{"id":14,"date":"2011-04-19T10:44:22","date_gmt":"2011-04-19T10:44:22","guid":{"rendered":"http:\/\/ecmlpkdd.blogs.bristol.ac.uk\/?page_id=14"},"modified":"2012-09-25T13:57:33","modified_gmt":"2012-09-25T13:57:33","slug":"schedule","status":"publish","type":"page","link":"https:\/\/ecmlpkdd.blogs.bristol.ac.uk\/programme\/schedule\/","title":{"rendered":"Schedule"},"content":{"rendered":"

See below for the daytime parallel sessions on Tuesday, Wednesday, and Thursday.
\nThe titles link to a local copy of the paper in the conference proceedings. <\/p>\n

The complete proceedings can be downloaded here in two parts: Part I (78MB)<\/a> and Part II (66MB)<\/a>. <\/p>\n

For full programme information please consult the conference booklet<\/a>. <\/p>\n

Tuesday 25th September<\/h2>\n

Tue1A: Bayesian Learning and Graphical Models
\n10:30 – 12:10<\/h4>\n

An experimental comparison of hybrid algorithms for Bayesian network structure learning<\/a><\/strong>
\nMaxime Gasse (University of Lyon), Alex Aussem (University of Lyon), Haytham Elghazel (University of Lyon)<\/em><\/p>\n

Bayesian Network Classifiers with Reduced Precision Parameters<\/a><\/strong>
\nSebastian Tschiatschek (TU Graz), Peter Reinprecht (TU Graz), Manfred M\u00fccke (Sustainable Computing Research, Austria), Franz Pernkopf (TU Graz)<\/em><\/p>\n

Combining Subjective Probabilities and Data in Training Markov Logic Networks<\/a><\/strong>
\nTivadar Papai (University of Rochester), Shalini Ghosh (SRI International), Henry Kautz (University of Rochester)<\/em><\/p>\n

Score-based Bayesian Skill Learning<\/a><\/strong>
\nShengbo Guo (Xerox Research Centre Europe), Scott Sanner, Thore Graepel (Microsoft Research Cambridge), Wray Buntine (NICTA)<\/em><\/p>\n

 <\/p>\n

Tue1B: Association Rules and Frequent Patterns
\n10:30 – 12:10<\/h4>\n

Discovering Descriptive Tile Trees by Mining Optimal Geometric Subtiles<\/a><\/strong>
\nNikolaj Tatti (University of Antwerp), Jilles Vreeken (University of Antwerp)<\/em><\/p>\n

Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees<\/a><\/strong>
\nMatteo Riondato (Department of Computer Science Brown University), Eli Upfal (Department of Computer Science Brown University)<\/em><\/p>\n

General Algorithms for Mining Closed Flexible Patterns under Various Equivalence Relations<\/a><\/strong>
\nTomohiro I (Kyushu University), Yuki Enokuma (Kyushu University), Hideo Bannai (Kyushu University), Masayuki Takeda (Kyushu University)<\/em><\/p>\n

Smoothing Categorical Data<\/a><\/strong>
\nArno Siebes (University of Utrecht), Rene Kersten (Universiteit Utrecht)<\/em><\/p>\n

 <\/p>\n

Tue1C: Reinforcement Learning and Planning I
\n10:30 – 12:10<\/h4>\n

Adaptive Planning for Markov Decision Processes with Uncertain Transition Models via Incremental Feature Dependency Discovery<\/a><\/strong>
\nNazim Kemal Ure (MIT), Alborz Geramifard (MIT), Girish Chowdhary (MIT), Jonathan How<\/em><\/p>\n

APRIL: Active Preference Learning-based Reinforcement Learning<\/a><\/strong>
\nRiad Akrour (INRIA), Marc Schoenauer, Michele Sebag (University of Paris Sud)<\/em><\/p>\n

Autonomous data-driven decision-making in Smart Electricity Markets<\/a><\/strong>
\nMarkus Peters (Erasmus University Rotterdam), Wolfgang Ketter (Erasmus University Rotterdam), Maytal Saar-Tsechansky (University of Texas at Austin), John Collins (University of Minnesota)<\/em><\/p>\n

Bayesian Nonparametric Inverse Reinforcement Learning<\/a><\/strong>
\nBernard Michini (Mass. Institute of Technology), Jonathan How<\/em><\/p>\n

 <\/p>\n

Tue1D: Industry Track on Big Data
\n10:30 – 12:10<\/h4>\n

Large-scale language learning<\/strong>
\nSlav Petrov (Google)<\/em><\/strong><\/p>\n

Finding Low-Dimensional Structure in High-Dimensional Visual Data<\/strong>
\nDavid Wipf (Microsoft Research Asia)<\/em><\/p>\n

