MLJ 2019

82 papers

2D Compressed Learning: Support Matrix Machine with Bilinear Random Projections Di Ma, Songcan Chen
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A Distributed Feature Selection Scheme with Partial Information Sharing Aida Brankovic, Luigi Piroddi
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A Flexible Probabilistic Framework for Large-Margin Mixture of Experts Archit Sharma, Siddhartha Saxena, Piyush Rai
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A Greedy Feature Selection Algorithm for Big Data of High Dimensionality Ioannis Tsamardinos, Giorgos Borboudakis, Pavlos Katsogridakis, Polyvios Pratikakis, Vassilis Christophides
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A Riemannian Gossip Approach to Subspace Learning on Grassmann Manifold Bamdev Mishra, Hiroyuki Kasai, Pratik Jawanpuria, Atul Saroop
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A Scalable Robust and Automatic Propositionalization Approach for Bayesian Classification of Large Mixed Numerical and Categorical Data Marc Boullé, Clément Charnay, Nicolas Lachiche
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A Scalable Sparse Cholesky Based Approach for Learning High-Dimensional Covariance Matrices in Ordered Data Kshitij Khare, Sang-Yun Oh, Syed Rahman, Bala Rajaratnam
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A Simple Homotopy Proximal Mapping Algorithm for Compressive Sensing Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu, Zhi-Hua Zhou
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Accelerated Gradient Boosting Gérard Biau, Benoît Cadre, Laurent Rouvìère
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Aggregating Algorithm for Prediction of Packs Dmitry Adamskiy, Anthony Bellotti, Raisa Dzhamtyrova, Yuri Kalnishkan
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Algorithms for Learning Parsimonious Context Trees Ralf Eggeling, Ivo Grosse, Mikko Koivisto
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An Accelerated Variance Reducing Stochastic Method with Douglas-Rachford Splitting Jingchang Liu, Linli Xu, Shuheng Shen, Qing Ling
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Annotation Cost-Sensitive Active Learning by Tree Sampling Yu-Lin Tsou, Hsuan-Tien Lin
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Arbitrage of Forecasting Experts Vítor Cerqueira, Luís Torgo, Fábio Pinto, Carlos Soares
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Asymptotically Optimal Algorithms for Budgeted Multiple Play Bandits Alexander Luedtke, Emilie Kaufmann, Antoine Chambaz
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Attentional Multilabel Learning over Graphs: A Message Passing Approach Kien Do, Truyen Tran, Thin Nguyen, Svetha Venkatesh
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Automatic Face Recognition with Well-Calibrated Confidence Measures Charalambos Eliades, Ladislav Lenc, Pavel Král, Harris Papadopoulos
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Bayesian Optimistic Kullback-Leibler Exploration Kanghoon Lee, Geon-hyeong Kim, Pedro A. Ortega, Daniel D. Lee, Kee-Eung Kim
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Boosting as a Kernel-Based Method Aleksandr Y. Aravkin, Giulio Bottegal, Gianluigi Pillonetto
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CaDET: Interpretable Parametric Conditional Density Estimation with Decision Trees and Forests Cyrus Cousins, Matteo Riondato
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Collaborative Topic Regression for Predicting Topic-Based Social Influence Asso Hamzehei, Raymond K. Wong, Danai Koutra, Fang Chen
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Combination of Inductive Mondrian Conformal Predictors Paolo Toccaceli, Alexander Gammerman
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Compatible Natural Gradient Policy Search Joni Pajarinen, Hong Linh Thai, Riad Akrour, Jan Peters, Gerhard Neumann
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Constructing Effective Personalized Policies Using Counterfactual Inference from Biased Data Sets with Many Features Onur Atan, William R. Zame, Qiaojun Feng, Mihaela van der Schaar
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Continuation Methods for Approximate Large Scale Object Sequencing Xenophon Evangelopoulos, Austin J. Brockmeier, Tingting Mu, John Yannis Goulermas
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Correction to: Adaptive Random Forests for Evolving Data Stream Classification Heitor Murilo Gomes, Albert Bifet, Jesse Read, Jean Paul Barddal, Fabrício Enembreck, Bernhard Pfahringer, Geoff Holmes, Talel Abdessalem
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Correction to: Modeling Outcomes of Soccer Matches Alkeos Tsokos, Santhosh Narayanan, Ioannis Kosmidis, Gianluca Baio, Mihai Cucuringu, Gavin Whitaker, Franz J. Király
