See below for the main-track parallel sessions on Tuesday, Wednesday, and Thursday. The journal track papers (marked with*) have 25 minutes, the regular papers have 20 minutes, both including discussion.
Tuesday 24th September
Tue1A: Reinforcement Learning
Chair: Pascal Poupart
11:00 – 12:20
| Learning graph-based representations for continuous reinforcement learning domains |
| Jan Hendrik Metzen |
| (poster stand #1) |
| Greedy confidence pursuit: A pragmatic approach to multi-bandit optimization |
| Philip Bachman and Doina Precup |
| (poster stand #2) |
| Exploiting multi-step sample trajectories for approximate value iteration |
| Robert Wright, Lei Yu, Steven Loscalzo, and Philip Dexter |
| (poster stand #3) |
| 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 |
| (poster stand #4) |
Tue1B: Networks (1)
Chair: Francesco Bonchi
11:00 – 12:25
| What Distinguish One from Its Peers in Social Networks?* |
| Yi-Chen Lo, Jhao-Yin Li, Mi-Yen Yeh, Shou-De Lin, and Jian Pei |
| (poster stand #5) Implementation |
| Detecting bicliques in GF[q] |
| Jan Ramon, Pauli Miettinen, and Jilles Vreeken |
| (poster stand #6) |
| As Strong as the Weakest Link: Mining Diverse Cliques in Weighted Graphs |
| Petko Bogdanov, Ben Baumer, Prithwish Basu, Amotz Bar-Noy, and Ambuj Singh |
| (poster stand #7) |
| How robust is the core of a network? |
| Abhijin Adiga and Anil Vullikanti |
| (poster stand #8) |
Tue1C: Privacy and Security
Chair: Stan Matwin
11:00 – 12:25
| Differential Privacy Based on Importance Weighting* |
| Zhanglong Ji and Charles Elkan |
| (poster stand #9) |
| Anonymizing data with relational and transaction attributes |
| Giorgos Poulis, Grigorios Loukides, Aris Gkoulalas-Divanis, and Spiros Skiadopoulos |
| (poster stand #10) |
| Privacy-preserving mobility monitoring using sketches of stationary sensor readings |
| Michael Kamp, Christine Kopp, Michael Mock, Mario Boley, and Michael May |
| (poster stand #11) |
| 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 |
| (poster stand #12) Implementation |
Tue1D: Ranking and Recommender Systems
Chair: Katharina Morik
11:00 – 12:25
| Growing a List* |
| Benjamin Letham, Cynthia Rudin, and Katherine A Heller |
| (poster stand #13) |
| A pairwise label ranking method with imprecise scores and partial predictions |
| Sebastien Destercke |
| (poster stand #14) |
| Learning Socially Optimal Information Systems from Egoistic Users |
| Karthik Raman and Thorsten Joachims |
| (poster stand #15) |
| Socially Enabled Preference Learning from Implicit feedback data |
| Julien Delporte, Alexandros Karatzoglou, Tomasz Matuszczyk, and Stephane Canu |
| (poster stand #16) |
Tue2A: Markov Decision Processes
Chair: Franz Pernkopf
14:15 – 15:55
| Expectation maximization for average reward decentralized POMDPs |
| Joni Pajarinen and Jaakko Peltonen |
| (poster stand #17) Implementation |
| Properly acting under partial observability with action feasibility constraints |
| Caroline Carvalho Chanel and Florent Teichteil-Konigsbuch |
| (poster stand #18) |
| Iterative model refinement of recommender MDPs based on expert feedback |
| Omar Khan, Pascal Poupart, and John Mark Agosta |
| (poster stand #19) |
| Solving relational MDPs with exogenous events and additive rewards |
| Saket Joshi, Roni Khardon, Prasad Tadepalli, Aswin Raghavan, and Alan Fern |
| (poster stand #20) |
| Continuous upper confidence trees with polynomial exploration-consistency |