 <\/p>\n

Tue2A: Graphs, Trees, Sequences and Strings I
\n14:00 – 15:40<\/h4>\n

A Family of Feed-forward Models for Protein Sequence Classification<\/a><\/strong>
\nSam Blasiak (George Mason University), Huzefa Rangwala (George Mason University), Kathryn Laskey (George Mason University)<\/em><\/p>\n

Nearly exact mining of frequent trees in large networks<\/a><\/strong>
\nAshraf Kibriya (Katholieke Universiteit Leuven), Jan Ramon (K.U. Leuven)<\/em><\/p>\n

Reachability Analysis and Modeling of Dynamic Event Networks<\/a><\/strong>
\nKathy Macropol (UCSB), Ambuj Singh (UCSB)<\/em><\/p>\n

Size Matters: Finding the Most Informative Set of Window Lengths<\/a><\/strong>
\n Jefrey Lijffijt (Aalto University), Panagiotis Papapetrou (Aalto University), Kai Puolam\u00e4ki (Aalto University)<\/em><\/p>\n

 <\/p>\n

Tue2B: Rankings and Recommendations
\n14:00 – 15:40<\/h4>\n

A Live Comparison of Methods for Personalized Article Recommendation at Forbes.com<\/a><\/strong>
\nEvan Kirshenbaum (HP Labs), George Forman (HP Labs), Michael Dugan (Forbes Media)<\/em><\/p>\n

Active Evaluation of Ranking Functions based on Graded Relevance<\/a><\/strong>
\nChristoph 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)<\/em><\/p>\n

Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback<\/a><\/strong>
\nBal\u00e1zs Hidasi (Gravity R&D Ltd.), Domonkos Tikk (Gravity R&D Ltd.)<\/em><\/p>\n

Probability Estimation for Multi-Class Classification based on Label Ranking<\/a><\/strong>
\nWeiwei Cheng (University of Marburg), Eyke Hullermeier (Philipps-Universitat Marburg)<\/em><\/p>\n

 <\/p>\n

Tue2C: Ensemble Methods
\n14:00 – 15:40<\/h4>\n

Boosting Nearest Neighbors for the Efficient Estimation of Posteriors<\/a><\/strong>
\nRoberto 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)<\/em><\/p>\n

Diversity Regularized Ensemble Pruning<\/a><\/strong>
\nNan Li (Nanjing University, China), Yang Yu (Nanjing University), Zhi-Hua Zhou (Nanjing University)<\/em><\/p>\n

Ensembles on Random Patches<\/a><\/strong>
\nGilles Louppe (University of Li\u00e8ge), Pierre Geurts (University of Li\u00e8ge)<\/em><\/p>\n

Multi-Task Boosting by Exploiting Task Relationships<\/a><\/strong>
\nYu Zhang (HKUST), Dit-Yan Yeung (HKUST)<\/em><\/p>\n

 <\/p>\n

Tue2D: Industry Track on Big Data
\n14:00 – 15:40<\/h4>\n

Demand management for on-street parking: data, analysis, and actions<\/strong>
\n Onno Zoeter (Xerox Research Centre Europe)<\/em><\/p>\n

Trusting cloudy data<\/strong>
\n Simon Shiu (HP Labs)<\/em><\/p>\n

 <\/p>\n

Tue3A: Dimensionality Reduction, Feature Selection and Extraction
\n16:10 – 17:50<\/h4>\n

Embedding Monte Carlo search of features in tree-based ensemble methods<\/a><\/strong>
\nFrancis Maes (University of Li\u00e8ge), Pierre Geurts (University of Li\u00e8ge), Louis Wehenkel (University of Li\u00e8ge)<\/em><\/p>\n

Hypergraph Spectra for Semi-supervised Feature Selection<\/a><\/strong>
\nZhihong Zhang (University of York), Edwin Hancock (University of York), Xiao Bai (Beihang University)<\/em><\/p>\n

Learning Neighborhoods for Metric Learning<\/a><\/strong>
\nJun Wang (University of Geneva), Adam Woznica (University of Geneva), Alexandros Kalousis (University of Geneva)<\/em><\/p>\n

PCA, Eigenvector Localization and Clustering for Side-Channel Attacks on Cryptographic Hardware Devices<\/a><\/strong>
\nDimitrios Mavroeidis (Radboud University Nijmegen), Lejla Batina, Twan Van Laarhoven (Radboud University), Elena Marchiori<\/em><\/p>\n

 <\/p>\n

Tue3B: Multi-Relational Mining and Learning
\n16:10 – 17:50<\/h4>\n

Author Name Disambiguation using a Categorical Distribution Similarity<\/a><\/strong>
\nShaohua Li (NTU), Gao Cong (NTU), Chunyan Miao (NTU)<\/em><\/p>\n