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Corruption-Tolerant Bandit Learning Sayash Kapoor, Kumar Kshitij Patel, Purushottam Kar
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Covariance-Based Dissimilarity Measures Applied to Clustering Wide-Sense Stationary Ergodic Processes Qidi Peng, Nan Rao, Ran Zhao
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Data Scarcity, Robustness and Extreme Multi-Label Classification Rohit Babbar, Bernhard Schölkopf
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Deep Collective Matrix Factorization for Augmented Multi-View Learning Ragunathan Mariappan, Vaibhav Rajan
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Distributed Bayesian Matrix Factorization with Limited Communication Xiangju Qin, Paul Blomstedt, Eemeli Leppäaho, Pekka Parviainen, Samuel Kaski
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Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations Balázs Csanád Csáji, Krisztián Balázs Kis
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Dolores: A Model That Predicts Football Match Outcomes from All over the World Anthony C. Constantinou
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Dynamic Attention-Integrated Neural Network for Session-Based News Recommendation Lemei Zhang, Peng Liu, Jon Atle Gulla
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Dynamic Principal Projection for Cost-Sensitive Online Multi-Label Classification Hong-Min Chu, Kuan-Hao Huang, Hsuan-Tien Lin
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Efficient and Robust TWSVM Classification via a Minimum L1-Norm Distance Metric Criterion He Yan, Qiaolin Ye, Dong-Jun Yu
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Efficient Feature Selection Using Shrinkage Estimators Konstantinos Sechidis, Laura Azzimonti, Adam Craig Pocock, Giorgio Corani, James Weatherall, Gavin Brown
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Efficient Learning with Robust Gradient Descent Matthew J. Holland, Kazushi Ikeda
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Efficient Venn Predictors Using Random Forests Ulf Johansson, Tuve Löfström, Henrik Linusson, Henrik Boström
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Engineering Fast Multilevel Support Vector Machines Ehsan Sadrfaridpour, Talayeh Razzaghi, Ilya Safro
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Extreme Value Correction: A Method for Correcting Optimistic Estimations in Rule Learning Martin Mozina, Janez Demsar, Ivan Bratko, Jure Zabkar
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Fast Generalization Rates for Distance Metric Learning Han-Jia Ye, De-Chuan Zhan, Yuan Jiang
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Good Arm Identification via Bandit Feedback Hideaki Kano, Junya Honda, Kentaro Sakamaki, Kentaro Matsuura, Atsuyoshi Nakamura, Masashi Sugiyama
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Grouped Gaussian Processes for Solar Power Prediction Astrid Dahl, Edwin V. Bonilla
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Improved Linear Embeddings via Lagrange Duality Kshiteej Sheth, Dinesh Garg, Anirban Dasgupta
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Improving Latent Variable Descriptiveness by Modelling Rather than Ad-Hoc Factors Alex Mansbridge, Roberto Fierimonte, Ilya Feige, David Barber
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Incorporating Domain Knowledge in Machine Learning for Soccer Outcome Prediction Daniel Berrar, Philippe Lopes, Werner Dubitzky
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Joint Detection of Malicious Domains and Infected Clients Paul Prasse, René Knaebel, Lukás Machlica, Tomás Pevný, Tobias Scheffer
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Learning Efficient Logic Programs Andrew Cropper, Stephen H. Muggleton
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Learning Rates for Kernel-Based Expectile Regression Muhammad Farooq, Ingo Steinwart
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Learning to Predict Soccer Results from Relational Data with Gradient Boosted Trees Ondrej Hubácek, Gustav Sourek, Filip Zelezný
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Lifted Discriminative Learning of Probabilistic Logic Programs Arnaud Nguembang Fadja, Fabrizio Riguzzi
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Lowest Probability Mass Neighbour Algorithms: Relaxing the Metric Constraint in Distance-Based Neighbourhood Algorithms Kai Ming Ting, Ye Zhu, Mark J. Carman, Yue Zhu, Takashi Washio, Zhi-Hua Zhou
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LSALSA: Accelerated Source Separation via Learned Sparse Coding Benjamin Cowen, Apoorva Nandini Saridena, Anna Choromanska
