unsupervised inference of auditory attention from biosensors
Melih Kandemir (Aalto University), Arto Klami (Aalto University), Akos Vetek (Nokia Research Center, Finland), Samuel Kaski (Aalto University)

 

Wed3C: Privacy and Security
16:10 – 17:25

AUDIO: An Integrity Auditing Framework of Outlier-Mining-as-a-Service Systems
Ruilin Liu (Stevens Institute of Technology), Hui Wang (Stevens Institute of Technolog), Anna Monreale (University of Pisa), Dino Pedreschi (Universita di Pisa), Fosca Giannotti (Universita di Pisa), Wenge Guo (New Jersey Institute of Technology)

Differentially Private Projected Histograms: Construction and Use for Prediction
Staal Vinterbo (UCSD)

Fairness-aware Classifier with Prejudice Remover Regularizer
Toshihiro Kamishima (National Institute of Advanced Industrial Science and Technology), Shotaro Akaho (AIST), Hideki Asoh (AIST), Jun Sakuma (University of Tsukuba  and Japan Science and Technology Agency)

 

Thursday 27th September

Thu1A: Social Network Mining II
10:30 – 12:10

On Approximation of Real-World Influence Spread
Yu Yang (University of Science and Technology of China), Enhong Chen (USTC), Qi Liu (USTC), Biao Xiang (USTC), Tong Xu (USTC), Shafqat Shad (USTC)

Opinion Formation by Voter Model with Temporal Decay Dynamics
Masahiro Kimura (Ryukoku University), Kazumi Saito (University of Shizuoka), Kouzou Ohara (Aoyama Gakuin University), Hiroshi Motoda (Osaka University)

Viral Marketing for Product Cross-sell through Social Networks
Ramasuri  Narayanam (IBM Research), Amit  Nanavati (IBM Reseach, India)

Which Topic will You Follow?
Deqing Yang (Fudan University), Yanghua Xiao, Wei Wang (School of computer science, Fudan University, Shanghai, China), Bo Xu, Sheng Huang (IBM China Research Lab, Shanghai, China), Hanghang Tong

 

Thu1B: Rule Learning and Subgroup Discovery
10:30 – 12:10

A Bayesian approach for classification rule mining in quantitative databases
Dominique Gay (Orange Labs), Marc Boulle (Orange)

A Bayesian Scoring Technique for Mining Predictive and Non-Spurious Rules
Iyad Batal (University of Pittsburgh), Gregory Cooper (University of Pittsburgh), Milos Hauskrecht (University of Pittsburgh)

Generic Pattern Trees for Exhaustive Exceptional Model Mining
Florian Lemmerich (University of Wuerzburg), Martin Becker (University of Wuerzburg), Martin Atzmueller (University of Kassel)

Handling Time Changing Data with Adaptive Very Fast Decision Rules
Petr Kosina (LIAAD-INESC Porto), Joao Gama (University of Porto)

 

Thu1C: Multi-Task and Transfer Learning I
10:30 – 12:10

Efficient Training of Graph-Regularized Multitask SVMs
Christian Widmer (Memorial Sloan-Kettering Cancer Center), Marius Kloft (TU Berlin), Nico Görnitz (TU Berlin), Gunnar Rätsch (Memorial Sloan-Kettering Cancer Center)

Geometry Preserving Multi-task Metric Learning
Peipei Yang (Institute of Automation, Chinese Academy of Science), Kaizhu Huang (Institute of Automation, Chinese Academy of Sciences), Cheng-Lin Liu (Institute of Automation, Chinese Academy of Sciences)

Learning and Inference in Probabilistic Classifier Chains with Beam Search
Abhishek Kumar (UC San Diego), Shankar Vembu (University of Toronto), Aditya Menon (UC San Diego), Charles Elkan (UC San Diego)

Learning Multiple Tasks with Boosted Decision Trees
Jean-Baptiste Faddoul (Inria Lille), Boris Chidlovskii (XRCE), Remi Gilleron (University of Lille 3), Fabien Torre

 

Thu1D: Industry Track: Start-up Stories
10:30 – 12:10

Who is buying what in the UK? Mining e-commerce data
Jurgen Van Gael (Rangespan)

Automated real-time intelligent marketing
Jason McFall (Causata)

 

Thu2A: Graphs, Trees, Sequences and Strings II
14:00 – 15:40

An Efficiently Computable Support Measure for Frequent Subgraph Pattern Mining
Yuyi Wang (Katholieke Universiteit Leuven), Jan Ramon (K.U. Leuven)

Efficient Graph Kernels by Randomization
Marion Neumann (Fraunhofer IAIS), Novi Patricia (Fraunhofer IAIS), Roman Garnett (Carnegie Mellon University), Kristian Kersting (University of Bonn)

Graph Mining for Object Tracking in Videos
Fabien Diot (LaHC, Alcatel-Lucent Bell Labs), Elisa Fromont (University of Saint-Etienne, France), Baptiste Jeudy (LaHC), emmanuel Marilly (Alcatel-Lucent Bell Labs), olivier Martinot (Alcatel-Lucent Bell Labs)