| Adrien Couetoux, David Auger, and Olivier Teytaud |
| (poster stand #21) |
Tue2B: Tensor Analysis & Dimensionality Reduction
Chair: Charles Elkan
14:15 – 15:55
| An analysis of tensor models for learning on structured data |
| Maximilian Nickel and Volker Tresp |
| (poster stand #22) |
| Learning modewise independent components from tensor data using multilinear mixing model |
| Haiping Lu |
| (poster stand #23) |
| Embedding with Autoencoder Regularization |
| Wenchao Yu, Guangxiang Zeng, Ping Luo, Fuzhen Zhuang, Qing He, and Zhongzhi Shi |
| (poster stand #24) |
| Learning Exemplar-Represented Manifolds in Latent Space for Classification |
| Shu Kong and Donghui Wang |
| (poster stand #25) |
| Locally Linear Landmarks for Large-Scale Manifold Learning |
| Max Vladymyrov and Miguel Carreira-Perpinan |
| (poster stand #26) Implementation |
Tue2C: Biomedical Applications
Chair: Jesse Davis
14:15 – 15:55
| Forest-Based Point Process for Event Prediction from Electronic Health Records |
| Jeremy Weiss, Michael Caldwell, and David Page |
| (poster stand #27) |
| On Discovering the Correlated Relationship between Static and Dynamic Data in Clinical Gait Analysis |
| Yin Song, Jian Zhang, Longbing Cao, and Morgan Sangeux |
| (poster stand #28) |
| Computational Drug Repositioning by Ranking and Integrating Multiple Data Sources |
| Ping Zhang, Pankaj Agarwal, and Zoran Obradovic |
| (poster stand #29) |
| 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 |
| (poster stand #30) |
| Protein Function Prediction using Dependence Maximization |
| Guoxian Yu, Carlotta Demoniconi, Huzefa Rangwala, and Guoji Zhang |
| (poster stand #31) |
Tue2D: Demo spotlights
Chair: Joaquin Vanschooren and Andreas Hotho
14:15 – 15:55
Tue3A: Inverse RL & RL Applications
Chair: Kurt Driessens
16:25 – 18:05
| A cascaded supervised learning approach to inverse reinforcement learning |
| Edouard Klein, Bilal Piot, Matthieu Geist, and Olivier Pietquin |
| (poster stand #32) |
| Learning from demonstrations: is it worth estimating a reward function? |
| Bilal Piot, Matthieu Geist, and Olivier Pietquin |
| (poster stand #33) |
| Recognition of agents based on observation of their sequential behavior |
| Qifeng Qiao and Peter Beling |
| (poster stand #34) |
| Learning throttle valve control using policy search |
| Bastian Bischoff, Duy Nguyen-Tuong, Torsten Koller, Heiner Markert, and Alois Knoll |
| (poster stand #35) |
| Model-selection for non-parametric function approximation in continuous control systems: A case study in a smart energy system |
| Daniel Urieli and Peter Stone |
| (poster stand #36) |
Tue3B: Matrix Analysis
Chair: Pauli Miettinen
16:25 – 18:05
| Noisy matrix completion using alternating minimization |
| Suriya Gunasekar, Ayan Acharya, Neeraj Gaur, and Joydeep Ghosh |
| (poster stand #37) |
| A nearly unbiased matrix completion approach |
| Dehua Liu, Tengfei Zhou, Hui Qian, Congfu Xu, and Zhihua Zhang |
| (poster stand #38) |
| A counterexample for the validity of using nuclear norm as a convex surrogate of rank |
| Hongyang Zhang, Zhouchen Lin, and Chao Zhang |
| (poster stand #39) |
| Efficient rank-one residue approximation method for graph regularized non-negative matrix factorization |
| Qing LIAO and Qian Zhang |
| (poster stand #40) |
| Maximum entropy models for iteratively identifying subjectively interesting structure in real-valued data |