Lifted Online Training of Relational Models with Stochastic Gradient Methods<\/a><\/strong>
\nBabak Ahmadi (Fraunhofer IAIS), Kristian Kersting (University of Bonn), Sriraam Natarajan (Wake Forest University)<\/em><\/p>\n

Scalable Relation Prediction Exploiting both Intrarelational Correlation and Contextual Information<\/a><\/strong>
\nXueyan 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)<\/em><\/p>\n

Relational Differential Prediction<\/a><\/strong>
\nHoussam Nassif (U of Wisconsin Madison), Vitor Santos Costa (Universidade do Porto), Elizabeth Burnside, David Page<\/em><\/p>\n

 <\/p>\n

Tue3C: Semi-Supervised and Transductive Learning
\n16:10 – 17:50<\/h4>\n

Bidirectional Semi-Supervised Learning with Graphs<\/a><\/strong>
\nTomoharu Iwata (NTT), Kevin Duh (NAIST)<\/em><\/p>\n

Graph-Based Transduction with Confidence<\/a><\/strong>
\nMatan Orbach (Technion), Koby Crammer (Technion)<\/em><\/p>\n

Maximum Consistency Preferential Random Walks<\/a><\/strong>
\nDeguang Kong (UTA ), Chris Ding<\/em><\/p>\n

Semi-Supervised Multi-label Classification: A Simultaneous Large-margin, Subspace Learning Approach<\/a><\/strong>
\nYuhong Guo (Temple University), Dale Schuurmans (University of Alberta)<\/em><\/p>\n

 <\/p>\n

Tue3D: Demo Spotlights
\n16:10 – 17:50<\/h4>\n

VIKAMINE – Open-Source Subgroup Discovery, Pattern Mining, and Analytics<\/a><\/strong>
\nMartin Atzmueller (University of Kassel), Florian Lemmerich (University of Wuerzburg)<\/em><\/p>\n

Association Rule Mining Following the Web Search Paradigm<\/a><\/strong>
\nRadek \u0160krabal (University of Economics, Prague), Milan \u0160im\u016fnek (University of Economics, Prague), Stanislav Voj\u00ed\u0159 (University of Economics, Prague), Andrej Hazucha (University of Economics, Prague), Tom\u00e1\u0161 Marek (University of Economics, Prague), David Chud\u00e1n (University of Economics, Prague), Tom\u00e1\u0161 Kliegr (University of Economics, PR)<\/em><\/p>\n

OutRules: A Framework for Outlier Descriptions in Multiple Context Spaces<\/a><\/strong>
\nEmmanuel M\u00fcller (Karlsruhe Institute of Technology), Fabian Keller (Karlsruhe Institute of Technology), Sebastian Blanc (Karlsruhe Institute of Technology), Klemens B\u00f6hm (Karlsruhe Institute of Technology)<\/em><\/p>\n

Knowledge Discovery through Symbolic Regression with HeuristicLab<\/a><\/strong>
\nGabriel Kronberger (FH O\u00d6), 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)<\/em><\/p>\n

An Aspect-Lexicon Creation and Evaluation Tool for Sentiment Analysis Researchers<\/a><\/strong>
\nMus’ab Husaini (Sabanci University), Ahmet Kocyigit (Sabanci University), Dilek Tapucu (Sabanci University), Berrin Yan\u0131ko\u011flu (Sabanci University), Y\u00fccel Sayg\u0131n (Sabanci University)<\/em><\/p>\n

ASV Monitor: Creating Comparability of Machine Learning Methods for Content Analysis<\/a><\/strong>
\nAndreas Niekler (HTWK Leipzig), Patrick J\u00e4hnichen (University of Leipzig), Gerhard Heyer (University of Leipzig)<\/em><\/p>\n

TopicExplorer: Exploring Document Collections with Topic Models<\/a><\/strong>
\nAlexander Hinneburg (University Halle), Rico Preis (Informatik, Martin-Luther-University Halle-Wittenberg), Ren\u00e9 Schr\u00f6der (Martin-Luther-University Halle-Wittenberg)<\/em><\/p>\n

Extracting Trajectories through an Efficient and Unifying Spatio-Temporal Pattern Mining System<\/a><\/strong>
\nHai Phan (LIRMM, TETIS – U. Montpellier2), Dino Ienco (IRSTEA), Pascal Poncelet (LIRMM – Univ. Montpellier 2), Maguelonne Teisseire (Irstea, Montpellier, France)<\/em><\/p>\n