Hypergraph Learning with Hyperedge Expansion
Li Pu (École Polytechnique Fédérale de Lausanne), Boi Faltings (EPFL)

 

Thu2B: Classification
14:00 – 15:40

A Note on Extending Generalization Bounds for Binary Large-margin Classifiers to Multiple Classes
Ueruen Dogan (University of Potsdam), Tobias Glasmachers (Institut für Neuroinformatik), Christian Igel (University of Copenhagen)

Extension of the Rocchio Classification Method to Multi-modal Categorization of Documents in Social Media
Amin Mantrach (Yahoo! Research Barcelona), Jean-Michel Renders (XRCE)

Label-noise Robust Logistic Regression and Its Applications
Jakramate  Bootkrajang (University of Birmingham), Ata Kaban (University of Birmingham)

Sentiment Classification with Supervised Sequence Encoder
Dmitriy Bespalov (Drexel University), Yanjun Qi (NEC Research), Bing Bai (NEC Research), Ali Shokoufandeh (Drexel University)

 

Thu2C: Multi-Task and Transfer Learning II
14:00 – 15:40

Discriminative Factor Alignment across Heterogeneous Feature Space
Fangwei Hu (Shanghai Jiao Tong University), Tianqi Chen, Nathan Liu, Qiang Yang (Hong Kong University of Science and Technologu), Yong Yu

Learning to Perceive Two-Dimensional Displays Using Probabilistic Grammars
Nan Li (Carnegie Mellon University), William Cohen, Kenneth Koedinger

Sparse Gaussian Processes for Multi-task Learning
Yuyang Wang (Tufts University), Roni Khardon (Tufts University)

Transfer Spectral Clustering
Wenhao Jiang (HK PolyU), Fu-lai Chung

 

Thu2D: Industry Track: Startup Stories
14:00 – 15:40

Somebody needs your algorithm – Cloud’N'Sci.fi
Petri Myllymaki (University of Helsinki, Ekahau, Cloud’N'Sci) and Pauli Misikangas (Ekahau, Cloud’N'Sci)

Machine Learning at PeerIndex: Telling stories about users and their influence
Ferenc Huszar (Peerindex)

 

Thu3A: Large-Scale, Distributed and Parallel Mining and Learning II
16:10 – 17:50

CC-MR – Finding Connected Components in Huge Graphs with MapReduce
Thomas Seidl (RWTH Aachen University), Brigitte Boden (RWTH Aachen University), Sergej Fries (RWTH Aachen University)

Fast Near Neighbor Search in High-Dimensional Binary Data
Anshumali  Shrivastav, Ping Li

Fully Sparse Topic Models
Khoat Than (Japan Advanced Institute of Science and Technology), Tu-Bao Ho (JAIST)

Massively Parallel Feature Selection: An Approach Based on Variance Preservation
Zheng Zhao (SAS), James Cox (SAS Inc), David Duling (SAS Inc), Warren Sarle (SAS Inc)

 

Thu3B: Natural Language Processing
16:10 – 17:50

Collective Information Extraction with Context-specific Consistencies
Peter Kluegl (University of Wuerzburg), Martin Toepfer (University of Wuerzburg), Florian Lemmerich (University of Wuerzburg), Andreas Hotho (University of Würzburg), Frank Puppe (University of Wuerzburg)

Supervised Learning of Semantic Relatedness
David Yanay (Technion), Ran El-Yaniv (Technion)

Unsupervised Bayesian Part of Speech Inference with Particle Gibbs
Gregory Dubbin (Individual Purchaser), Phil Blunsom (University of Oxford)

WikiSent : Weakly Supervised Sentiment Analysis Through Extractive Summarization With Wikipedia
Subhabrata Mukherjee (IIT Bombay), Pushpak Bhattacharyya (IIT Bombay)

 

Thu3C: Reinforcement Learning and Planning II
16:10 – 17:50

Bootstrapping Monte Carlo Tree Search with an Imperfect Heuristic
Truong-Huy Nguyen (National University of Singapore), Wee-Sun Lee (National Uni. of Singapore), Tze-Yun Leong (National Uni. of Singapore)

Fast Reinforcement Learning with Large Action Sets using Error-Correcting Output Codes for MDP Factorization
Gabriel Dulac-Arnold (LIP6-UPMC), Ludovic Denoyer (LIP6-UPMC), Philippe Preux (INRIA-LIFL), Patrick Gallinari (Université Pierre et Marie Curie, France)

Learning Policies For Battery Usage Optimization in Electric Vehicles
Stefano Ermon (Cornell University), Yexiang Xue (Cornell University), Carla Gomes (Cornell University), Bart Selman (Cornell University)

Structured Apprenticeship Learning
Abdeslam Boularias (MPI for Intelligent Systems), Oliver Kroemer (TU Darmstadt), Jan Peters (TU Darmstadt)

 

Thu3D: Industry Track: Startup Stories
16:10 – 17:50

“Choice is good, choosing is a chore” – choosing the right database platform for effective Knowledge Discovery
Alastair Page (JustOneDB)

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