Accepted Papers

Articles accepted for the journal track:

Fast Sequence Segmentation using Log-Linear Models
Nikolaj Tatti
ROC Curves in Cost Space
Cesar Ferri, Jose Hernandez-Orallo, and Peter Flach
A Framework for Semi-Supervised and Unsupervised Optimal Extraction of Clusters from Hierarchies
Ricardo J.G.B. Campello, Davoud Moulavi, Arthur Zimek, and Jörg Sander
Pairwise Meta-Rules for Better Meta-Learning-Based Algorithm Ranking
Quan Sun and Bernhard Pfahringer
Block coordinate descent algorithms for large-scale sparse multiclass classification
Mathieu Blondel, Kazuhiro Seki, and Kuniaki Uehara
A Comparative Evaluation of Stochastic-based Inference Methods for Gaussian Process Models
Maurizio Filippone, Mingjun Zhong, and Mark Girolami
Probabilistic Topic Models for Sequence Data
Nicola Barbieri,  Antonio Bevacqua, Marco Carnuccio,  Giuseppe Manco, and Ettore Ritacco
The flip-the-state transition operator for restricted Boltzmann machines
Kai Brügge, Asja Fischer, and Christian Igel
Differential Privacy Based on Importance Weighting
Zhanglong Ji and Charles Elkan
Activity Preserving Graph Simplification
Francesco Bonchi, Gianmarco De Francisci Morales, Aristides Gionis, and Antti Ukkonen
ABACUS: Frequent Pattern Mining Based Community Discovery in Multidimensional Networks
Michele Berlingerio, Fabio Pinelli, and Francesco Calabrese
Growing a List
Benjamin Letham, Cynthia Rudin, and Katherine A Heller
What Distinguish One from Its Peers in Social Networks?
Yi-Chen Lo, Jhao-Yin Li, Mi-Yen Yeh, Shou-De Lin, and Jian Pei
Spatio-Temporal Random Fields: Compressible Representation and Distributed Estimation
Nico Piatkowski, Sangkyun Lee, and Katharina Morik

Articles accepted for the nectar track:

Towards Robot Skill Learning: From Simple Skills to Table Tennis
Jan Peters, Katharina Muelling, Jens Kober, Oliver Kroemer, and Gerhard Neumann
Functional MRI Analysis with Sparse Models
Irina Rish
Tensor Factorization for Multi-Relational Learning
Maximilian Nickel and Volker Tresp
A theoretical framework for exploratory data mining: recent insights and challenges ahead
Tijl De Bie and Eirini Spyropoulou
MONIC and follows-up on Modeling and Monitoring Cluster Transitions
Myra Spiliopoulou, Eirini Ntoutsi, Yannis Theodoridis, and Rene Schult

Articles accepted for the demo track:

Image Hub Explorer: Evaluating Representations and Metrics for Content-based Image Retrieval and Object Recognition
Nenad Tomasev and Dunja Mladenic
Ipseity – A Laboratory for Synthesizing and Validating Artificial Cognitive Systems in Multi-Agent Systems
Fabrice Lauri, Nicolas Gaud, Stéphane Galland, and Vincent Hilaire
OpenML: A Collaborative Science Platform
Jan Van Rijn, Bernd Bischl, Luis Torgo,Bo Gao, ; Venkatesh Umaashankar, Simon Fischer, Patrick Winter,Bernd Wiswedel, Michael Berthold, and Joaquin Vanschoren
ViperCharts: Visual Performance Evaluation Platform
Borut Sluban and Nada Lavrac
Targeted Linked Hypernym Discovery: real-time classification of entities in text with Wikipedia
Milan Dojchinovski and Tomáš Kliegr
Hermoupolis: A Trajectory Generator for Simulating Generalized Mobility Patterns
Nikos Pelekis, Christos Ntrigkogias, Panagiotis Tampakis, Stylianos Sideridis, and Yannis Theodoridis
AllAboard: a system for exploring urban mobility and optimizing public transport using cellphone data
Michele Berlingerio, Francesco Calabrese, Giusy Di Lorenzo, Rahul Nair, Fabio Pinelli, and Marco Sbodio
ScienScan – efficient visualization and browsing tool for academic search.
Daniil Mirylenka and Andrea Passerini
InVis: A Tool for Interactive Visual Data Analysis
Daniel Paurat and Thomas Gaertner
Kanopy: Analysing the Semantic Network around Document Topics
Ioana Hulpus, Conor Hayes, Marcel Karnstedt, Derek Greene, and Marek Jozwowicz
SCCQL: a Constraint-based Clustering System
Antoine Adam, Hendrik Blockeel, Sander Govers, and Abram Aertsen