| Kleanthis-Nikolaos Kontonasios, Jilles Vreeken,and Tijl De Bie |
| (poster stand #41) |
Tue3C: Applications
Chair: Bernhard Pfahringer
16:25 – 18:05
| Incremental Sensor Placement Optimization on Water Network |
| Xiaomin Xu, Yiqi Lu, Sheng Huang, Yanghua Xiao, and Wei Wang |
| (poster stand #42) |
| Detecting Marionette Microblog Users for Improved Information Credibility |
| Xian Wu, Ziming Feng, Wei Fan, Jing Gao, and Yong Yu |
| (poster stand #43) |
| Will my Question be Answered? Predicting “Question Answerability” in Community Question-Answering Sites |
| Gideon Dror, Yoelle Maarek, and Idan Szpektor |
| (poster stand #44) |
| Learning to Detect Patterns of Crime |
| Tong Wang, Cynthia Rudin, Dan Wagner, and Rich Sevieri |
| (poster stand #45) |
| Space Allocation in the Retail Industry: A Decision Support System Integrating Evolutionary Algorithms and Regression Models |
| Fabio Pinto and Carlos Soares |
| (poster stand #46) |
Tue3D: Semi-supervised Learning
Chair: Thomas Gärtner
16:25 – 18:05
| Exploratory Learning |
| Bhavana Dalvi, William Cohen, and Jamie Callan |
| (poster stand #47) |
| Semi-supervised Gaussian Process Ordinal Regression |
| Srijith P. K., Shirish Shevade, and Sundararajan S. |
| (poster stand #48) Implementation |
| Influence of Graph Construction on Semi-supervised Learning |
| Celso Andre De Sousa, Gustavo Batista, and Solange Rezende |
| (poster stand #49) |
| Tractable Semi-Supervised Learning of Complex Structured Prediction Models |
| Kai-Wei Chang, Sundararajan S., and Sathiya Keerthi |
| (poster stand #50) |
| PSSDL: Probabilistic semi-supervised dictionary learning |
| Behnam Babagholami-Mohamadabadi, Ali Zarghami, Mohammadreza Zolfaghari, and Mahdieh Soleymani Baghshah |
| (poster stand #51) |
Wednesday 25th September
Wed1A: Nectar (1)
Chair: Rosa Meo and Michele Sebag
10:30 – 12:10
| 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 |
Wed1B: Active Learning and Optimization
Chair: Andrea Passerini
10:30 – 12:10
| A Lipschitz Exploration-Exploitation Scheme for Bayesian Optimization |
| Ali Jalali, Javad Azimi, Xiaoli Fern, and Ruofei Zhang |
| (poster stand #1) |
| Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration |
| Emile Contal, David Buffoni, Alexandre Robicquet, and Nicolas Vayatis |
| (poster stand #2) Implementation |
| Regret bounds for reinforcement learning with policy advice |
| Mohammad Azar, Alessandro Lazaric, and Emma Brunskill |
| (poster stand #3) |
| A time and space efficient algorithm for contextual linear bandits |
| Jose Bento, Stratis Ioannidis, S Muthukrishnan, and Jinyun Yan |
| (poster stand #4) |
| Knowledge transfer for multi-labeler active learning |
| Meng Fang, Jie Yin, and Xingquan Zhu |
| (poster stand #5) |
Wed1C: Networks (2)
Chair: Jan Ramon
10:30 – 11:55
| ABACUS: Frequent Pattern Mining Based Community Discovery in Multidimensional Networks* |
| Michele Berlingerio, Fabio Pinelli, and Francesco Calabrese |
| (poster stand #6) |
| Discovering Nested Communities |
| Nikolaj Tatti and Aristides Gionis |
| (poster stand #7) |
| CSI: Community-level Social Influence analysis |
| Yasir Mehmood, Nicola Barbieri, Francesco Bonchi, and Antti Ukkonen |
| (poster stand #8) |
| Community Distribution Outlier Detection in Heterogeneous Information Networks |
| Manish Gupta, Jing Gao, and Jiawei Han |
| (poster stand #9) Implementation |
Wed1D: Structured Output, Multi-task
Chair: Manfred Jaeger