Scientific workflow management with ADAMS<\/a><\/strong>
\nPeter Reutemann (University of Waikato), Joaquin Vanschoren (Leiden University)<\/em><\/p>\n

ClowdFlows: A Cloud Based Scientific Workflow Platform<\/a><\/strong>
\nJanez Kranjc (Jo\u017eef Stefan Institute), Vid Podpe\u010dan (Jo\u017eef Stefan Institute), Nada Lavra\u010d (Jo\u017eef Stefan Institute)<\/em><\/p>\n

 <\/p>\n

Wednesday 26th September<\/h2>\n

Wed1A: Distance-Based Methods and Kernels
\n10:30 – 12:10<\/h4>\n

Classifying Stem Cell Differentiation Images by Information Distance<\/a><\/strong>
\n Xianglilan Zhang (University of Waterloo), Hongnan Wang, Tony Collins, Zhigang Luo, Ming Li<\/em><\/p>\n

Distance Metric Learning Revisted<\/a><\/strong>
\n Qiong Cao (University of Exeter), Yiming Ying (University of Exeter), Peng Li<\/em><\/p>\n

Geodesic Analysis on the Gaussian RKHS hypersphere<\/a><\/strong>
\n Nicolas Courty (Univeristy of Bretagne Sud), Thomas Burger (CEA), Pierre-Fran\u00e7ois Marteau (University of Bretagne Sud)<\/em><\/p>\n

The Bitvector Machine: A Fast and Robust Machine Learning Algorithm for Non-Linear Problems<\/a><\/strong>
\n Stefan Edelkamp (Universit\u00e4t Bremen), Martin Stommel (University of Bremen)<\/em><\/p>\n

 <\/p>\n

Wed1B: Time Series and Temporal Data Mining
\n10:30 – 12:10<\/h4>\n

Community Trend Outlier Detection using Soft Temporal Pattern Mining<\/a><\/strong>
\n Manish Gupta (UIUC), Jing Gao (SUNY at Buffalo), Yizhou Sun (UIUC), Jiawei Han (UIUC)<\/em><\/p>\n

Data structures for detecting rare variations in time series<\/a><\/strong>
\n Caio Valentim (PUC-RIO), Davi Sotelo (PUC-RIO), Eduardo Laber (PUC-RIO)<\/em><\/p>\n

Invariant Time-Series Classification<\/a><\/strong>
\n Josif Grabocka (University of Hildesheim), Alexandros Nanopoulos (ISMLL, Germany), Lars\u00a0 Schmidt-Thieme (University of Hildesheim)<\/em><\/p>\n

Learning Bi-clustered Vector Autoregressive Models<\/a><\/strong>
\n Tzu-Kuo Huang (Carnegie Mellon University), Jeff Schneider (Carnegie Mellon University)<\/em><\/p>\n

 <\/p>\n

Wed1C: Spatial and Geographical Data Mining
\n10:30 – 12:10<\/h4>\n

Inferring Geographic Coincidence in Ephemeral Social Networks<\/a><\/strong>
\n 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)<\/em><\/p>\n

Socioscope: Spatio-Temporal Signal Recovery from Social Media<\/a><\/strong>
\n 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)<\/em><\/p>\n

Location Affiliation Networks: Bonding Social and Spatial Information<\/a><\/strong>
\n Konstantinos Pelechrinis (University of Pittsburgh), Prashant Krishnamurthy (University of Pittsburgh)<\/em><\/p>\n

Pedestrian Quantity Estimation with Trajectory Patterns<\/a><\/strong>
\n Thomas Liebig (Fraunhofer IAIS), Zhao Xu (Fraunhofer IAIS), Michael May (Fraunhofer Institute), Stefan Wrobel (Fraunhofer IAIS)<\/em><\/p>\n

 <\/p>\n

Wed1D: Nectar Track
\n10:30 – 12:10<\/h4>\n

Learning Submodular Functions<\/a><\/strong>
\nMaria-Florina Balcan, Nick Harvey<\/em><\/p>\n

Modelling Input Varying Correlations Between Multiple Responses<\/a><\/strong>
\nAndrew Wilson (University of Cambridge), Zoubin Ghahramani (University of Cambridge)<\/em><\/p>\n

 <\/p>\n

Wed2A: Social Network Mining I
\n14:00 – 15:40<\/h4>\n

Discovering Links among Social Networks<\/a><\/strong>
\nFrancesco Buccafurri (University of Reggio Calabria), Gianluca Lax (University of Reggio Calabria), Antonino Nocera (University of Reggio Calabria), Domenico Ursino (University of Reggio Calabria)<\/em><\/p>\n

Efficient Bi-objective Team Formation in Social Networks<\/a><\/strong>
\nMehdi Kargar (York University), Aijun An (York University), Morteza Zihayat (York University)<\/em><\/p>\n