Articles accepted for the proceedings track:

A Relevance Criterion for Sequential Patterns
Henrik Grosskreutz, Bastian Lang, and Daniel Trabold
Using Both Latent and Supervised Shared Topics for Multitask Learning
Ayan Acharya, Aditya Rawal, Eduardo Hruschka, and Raymond Mooney
Mixtures of Large Margin Nearest Neighbor Classifiers
Murat Semerci, and Ethem Alpaydin
Exploratory Learning
Bhavana Dalvi, William Cohen, and Jamie Callan
Learning Exemplar-Represented Manifolds in Latent Space for Classification
Shu Kong and Donghui Wang
Incremental Sensor Placement Optimization on Water Network
Xiaomin Xu, Yiqi Lu, Sheng Huang, Yanghua Xiao, and Wei Wang
Hub Co-occurrence Modeling for Robust High-dimensional kNN Classification
Nenad Tomasev and Dunja Mladenic
Noisy Matrix Completion Using Alternating Minimization
Suriya Gunasekar, Ayan Acharya, Neeraj Gaur and Joydeep Ghosh
Community Distribution Outlier Detection in Heterogeneous Information Networks
Manish Gupta, Jing Gao, and Jiawei Han
How robust is the core of a network?
Abhijin Adiga and Anil Vullikanti
Mining Outlier Participants: Insights Using Directional Distributions in Latent Models
Didi Surian and Sanjay Chawla
A Layered Dirichlet Process for Hierarchical Segmentation of Sequential Grouped Data
Adway Mitra, Ranganath B.N., and Indrajit Bhattacharya
Knowledge Transfer for Multi-Labeler Active Learning
Meng Fang, Jie Yin, and Xingquan Zhu
A fast and simple method for mining subsequences with surprising event counts
Jefrey Lijffijt
Supervised Learning of Syntactic Contexts for Uncovering Definitions and Extracting Hypernym Relations in Text Databases
Guido Boella and Luigi Di Caro
Incremental Local Evolutionary Outlier Detection for Dynamic Social Networks
Tengfei Ji, Jun Gao, and Dongqing Yang
Forest-Based Point Process for Event Prediction from Electronic Health Records
Jeremy Weiss, Michael Caldwell, and David Page
Locally Linear Landmarks for Large-Scale Manifold Learning
Max Vladymyrov and Miguel Carreira-Perpinan
Top-k Frequent Closed Itemset Mining Using Top-k SAT Problem
Said Jabbour, Lakhdar Sais, and Yakoub Salhi
Relevant Subsequence Detection with Sparse Dictionary Learning
Sam Blasiak, Huzefa Rangwala,and Kathryn Laskey
From Topic Models to Semi-Supervised Learning: Biasing Mixed-membership Models to Exploit Topic-Indicative Features in Entity Clustering
Ramnath Balasubramanyan, William Cohen, and Bhavana Dalvi
Learning Throttle Valve Control Using Policy Search
Bastian Bischoff, Duy Nguyen-Tuong, Torsten Koller, Heiner Markert, and Alois Knoll
A Lipschitz Exploration-Exploitation Scheme for Bayesian Optimization
Ali Jalali, Javad Azimi, Xiaoli Fern, and Ruofei Zhang
Explaining Interval Sequences by Randomization
Andreas Henelius, Jussi Korpela, and Kai Puolamaki
Fault tolerant regression for sensor data
Indre Zliobaite, and Jaakko Hollmén
Pitfalls in benchmarking data stream classification and how to avoid them
Albert Bifet, Jesse Read, Indre Zliobaite, Bernhard Pfahringer, and Geoff Holmes
Embedding with Autoencoder Regularization
Wenchao Yu, Guangxiang Zeng, Ping Luo, Fuzhen Zhuang, Qing He, and Zhongzhi Shi
Boosting for Unsupervised Domain Adaptation
Amaury Habrard, Jean-Philippe Peyrache, and Marc Sebban
SNNAP: Solver-based Nearest Neighbor for Algorithm Portfolios
Marco Collautti, Yuri Malitsky, and Barry O’Sullivan
Fast Variational Bayesian Linear State-Space Model
Jaakko Luttinen
Discovering Skylines of Subgroup Sets
Matthijs van Leeuwen and Antti Ukkonen
A Counterexample for the Validity of Using Nuclear Norm as a Convex Surrogate of Rank
Hongyang Zhang, Zhouchen Lin, and Chao Zhang
A Nearly Unbiased Matrix Completion Approach