10:30 – 12:10
| Taxonomic prediction with tree-structured covariances |
| Matthew Blaschko, Wojciech Zaremba, and Arthur Gretton |
| (poster stand #10) Implementation |
| Position preserving multi-output prediction |
| Zubin Abraham and Pang-Ning Tan |
| (poster stand #11) |
| Structured output learning with candidate labels for local parts |
| Chengtao Li, Jianwen Zhang, and Zheng Chen |
| (poster stand #12) |
| Shared structure learning for multiple tasks with multiple views |
| Xin Jin, Fuzhen Zhuang, Shuhui Wang, Qing He, and Zhongzhi Shi |
| (poster stand #13) Implementation |
| Using both latent and supervised shared topics for multi-task learning |
| Ayan Acharya, Aditya Rawal, Eduardo Hruschka, and Raymond Mooney |
| (poster stand #14) |
Wed2A: Nectar (2)
Chair: Rosa Meo and Michele Sebag
14:00 – 15:40
| A theoretical framework for exploratory data mining: recent insights and challenges ahead |
| Tijl De Bie and Eirini Spyropoulou |
| Tensor Factorization for Multi-Relational Learning |
| Maximilian Nickel and Volker Tresp |
| MONIC – Modeling and Monitoring Cluster Transitions |
| Myra Spiliopoulou, Eirini Ntoutsi, Yannis Theodoridis, and Rene Schult |
Wed2B: Models for Sequential Data
Chair: Eyke Hüllermeier
14:00 – 15:40
| Spectral learning of sequence taggers over continuous sequences |
| Ariadna Quattoni and Adria Recasens |
| (poster stand #15) |
| Fast variational bayesian linear state-space model |
| Jaakko Luttinen |
| (poster stand #16) Implementation |
| Inhomogeneous parsimonious Markov models |
| Ralf Eggeling, Andre Gohr, Pierre-Yves Bourguignon, Edgar Wingender, and Ivo Grosse |
| (poster stand #17) |
| Future locations prediction with uncertain data |
| Disheng Qiu, Paolo Papotti, and Blanco Lorenzo |
| (poster stand #18) |
| Modeling short-term energy load with continuous conditional random fields |
| Hongyu Guo |
| (poster stand #19) |
Wed2C: Graph Mining
Chair: Toon Calders
14:00 – 15:25
| Activity Preserving Graph Simplification* |
| Francesco Bonchi, Gianmarco De Francisci Morales, Aristides Gionis, and Antti Ukkonen |
| (poster stand #20) |
| A Fast Approximation of the Weisfeiler-Lehman Graph Kernel for RDF Data |
| Gerben De Vries |
| (poster stand #21) Implementation |
| Improving relational classification using link prediction techniques |
| Cristina Perez-Sola and Jordi Herrera-Joancomarti |
| (poster stand #22) |
| Efficient Frequent Connected Induced Subgraph Mining in Graphs of Bounded Treewidth |
| Tamas Horvath, Keisuke Otaki, and Jan Ramon |
| (poster stand #23) |
Wed2D: Natural Language Processing & Probabilistic Models
Chair:
14:00 – 15:40
| Supervised Learning of Syntactic Contexts for Uncovering Definitions and Extracting Hypernym Relations in Text Databases |
| Guido Boella and Luigi Di Caro |
| (poster stand #24) |
| Error Prediction With Partial Feedback |
| William Darling, Guillaume Bouchard, Shachar Mirkin, and Cedric Archambeau |
| (poster stand #25) |
| Boot-Strapping Language Identifiers for Short Colloquial Postings |
| Moises Goldszmidt, Marc Najork, and Stelios Paparizos |
| (poster stand #26) Implementation |
| A bayesian classifier for learning from tensorial data |
| Wei Liu, Jeffrey Chan, James Bailey, Christopher Leckie, Fang Chen, and Rao Kotagiri |
| (poster stand #27) |
| Prediction with model based neutrality |
| Kazuto Fukuchi, Jun Sakuma, and Toshihiro Kamishima |
| (poster stand #28) |