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Majority Vote Ensembles of Conformal Predictors Giovanni Cherubin
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Millionaire: A Hint-Guided Approach for Crowdsourcing Bo Han, Quanming Yao, Yuangang Pan, Ivor W. Tsang, Xiaokui Xiao, Qiang Yang, Masashi Sugiyama
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Modeling Outcomes of Soccer Matches Alkeos Tsokos, Santhosh Narayanan, Ioannis Kosmidis, Gianluca Baio, Mihai Cucuringu, Gavin Whitaker, Franz J. Király
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N-Ary Decomposition for Multi-Class Classification Joey Tianyi Zhou, Ivor W. Tsang, Shen-Shyang Ho, Klaus-Robert Müller
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Nonparametric Predictive Distributions Based on Conformal Prediction Vladimir Vovk, Jieli Shen, Valery Manokhin, Min-ge Xie
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Nuclear Discrepancy for Single-Shot Batch Active Learning Tom J. Viering, Jesse H. Krijthe, Marco Loog
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On PAC-Bayesian Bounds for Random Forests Stephan Sloth Lorenzen, Christian Igel, Yevgeny Seldin
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On the Analysis of Adaptability in Multi-Source Domain Adaptation Ievgen Redko, Amaury Habrard, Marc Sebban
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Online Aggregation of Unbounded Losses Using Shifting Experts with Confidence Vladimir V'yugin, Vladimir G. Trunov
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Online Probabilistic Theory Revision from Examples with ProPPR Victor Guimarães, Aline Paes, Gerson Zaverucha
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Probabilistic and Exact Frequent Subtree Mining in Graphs Beyond Forests Pascal Welke, Tamás Horváth, Stefan Wrobel
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Probabilistic Movement Models and Zones of Control Ulf Brefeld, Jan Lasek, Sebastian Mair
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RankMerging: A Supervised Learning-to-Rank Framework to Predict Links in Large Social Networks Lionel Tabourier, Daniel Faria Bernardes, Anne-Sophie Libert, Renaud Lambiotte
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Rethinking Statistical Learning Theory: Learning Using Statistical Invariants Vladimir Vapnik, Rauf Izmailov
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Risk Bound of Transfer Learning Using Parametric Feature Mapping and Its Application to Sparse Coding Wataru Kumagai, Takafumi Kanamori
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Semi-Supervised Online Structure Learning for Composite Event Recognition Evangelos Michelioudakis, Alexander Artikis, Georgios Paliouras
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Speculate-Correct Error Bounds for K-Nearest Neighbor Classifiers Eric Bax, Lingjie Weng, Xu Tian
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Stochastic Gradient Hamiltonian Monte Carlo with Variance Reduction for Bayesian Inference Zhize Li, Tianyi Zhang, Shuyu Cheng, Jun Zhu, Jian Li
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Supervised Representation Learning for Multi-Label Classification Ming Huang, Fuzhen Zhuang, Xiao Zhang, Xiang Ao, Zhengyu Niu, Min-Ling Zhang, Qing He
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TD-Regularized Actor-Critic Methods Simone Parisi, Voot Tangkaratt, Jan Peters, Mohammad Emtiyaz Khan
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Temporal Pattern Attention for Multivariate Time Series Forecasting Shun-Yao Shih, Fan-Keng Sun, Hung-Yi Lee
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The Kernel Kalman Rule - Efficient Nonparametric Inference by Recursive Least-Squares and Subspace Projections Gregor H. W. Gebhardt, Andras Gabor Kupcsik, Gerhard Neumann
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The Open International Soccer Database for Machine Learning Werner Dubitzky, Philippe Lopes, Jesse Davis, Daniel Berrar
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The Risk of Trivial Solutions in Bipartite Top Ranking Aditya Krishna Menon
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The Teaching Size: Computable Teachers and Learners for Universal Languages Jan Arne Telle, José Hernández-Orallo, Cèsar Ferri
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Unsupervised Feature Selection Based on Kernel Fisher Discriminant Analysis and Regression Learning Ronghua Shang, Yang Meng, Chiyang Liu, Licheng Jiao, Amir Masoud Ghalamzan Esfahani, Rustam Stolkin
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Visualizing and Understanding Sum-Product Networks Antonio Vergari, Nicola Di Mauro, Floriana Esposito
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