Dehua Liu, Tengfei Zhou, Hui Qian, Congfu Xu, and Zhihua Zhang
Local Outlier Detection with Interpretation
Xuan-Hong Dang, Barbora Micenkova, Ira Assent, and Raymond T. Ng
Semi-supervised Gaussian Process Ordinal Regression
Srijith P. K., Shirish Shevade, and Sundararajan S.
Detecting Marionette Microblog Users for Improved Information Credibility
Xian Wu, Ziming Feng, Wei Fan, Jing Gao, and Yong Yu
Knowledge Intensive Learning: Combining Qualitative Constraints with Causal Independence for Parameter Learning in Probabilistic Models
Shuo Yang and Sriraam Natarajan
Expectation maximization for average reward decentralized POMDPs
Joni Pajarinen and Jaakko Peltonen
How long will she call me? Distribution
Yuxiao Dong, Jie Tang, Tiancheng Lou, Bin Wu, and Nitesh Chawla
Position Preserving Multi-Output Prediction
Zubin Abraham and Pang-Ning Tan
Anomaly Detection in Vertically Partitioned Data by Distributed Core Vector Machines
Marco Stolpe, Kanishka Bhaduri, Kamalika Das, and Katharina Morik
‎PSSDL : Probabilistic Semi-Supervised Dictionary Learning
Behnam Babagholami-Mohamadabadi, Ali Zarghami, Mohammadreza Zolfaghari, and Mahdieh Soleymani Baghshah
A declarative framework for Constrained Clustering
Thi-Bich-Hanh Dao, Khanh-Chuong Duong, and Christel Vrain
Adaptive Model Rules from Data Streams
Ezilda Almeida, Carlos Ferreira, and Joao Gama
The Stochastic Gradient Descent for the Primal L1-SVM Optimization Revisited
Constantinos Panagiotakopoulos and Petroula Tsampouka
Prediction with Model-based Neutrality
Kazuto Fukuchi, Jun Sakuma, and Toshihiro Kamishima
Discovering Nested Communities
Nikolaj Tatti and Aristides Gionis
Sparse Relational Topic Models for Document Networks
Aonan Zhang, Jun Zhu, and Bo Zhang
Learning Socially Optimal Information Systems from Egoistic Users
Karthik Raman and Thorsten Joachims
Greedy Confidence Pursuit: A Pragmatic Approach to Multi-Bandit Optimization
Philip Bachman and Doina Precup
Protein Function Prediction using Dependence Maximization
Guoxian Yu, Carlotta Demoniconi, Huzefa Rangwala, and Guoji Zhang
Solving Relational MDPs with Exogenous Events and Additive Rewards
Saket Joshi, Roni Khardon, Prasad Tadepalli, Aswin Raghavan, and Alan Fern
Nested Hierarchical Dirichlet Process for Nonparametric Entity-Topic Analysis
Priyanka Agrawal, Lavanya Tekumalla, and Indrajit Bhattacharya
Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation
Song Liu, John Quinn, Michael Gutmann, and Masashi Sugiyama
AR-Boost: Reducing Overfitting by a Robust Data-Driven Regularization Strategy
Baidya Nath Saha, Gautam Kunapuli, Nilanjan Ray, Joseph Maldjian, and Sriraam Natarajan
Decision-theoretic Sparsification for Gaussian Process Preference Learning
Ehsan Abbasnejad, Edwin Bonilla, and Scott Sanner
An Analysis of Tensor Models for Learning on Structured Data
Maximilian Nickel and Volker Tresp
Inhomogeneous Parsimonious Markov Models
Ralf Eggeling, Andre Gohr, Pierre-Yves Bourguignon, Edgar Wingender, and Ivo Grosse
Fast and Exact Mining of Probabilistic Data Streams
Reza Akbarinia and Florent Masseglia
Learning Modewise Independent Components from Tensor Data Using Multilinear Mixing Model
Haiping Lu
Shared Structure Learning for Multiple Tasks with Multiple Views
Xin Jin, Fuzhen Zhuang, Shuhui Wang, Qing He, and Zhongzhi Shi
Probabilistic Clustering for Hierarchical Multi-Label Classification of Protein Functions
Rodrigo Barros, Ricardo Cerri, Alex Freitas, and Andre Carvalho
Variational Hidden Conditional Random Fields with Coupled Dirichlet Process Mixtures
KONSTANTINOS BOUSMALIS, Stefanos Zafeiriou, Louis-Philippe Morency, Maja Pantic, and Zoubin Ghahramani