Feature-Enhanced Probabilistic Models for Diffusion Network Inference<\/a><\/strong>
\nLiaoruo Wang (Cornell University), Stefano Ermon (Cornell University), John Hopcroft (Cornell University)<\/em><\/p>\n

Influence Spread in Large-Scale Social Networks ? A Belief Propagation Approach<\/a><\/strong>
\nHuy Nguyen (University of Houston), Rong Zheng (University of Houston)<\/em><\/p>\n

 <\/p>\n

Wed2B: Large-Scale, Distributed and Parallel Mining and Learning I
\n14:00 – 15:40<\/h4>\n

Learning compact class codes for fast inference in large multi class classification<\/a><\/strong>
\nMoustapha Cisse (LIP6\/CNRS), Thierry Arti\u00e8res (LIP6 – UPMC), Patrick Gallinari (Universit\u00e9 Pierre et Marie Curie, France)<\/em><\/p>\n

ParCube: Sparse Parallelizable Tensor Decompositions<\/a><\/strong>
\nEvangelos Papalexakis (Carnegie Mellon University), Christos Faloutsos (Carnegie Mellon University), Nicholas Sidiropoulos (University of Minnesota)<\/em><\/p>\n

Stochastic Coordinate Descent Methods for Regularized Smooth and Nonsmooth Losses<\/a><\/strong>
\nQing Tao (IA), Kang Kong (IAT), Dejun Chu (AOA), Gaowei Wu<\/em><\/p>\n

Sublinear Algorithms for Penalized Logistic Regression in Massive Datasets<\/a><\/strong>
\nHaoruo Peng (Tsinghua University), Zhengyu Wang (Tsinghua University), Edward Chang, Shuchang Zhou (Google Research Beijing), Zhihua Zhang<\/em><\/p>\n

 <\/p>\n

Wed2C: Online Learning and Data Streams
\n14:00 – 15:40<\/h4>\n

Adaptive Two-View Online Learning for Math Topic Classification<\/a><\/strong>
\n Tam Nguyen (NTU), Kuiyu Chang (NTU), Siu Cheung Hui (NTU)<\/em><\/p>\n

BDUOL: Double Updating Online Learning on a Fixed Budget<\/a><\/strong>
\n Peilin Zhao (NTU), Steven C.H. Hoi (NTU)<\/em><\/p>\n

Improved counter based algorithms for frequent pairs mining in transactional data streams<\/a><\/strong>
\n Konstantin Kutzkov (IT University of Copenhagen)<\/em><\/p>\n

Mirror Descent for Metric Learning: A Unified Approach<\/a><\/strong>
\n Gautam Kunapuli (Univ. of Wisconsin-Madison), Jude Shavlik (Univ. of Wisconsin-Madison)<\/em><\/p>\n

 <\/p>\n

Wed2D: Nectar Track
\n14:00 – 15:40<\/h4>\n

Matrix Factorization as Search<\/a><\/strong>
\nKristian Kersting (University of Bonn), Christian Bauckhage, Christian Thurau, Mirwaes Wahabzada (Fraunhofer IAIS)<\/em><\/p>\n

Metal binding in proteins: machine learning complements X-ray absorption spectroscopy<\/a><\/strong>
\nMarco Lippi, Andrea Passerini (Universit\u00e0 degli Studi di Trento), Marco Punta, Paolo Frasconi (Universita degli Studi di Firenze)<\/em><\/p>\n

 <\/p>\n

Wed3A: Data Mining Process
\n16:10 – 17:25<\/h4>\n

A Framework for Evaluating the Smoothness of Data-Mining Results<\/a><\/strong>
\n Gaurav Misra, Behzad Golshan, Evimaria Terzi (Boston University)<\/em><\/p>\n

Coupled Bayesian Sets algorithm for semi-supervised learning and information extraction.<\/a><\/strong>
\n Saurabh Verma (IT-BHU), Estevam Hruschka<\/em><\/p>\n

Policy iteration based on a learned transition model<\/a><\/strong>
\n Vivek Ramavajjala (UC San Diego), Charles Elkan (University of California San Diego)<\/em><\/p>\n

 <\/p>\n

Wed3B: Sensor Data
\n16:10 – 17:25<\/h4>\n

MDL-based Analysis of Time Series at Multiple Time-Scales<\/a><\/strong>
\n Ugo Vespier (LIACS), Arno Knobbe (Universiteit Leiden), Siegfried Nijssen (Katholieke Universiteit Leuven, Belgium), Joaquin Vanschoren<\/em><\/p>\n