Wed3A: Subgroup Discovery & Streams
Chair: Nada Lavrac
16:10 – 17:50
| Discovering skylines of subgroup sets |
| Matthijs van Leeuwen and Antti Ukkonen |
| (poster stand #29) |
| Difference-based estimates for generalization-aware subgroup discovery |
| Florian Lemmerich, Martin Becker, and Frank Puppe |
| (poster stand #30) |
| Fast and exact mining of probabilistic data streams |
| Reza Akbarinia and Florent Masseglia |
| (poster stand #31) |
| Pitfalls in benchmarking data stream classification and how to avoid them |
| Albert Bifet, Jesse Read, Indre Zliobaite, Bernhard Pfahringer, and Geoff Holmes |
| (poster stand #32) |
| Adaptive model rules from high-speed data streams |
| Ezilda Almeida, Carlos Ferreira, and Joao Gama |
| (poster stand #33) |
Wed3B: Multi-label Classification & Outlier Detection
Chair: Luis Torgo
16:10 – 17:50
| Multi-label classification with output kernels |
| Yuhong Guo and Dale Schuurmans |
| (poster stand #34) |
| Probabilistic clustering for hierarchical multi-label classification of protein functions |
| Rodrigo Barros, Ricardo Cerri, Alex Freitas, and Andre Carvalho |
| (poster stand #35) |
| Mining outlier participants: insights using directional distributions in latent models |
| Didi Surian and Sanjay Chawla |
| (poster stand #36) |
| Anomaly detection in vertically partitioned data by distributed core vector machines |
| Marco Stolpe, Kanishka Bhaduri, Kamalika Das, and Katharina Morik |
| (poster stand #37) |
| Local outlier detection with interpretation |
| Xuan-Hong Dang, Barbora Micenkova, Ira Assent, and Raymond T. Ng |
| (poster stand #38) |
Wed3C: Ensembles
Chair: Geoff Holmes
16:10 – 17:50
| Boosting for unsupervised domain adaptation |
| Amaury Habrard, Jean-Philippe Peyrache, and Marc Sebban |
| (poster stand #39) |
| AR-Boost: Reducing Overfitting by a Robust Data-Driven Regularization Strategy |
| Baidya Nath Saha, Gautam Kunapuli, Nilanjan Ray, Joseph Maldjian, and Sriraam Natarajan |
| (poster stand #40) |
| Parallel Boosting with Momentum |
| Indraneel Mukherjee, Yoram Singer, Rafael Frongillo, and Kevin Canini |
| (poster stand #41) |
| Inner Ensembles: Using Ensemble Methods in the Learning Phase |
| Houman Abbasian, Chris Drummond, Nathalie Japkowicz, and Stan Matwin |
| (poster stand #42) |
| Mixtures of Large Margin Nearest Neighbor Classifiers |
| Murat Semerci, and Ethem Alpaydin |
| (poster stand #43) |
Wed3D: Bayesian Learning
Chair: Roni Khardon
16:10 – 17:35
| A Comparative Evaluation of Stochastic-based Inference Methods for Gaussian Process Models* |
| Maurizio Filippone, Mingjun Zhong, and Mark Girolami |
| (poster stand #44) |
| Decision-theoretic Sparsification for Gaussian Process Preference Learning |
| Ehsan Abbasnejad, Edwin Bonilla, and Scott Sanner |
| (poster stand #45) |
| Variational Hidden Conditional Random Fields with Coupled Dirichlet Process Mixtures |
| Konstantinos Bousmalis, Stefanos Zafeiriou, Louis-Philippe Morency, Maja Pantic, and Zoubin Ghahramani |
| (poster stand #46) |
| Sparsity in Bayesian Blind Source Separation and Deconvolution |
| Vaclav Smidl and Ondrej Tichy |
| (poster stand #47) |
Thursday 26th September
Thu1A: Industrial track (1)
Chair: Bernhard Pfahringe
11:00 – 12:25
| Some of the Problems and Applications of Opinion Analysis |
| Hugo Zaragoza |
| Machine Learning in a large diversified Internet Group |
| Jean-Paul Schmetz |
Thu1B: Sequential Pattern Mining
Chair: Donata Malerba