Identifiability of Model Properties in Over-Parameterized Model Classes
Manfred Jaeger
Will my Question be Answered? Predicting “Question Answerability” in Community Question-Answering Sites
Gideon Dror, Yoelle Maarek, and Idan Szpektor
Continuous Similarity Computation over Streaming Graphs
Elena Valari and Apostolos Papadopoulos
Improving relational classification using link prediction techniques
Cristina Pérez-Solà and Jordi Herrera-Joancomartí
Trend Mining in Dynamic Attributed Graphs
Elise Desmier, Marc Plantevit, Celine Robardet and Jean-Francois Boulicaut
Properly Acting under Partial Observability with Action Feasibility Constraints
Caroline Carvalho Chanel and Florent Teichteil-Königsbuch
Sparsity in Bayesian Blind Source Separation and Deconvolution
Václav Šmídl and Ondřej Tichý
Learning from demonstrations: Is it worth estimating a reward function?
Bilal Piot, Matthieu Geist, and Olivier Pietquin
On Discovering the Correlated Relationship between Static and Dynamic Data in Clinical Gait Analysis
Yin Song, Jian Zhang, Longbing Cao, and Morgan Sangeux
Efficient Frequent Connected Induced Subgraph Mining in Graphs of Bounded Treewidth
Tamas Horvath, Keisuke Otaki, and Jan Ramon
CSI: Community-level Social Influence analysis
Yasir Mehmood, Nicola Barbieri, Francesco Bonchi, and Antti Ukkonen
Socially Enabled Preference Learning from Implicit feedback data
Julien Delporte, Alexandros Karatzoglou, Tomasz Matuszczyk, and Stephane Canu
Bundle CDN: A Highly Parallelized Approach for Large-scale L1-regularized Logistic Regression
Yatao Bian, Xiong Li, mingqi Cao, and Yuncai Liu
Cross-Domain Recommendation via Cluster-Level Latent Factor Model
Sheng Gao, Hao Luo, Da Chen, Shantao Li, Patrick Gallinar, and Jun Guo
Difference-based Estimates for Generalization-Aware Subgroup Discovery
Florian Lemmerich, Martin Becker, and Frank Puppe
Structured Output Learning with Candidate Labels for Local Parts
Chengtao Li, Jianwen Zhang, and Zheng Chen
Error Prediction With Partial Feedback
William Darling, Guillaume Bouchard, shachar Mirkin, and Cedric Archambeau
Regret Bounds for Reinforcement Learning with Policy Advice
Mohammad Azar, Alessandro Lazaric, and Emma Brunskill
Continuous Upper Confidence Trees with Polynomial Exploration – Consistency
Adrien Couetoux, David Auger, Olivier Teytaud
Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration
Emile Contal, David Buffoni, Alexandre Robicquet, and Nicolas Vayatis
A Fast Approximation of the Weisfeiler-Lehman Graph Kernel for RDF Data
Gerben De Vries
Recognition of Agents based on Observation of Their Sequential Behavior
Qifeng Qiao and Peter Beling
Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling
Houssam Nassif, Finn Kuusisto, Elizabeth Burnside, David Page, Jude Shavlik, and Vitor Santos Costa
Model-Selection for Non-Parametric Function Approximation in Continuous Control Problems: A Case Study in a Smart Energy System
Daniel Urieli and Peter Stone
Reduced-Rank Local Distance Metric Learning
Yinjie Huang, Cong Li, Michael Georgiopoulos, and Georgios Anagnostopoulos
A Time and Space Efficient Algorithm for Contextual Linear Bandits
Jose Bento, Stratis Ioannidis, S Muthukrishnan, and Jinyun Yan
Greedy Part-Wise Learning of Sum-Product Networks
Robert Peharz, Bernhard Geiger, and Franz Pernkopf
Privacy-Preserving Mobility Monitoring using Sketches of Stationary Sensor Readings
Michael Kamp, Christine Kopp, Michael Mock, Mario Boley, and Michael May
Learning to Detect Patterns of Crime
Tong Wang, Cynthia Rudin, Dan Wagner, and Rich Sevieri
Influence of Graph Construction on Semi-supervised Learning
Celso André De Sousa, Gustavo Batista, and Solange Rezende
Detecting Bicliques in GF[q]