Separable Approximate Optimization of Support Vector Machines for Distributed Sensing<\/a><\/strong>
\n Sangkyun Lee (TU Dortmund), Marco Stolpe (TU Dortmund), Katharina Morik (University of Dortmund)<\/em><\/p>\n

Unsupervised inference of auditory attention from biosensors<\/a><\/strong>
\n Melih Kandemir (Aalto University), Arto Klami (Aalto University), Akos Vetek (Nokia Research Center, Finland), Samuel Kaski (Aalto University)<\/em><\/p>\n

 <\/p>\n

Wed3C: Privacy and Security
\n16:10 – 17:25<\/h4>\n

AUDIO: An Integrity Auditing Framework of Outlier-Mining-as-a-Service Systems<\/a><\/strong>
\n 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)<\/em><\/p>\n

Differentially Private Projected Histograms: Construction and Use for Prediction<\/a><\/strong>
\n Staal Vinterbo (UCSD)<\/em><\/p>\n

Fairness-aware Classifier with Prejudice Remover Regularizer<\/a><\/strong>
\n Toshihiro Kamishima (National Institute of Advanced Industrial Science and Technology), Shotaro Akaho (AIST), Hideki Asoh (AIST), Jun Sakuma (University of Tsukuba\u00a0 and Japan Science and Technology Agency)<\/em><\/p>\n

 <\/p>\n

Thursday 27th September<\/h2>\n

Thu1A: Social Network Mining II
\n10:30 – 12:10<\/h4>\n

On Approximation of Real-World Influence Spread<\/a><\/strong>
\n Yu Yang (University of Science and Technology of China), Enhong Chen (USTC), Qi Liu (USTC), Biao Xiang (USTC), Tong Xu (USTC), Shafqat Shad (USTC)<\/em><\/p>\n

Opinion Formation by Voter Model with Temporal Decay Dynamics<\/a><\/strong>
\n Masahiro Kimura (Ryukoku University), Kazumi Saito (University of Shizuoka), Kouzou Ohara (Aoyama Gakuin University), Hiroshi Motoda (Osaka University)<\/em><\/p>\n

Viral Marketing for Product Cross-sell through Social Networks<\/a><\/strong>
\n Ramasuri\u00a0 Narayanam (IBM Research), Amit\u00a0 Nanavati (IBM Reseach, India)<\/em><\/p>\n

Which Topic will You Follow?<\/a><\/strong>
\n 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<\/em><\/p>\n

 <\/p>\n

Thu1B: Rule Learning and Subgroup Discovery
\n10:30 – 12:10<\/h4>\n

A Bayesian approach for classification rule mining in quantitative databases<\/a><\/strong>
\n Dominique Gay (Orange Labs), Marc Boulle (Orange)<\/em><\/p>\n

A Bayesian Scoring Technique for Mining Predictive and Non-Spurious Rules<\/a><\/strong>
\n Iyad Batal (University of Pittsburgh), Gregory Cooper (University of Pittsburgh), Milos Hauskrecht (University of Pittsburgh)<\/em><\/p>\n

Generic Pattern Trees for Exhaustive Exceptional Model Mining<\/a><\/strong>
\n Florian Lemmerich (University of Wuerzburg), Martin Becker (University of Wuerzburg), Martin Atzmueller (University of Kassel)<\/em><\/p>\n

Handling Time Changing Data with Adaptive Very Fast Decision Rules<\/a><\/strong>
\n Petr Kosina (LIAAD-INESC Porto), Joao Gama (University of Porto)<\/em><\/p>\n

 <\/p>\n

Thu1C: Multi-Task and Transfer Learning I
\n10:30 – 12:10<\/h4>\n

Efficient Training of Graph-Regularized Multitask SVMs<\/a><\/strong>
\n Christian Widmer (Memorial Sloan-Kettering Cancer Center), Marius Kloft (TU Berlin), Nico G\u00f6rnitz (TU Berlin), Gunnar R\u00e4tsch (Memorial Sloan-Kettering Cancer Center)<\/em><\/p>\n

Geometry Preserving Multi-task Metric Learning<\/a><\/strong>
\n 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)<\/em><\/p>\n

Learning and Inference in Probabilistic Classifier Chains with Beam Search<\/a><\/strong>
\n Abhishek Kumar (UC San Diego), Shankar Vembu (University of Toronto), Aditya Menon (UC San Diego), Charles Elkan (UC San Diego)<\/em><\/p>\n

Learning Multiple Tasks with Boosted Decision Trees<\/a><\/strong>
\n Jean-Baptiste Faddoul (Inria Lille), Boris Chidlovskii (XRCE), Remi Gilleron (University of Lille 3), Fabien Torre<\/em><\/p>\n