11:00 – 12:20
| Itemset Based Sequence Classification |
| Cheng Zhou, Boris Cule, and Bart Goethals |
| (poster stand #48) Implementation |
| A relevance criterion for sequential patterns |
| Henrik Grosskreutz, Bastian Lang, and Daniel Trabold |
| (poster stand #49) |
| A fast and simple method for mining subsequences with surprising event counts |
| Jefrey Lijffijt |
| (poster stand #50) |
| Relevant subsequence detection with sparse dictionary learning |
| Sam Blasiak, Huzefa Rangwala,and Kathryn Laskey |
| (poster stand #51) |
Thu1C: Graphical Models
Chair: Sangkyun Lee
11:00 – 12:25
| Spatio-Temporal Random Fields: Compressible Representation and Distributed Estimation* |
| Nico Piatkowski, Sangkyun Lee, and Katharina Morik |
| (poster stand #52) Implementation |
| Knowledge intensive learning: combining qualitative constraints with causal independence for parameter learning in probabilistic models |
| Shuo Yang and Sriraam Natarajan |
| (poster stand #53) |
| Direct learning of sparse changes in Markov networks by density ratio ratio estimation |
| Song Liu, John Quinn, Michael Gutmann, and Masashi Sugiyama |
| (poster stand #54) Implementation |
| Greedy part-wise learning of sum-product networks |
| Robert Peharz, Bernhard Geiger, and Franz Pernkopf |
| (poster stand #55) |
Thu1D: Unsupervised Learning
Chair: Volker Tresp
11:00 – 12:25
| A Framework for Semi-Supervised and Unsupervised Optimal Extraction of Clusters from Hierarchies* |
| Ricardo J.G.B. Campello, Davoud Moulavi, Arthur Zimek, and Jorg Sander |
| (poster stand #56) |
| Minimal Shrinkage for Noisy Data Recovery |
| Deguang Kong and Chris Ding |
| (poster stand #57) |
| Reduced-Rank Local Distance Metric Learning |
| Yinjie Huang, Cong Li, Michael Georgiopoulos, and Georgios Anagnostopoulos |
| (poster stand #58) |
| Cross-Domain Recommendation via Cluster-Level Latent Factor Model |
| Sheng Gao, Hao Luo, Da Chen, Shantao Li, Patrick Gallinar, and Jun Guo |
| (poster stand #59) |
Thu2A: Industrial track (2)
Chair: Thomas Gärtner
14:15 – 15:40
| Bayesian Learning in Online Service: Statistics Meets Systems |
| Ralf Herbrich |
| ML and Business: A Love-Hate Relationship |
| Andreas Antrup |
Thu2B: Dynamic Graphs
Chair: Myra Spiliopoulou
14:15 – 15:35
| Incremental Local Evolutionary Outlier Detection for Dynamic Social Networks |
| Tengfei Ji, Jun Gao, and Dongqing Yang |
| (poster stand #60) |
| Continuous Similarity Computation over Streaming Graphs |
| Elena Valari and Apostolos Papadopoulos |
| (poster stand #61) |
| Trend Mining in Dynamic Attributed Graphs |
| Elise Desmier, Marc Plantevit, Celine Robardet, and Jean-Francois Boulicaut |
| (poster stand #62) |
| How long will she call me? Distribution, Social Theory and Duration Prediction |
| Yuxiao Dong, Jie Tang, Tiancheng Lou, Bin Wu, and Nitesh Chawla |
| (poster stand #63) Implementation |
Thu2C: Statistical Learning (1)
Chair: Paolo Frasconi
14:15 – 15:40
| Block coordinate descent algorithms for large-scale sparse multiclass classification* |
| Mathieu Blondel, Kazuhiro Seki, and Kuniaki Uehara |
| (poster stand #64) Implementation |
| MORD: Multi-class classifier for Ordinal Regression |
| Konstiantyn Antoniuk, Vojtech Franc, and Vaclav Hlavac |
| (poster stand #65) |
| Identifiability of Model Properties in Over-Parameterized Model Classes |
| Manfred Jaeger |
| (poster stand #66) |
| Multi-core structural SVM training |