Jan Ramon, Pauli Miettinen, and Jilles Vreeken
Area Under the Precision-Recall Curve: Point Estimates and Confidence Intervals
Kendrick Boyd, Kevin Eng, and David Page
Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Srndic, Pavel Laskov, Giorgio Giacinto, and Fabio Roli
Parallel Boosting with Momentum
Indraneel Mukherjee, Yoram Singer, Rafael Frongillo, and Kevin Canini
MORD: Multi-class classifier for Ordinal Regression
Konstiantyn Antoniuk, Vojtech Franc, and Vaclav Hlavac
Exploiting Multi-Step Sample Trajectories for Approximate Value Iteration
Robert Wright, Lei Yu, Steven Loscalzo, and Philip Dexter
Tractable Semi-Supervised Learning of Complex Structured Prediction Models
Kai-Wei Chang, Sundararajan S., and Sathiya Keerthi
Multi-core Structural SVM Training
Kai-Wei Chang, Vivek srikumar, and Dan Roth
Multi-label Classification with Output Kernels
Yuhong Guo and Dale Schuurmans
Boot-Strapping Language Identifiers for Short Colloquial Postings
Moises Goldszmidt, Marc Najork, and Stelios Paparizos
Space Allocation in the Retail Industry: A Decision Support System Integrating Evolutionary Algorithms and Regression Models
Fábio Pinto and Carlos Soares
Efficient Rank-one Residue Approximation Method for Graph Regularized Non-negative Matrix Factorization
Qing LIAO and Qian Zhang
Computational Drug Repositioning by Ranking and Integrating Multiple Data Sources
Ping Zhang, Pankaj Agarwal, and Zoran Obradovic
Minimal Shrinkage for Noisy Data Recovery
Deguang Kong and Chris Ding
Iterative Model Refinement of Recommender MDPs based on Expert Feedback
Omar Khan, Pascal Poupart, and John Mark Agosta
Fast kNN Graph Construction with Locality Sensitive Hashing
Yan-Ming Zhang, Kaizhu Huang, Guanggang Geng, and Cheng-Lin Liu
Automatically Mapped Transfer Between Reinforcement Learning Tasks via Three-Way Restricted Boltzmann Machines
Haitham Bou Ammar, Decebal Constantin Mocanu, Matthew Taylor, Kurt Driessens, Gerhard Weiss, and Karl Tuyls
Inner Ensembles: Using Ensemble Methods in the Learning Phase
HOUMAN ABBASIAN, Chris Drummond, Nathalie Japkowicz, and Stan Matwin
A pairwise label ranking method with imprecise scores and partial predictions
Sebastien Destercke
A cascaded supervised learning approach to inverse reinforcement learning
Edouard Klein, Bilal Piot, Matthieu Geist, and Olivier Pietquin
Future Locations Prediction with Uncertain Data
Disheng Qiu, Paolo Papotti, and Blanco Lorenzo
Spectral Learning of Sequence Taggers over Continuos Sequences
Ariadna Quattoni and Adria Recasens
Learning Discriminative Sufficient Statistics Score Space
Xiong Li, Bin Wang, Yuncai Liu, and Tai Sing Lee
Modeling Short-term Energy Load with Continuous Conditional Random Fields
Hongyu Guo
Learning Graph-based Representations for Continuous Reinforcement Learning Domains
Jan Hendrik Metzen
Maximum Entropy Models for Iteratively Identifying Subjectively Interesting Structure in Real-Valued Data
Kleanthis-Nikolaos Kontonasios, Jilles Vreeken, and Tijl De Bie
Itemset Based Sequence Classification
Cheng Zhou, Boris Cule, and Bart Goethals
As Strong as the Weakest Link: Mining Diverse Cliques in Weighted Graphs
Petko Bogdanov, Ben Baumer, Prithwish Basu, Amotz Bar-Noy, and Ambuj Singh
Taxonomic Prediction with Tree-Structured Covariances
Matthew Blaschko, Wojciech Zaremba, and Arthur Gretton
Anonymizing data with relational and transaction attributes
Giorgos Poulis, Grigorios Loukides, Aris Gkoulalas-Divanis, and Spiros Skiadopoulos
A Bayesian Classifier for Learning from Tensorial Data
Wei Liu, Jeffrey Chan, James Bailey, Christopher Leckie, Fang Chen, and Rao Kotagiri

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