 <\/p>\n

Thu1D: Industry Track: Start-up Stories
\n10:30 – 12:10<\/h4>\n

Who is buying what in the UK? Mining e-commerce data<\/strong>
\n Jurgen Van Gael (Rangespan)<\/em><\/p>\n

Automated real-time intelligent marketing<\/strong>
\n Jason McFall (Causata)<\/em><\/p>\n

 <\/p>\n

Thu2A: Graphs, Trees, Sequences and Strings II
\n14:00 – 15:40<\/h4>\n

An Efficiently Computable Support Measure for Frequent Subgraph Pattern Mining<\/a><\/strong>
\nYuyi Wang (Katholieke Universiteit Leuven), Jan Ramon (K.U. Leuven)<\/em><\/p>\n

Efficient Graph Kernels by Randomization<\/a><\/strong>
\nMarion Neumann (Fraunhofer IAIS), Novi Patricia (Fraunhofer IAIS), Roman Garnett (Carnegie Mellon University), Kristian Kersting (University of Bonn)<\/em><\/p>\n

Graph Mining for Object Tracking in Videos<\/a><\/strong>
\nFabien 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)<\/em><\/p>\n

Hypergraph Learning with Hyperedge Expansion<\/a><\/strong>
\nLi Pu (\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne), Boi Faltings (EPFL)<\/em><\/p>\n

 <\/p>\n

Thu2B: Classification
\n14:00 – 15:40<\/h4>\n

A Note on Extending Generalization Bounds for Binary Large-margin Classifiers to Multiple Classes<\/a><\/strong>
\nUeruen Dogan (University of Potsdam), Tobias Glasmachers (Institut f\u00fcr Neuroinformatik), Christian Igel (University of Copenhagen)<\/em><\/p>\n

Extension of the Rocchio Classification Method to Multi-modal Categorization of Documents in Social Media<\/a><\/strong>
\nAmin Mantrach (Yahoo! Research Barcelona), Jean-Michel Renders (XRCE)<\/em><\/p>\n

Label-noise Robust Logistic Regression and Its Applications<\/a><\/strong>
\nJakramate\u00a0 Bootkrajang (University of Birmingham), Ata Kaban (University of Birmingham)<\/em><\/p>\n

Sentiment Classification with Supervised Sequence Encoder<\/a><\/strong>
\nDmitriy Bespalov (Drexel University), Yanjun Qi (NEC Research), Bing Bai (NEC Research), Ali Shokoufandeh (Drexel University)<\/em><\/p>\n

 <\/p>\n

Thu2C: Multi-Task and Transfer Learning II
\n14:00 – 15:40<\/h4>\n

Discriminative Factor Alignment across Heterogeneous Feature Space<\/a><\/strong>
\nFangwei Hu (Shanghai Jiao Tong University), Tianqi Chen, Nathan Liu, Qiang Yang (Hong Kong University of Science and Technologu), Yong Yu<\/em><\/p>\n

Learning to Perceive Two-Dimensional Displays Using Probabilistic Grammars<\/a><\/strong>
\nNan Li (Carnegie Mellon University), William Cohen, Kenneth Koedinger<\/em><\/p>\n

Sparse Gaussian Processes for Multi-task Learning<\/a><\/strong>
\nYuyang Wang (Tufts University), Roni Khardon (Tufts University)<\/em><\/p>\n

Transfer Spectral Clustering<\/a><\/strong>
\n Wenhao Jiang (HK PolyU), Fu-lai Chung<\/em><\/p>\n

 <\/p>\n

Thu2D: Industry Track: Startup Stories
\n14:00 – 15:40<\/h4>\n

Somebody needs your algorithm – Cloud’N’Sci.fi<\/strong>
\n Petri Myllymaki (University of Helsinki, Ekahau, Cloud’N’Sci) and Pauli Misikangas (Ekahau, Cloud’N’Sci)<\/em><\/p>\n

Machine Learning at PeerIndex: Telling stories about users and their influence<\/strong>
\n Ferenc Huszar (Peerindex)<\/em>
\n<\/em><\/p>\n

 <\/p>\n

Thu3A: Large-Scale, Distributed and Parallel Mining and Learning II
\n16:10 – 17:50<\/h4>\n

CC-MR – Finding Connected Components in Huge Graphs with MapReduce<\/a><\/strong>
\nThomas Seidl (RWTH Aachen University), Brigitte Boden (RWTH Aachen University), Sergej Fries (RWTH Aachen University)<\/em><\/p>\n

Fast Near Neighbor Search in High-Dimensional Binary Data<\/a><\/strong>
\nAnshumali\u00a0 Shrivastav, Ping Li<\/em><\/p>\n