| Kai-Wei Chang, Vivek srikumar, and Dan Roth |
| (poster stand #67) |
Thu2D: Evaluation & kNN
Chair: Johannes Furnkranz
14:15 – 15:40
| ROC Curves in Cost Space* |
| Cesar Ferri, Jose Hernandez-Orallo, and Peter Flach |
| (poster stand #68) |
| Area Under the Precision-Recall Curve: Point Estimates and Confidence Intervals |
| (A typo appearing in the published paper is corrected in the linked pdf) |
| Kendrick Boyd, Kevin Eng, and David Page |
| (poster stand #69) |
| Hub Co-occurrence Modeling for Robust High-dimensional kNN Classification |
| Nenad Tomasev and Dunja Mladenic |
| (poster stand #70) |
| Fast kNN Graph Construction with Locality Sensitive Hashing |
| Yan-Ming Zhang, Kaizhu Huang, Guanggang Geng, and Cheng-Lin Liu |
| (poster stand #71) |
Thu3A: Sequence & Time Series Analysis
Chair: Mark Last
16:10 – 17:35
| Fast Sequence Segmentation using Log-Linear Models* |
| Nikolaj Tatti |
| (poster stand #72) |
| Explaining interval sequences by randomization |
| Andreas Henelius, Jussi Korpela, and Kai Puolamaki |
| (poster stand #73) |
| A layered Dirichlet process for hierarchical segmentation of sequential grouped data |
| Adway Mitra, Ranganath B.N., and Indrajit Bhattacharya |
| (poster stand #74) |
| Fault tolerant regression for sensor data |
| Indre Zliobaite and Jaakko Hollmen |
| (poster stand #75) |
Thu3B: Declarative Data Mining & Meta Learning
Chair: Luc De Raedt
16:10 – 17:35
| Pairwise Meta-Rules for Better Meta-Learning-Based Algorithm Ranking* |
| Quan Sun and Bernhard Pfahringer |
| (poster stand #76) |
| SNNAP: Solver-based nearest neighbor for algorithm portfolios |
| Marco Collautti, Yuri Malitsky, and Barry O’Sullivan |
| (poster stand #77) Implementation |
| Top-k frequent closed item set mining using top-k-SAT problem |
| Said Jabbour, Lakhdar Sais, and Yakoub Salhi |
| (poster stand #78) |
| A declarative framework for constrained clustering |
| Thi-Bich-Hanh Dao, Khanh-Chuong Duong, and Christel Vrain |
| (poster stand #79) |
Thu3C: Topic Models
Chair: Edwin Bonilla
16:10 – 17:35
| Probabilistic Topic Models for Sequence Data* |
| Nicola Barbieri, Antonio Bevacqua, Marco Carnuccio, Giuseppe Manco, and Ettore Ritacco |
| (poster stand #80) |
| Nested hierarchical Dirichlet processes for nonparametric entity-topic analysis |
| Priyanka Agrawal, Lavanya Tekumalla, and Indrajit Bhattacharya |
| (poster stand #81) |
| From topic models to semi-supervised learning: biased mixed-membership models to exploit topic-indicative features in entity clustering |
| Ramnath Balasubramanyan, William Cohen, and Bhavana Dalvi |
| (poster stand #82) |
| Sparse relational topic models for document networks |
| Aonan Zhang, Jun Zhu, and Bo Zhang |
| (poster stand #83) |
Thu3D: Statistical Learning (2)
Chair: Dale Schuurmans
16:10 – 17:35
| The flip-the-state transition operator for restricted Boltzmann machines* |
| Kai Brugge, Asja Fischer, and Christian Igel |
| (poster stand #84) |
| Learning Discriminative Sufficient Statistics Score Space |
| Xiong Li, Bin Wang, Yuncai Liu, and Tai Sing Lee |
| (poster stand #85) |
| The stochastic gradient descent for the primal L1-SVM optimization revisited |
| Constantinos Panagiotakopoulos and Petroula Tsampouka |
| (poster stand #86) Implementation |
| Bundle CDN: A Highly Parallelized Approach for Large-scale L1-regularized Logistic Regression |
| Yatao Bian, Xiong Li, mingqi Cao, and Yuncai Liu |
| (poster stand #87) |