Fully Sparse Topic Models<\/a><\/strong>
\nKhoat Than (Japan Advanced Institute of Science and Technology), Tu-Bao Ho (JAIST)<\/em><\/p>\n

Massively Parallel Feature Selection: An Approach Based on Variance Preservation<\/a><\/strong>
\nZheng Zhao (SAS), James Cox (SAS Inc), David Duling (SAS Inc), Warren Sarle (SAS Inc)<\/em><\/p>\n

 <\/p>\n

Thu3B: Natural Language Processing
\n16:10 – 17:50<\/h4>\n

Collective Information Extraction with Context-specific Consistencies<\/a><\/strong>
\nPeter Kluegl (University of Wuerzburg), Martin Toepfer (University of Wuerzburg), Florian Lemmerich (University of Wuerzburg), Andreas Hotho (University of W\u00fcrzburg), Frank Puppe (University of Wuerzburg)<\/em><\/p>\n

Supervised Learning of Semantic Relatedness<\/a><\/strong>
\nDavid Yanay (Technion), Ran El-Yaniv (Technion)<\/em><\/p>\n

Unsupervised Bayesian Part of Speech Inference with Particle Gibbs<\/a><\/strong>
\nGregory Dubbin (Individual Purchaser), Phil Blunsom (University of Oxford)<\/em><\/p>\n

WikiSent : Weakly Supervised Sentiment Analysis Through Extractive Summarization With Wikipedia<\/a><\/strong>
\nSubhabrata Mukherjee (IIT Bombay), Pushpak Bhattacharyya (IIT Bombay)<\/em><\/p>\n

 <\/p>\n

Thu3C: Reinforcement Learning and Planning II
\n16:10 – 17:50<\/h4>\n

Bootstrapping Monte Carlo Tree Search with an Imperfect Heuristic<\/a><\/strong>
\nTruong-Huy Nguyen (National University of Singapore), Wee-Sun Lee (National Uni. of Singapore), Tze-Yun Leong (National Uni. of Singapore)<\/em><\/p>\n

Fast Reinforcement Learning with Large Action Sets using Error-Correcting Output Codes for MDP Factorization<\/a><\/strong>
\nGabriel Dulac-Arnold (LIP6-UPMC), Ludovic Denoyer (LIP6-UPMC), Philippe Preux (INRIA-LIFL), Patrick Gallinari (Universit\u00e9 Pierre et Marie Curie, France)<\/em><\/p>\n

Learning Policies For Battery Usage Optimization in Electric Vehicles<\/a><\/strong>
\nStefano Ermon (Cornell University), Yexiang Xue (Cornell University), Carla Gomes (Cornell University), Bart Selman (Cornell University)<\/em><\/p>\n

Structured Apprenticeship Learning<\/a><\/strong>
\nAbdeslam Boularias (MPI for Intelligent Systems), Oliver Kroemer (TU Darmstadt), Jan Peters (TU Darmstadt)<\/em><\/p>\n

 <\/p>\n

Thu3D: Industry Track: Startup Stories
\n16:10 – 17:50<\/h4>\n

\u201cChoice is good, choosing is a chore\u201d \u2013 choosing the right database platform for effective Knowledge Discovery<\/strong>
\n Alastair Page (JustOneDB)<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

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 […]<\/p>\n","protected":false},"author":160,"featured_media":0,"parent":151,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"pages.php","meta":[],"_links":{"self":[{"href":"https:\/\/ecmlpkdd.blogs.bristol.ac.uk\/wp-json\/wp\/v2\/pages\/14"}],"collection":[{"href":"https:\/\/ecmlpkdd.blogs.bristol.ac.uk\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/ecmlpkdd.blogs.bristol.ac.uk\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/ecmlpkdd.blogs.bristol.ac.uk\/wp-json\/wp\/v2\/users\/160"}],"replies":[{"embeddable":true,"href":"https:\/\/ecmlpkdd.blogs.bristol.ac.uk\/wp-json\/wp\/v2\/comments?post=14"}],"version-history":[{"count":45,"href":"https:\/\/ecmlpkdd.blogs.bristol.ac.uk\/wp-json\/wp\/v2\/pages\/14\/revisions"}],"predecessor-version":[{"id":889,"href":"https:\/\/ecmlpkdd.blogs.bristol.ac.uk\/wp-json\/wp\/v2\/pages\/14\/revisions\/889"}],"up":[{"embeddable":true,"href":"https:\/\/ecmlpkdd.blogs.bristol.ac.uk\/wp-json\/wp\/v2\/pages\/151"}],"wp:attachment":[{"href":"https:\/\/ecmlpkdd.blogs.bristol.ac.uk\/wp-json\/wp\/v2\/media?parent=14"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}