JMLR Volume 13
Distance Metric Learning with Eigenvalue Optimization
Yiming Ying, Peng Li abs ][pdf ][bib ]
Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection
Gavin Brown, Adam Pocock, Ming-Jie Zhao, Mikel Luján abs ][pdf ][bib ]
Plug-in Approach to Active Learning
Stanislav Minsker abs ][pdf ][bib ]
Refinement of Operator-valued Reproducing Kernels
Haizhang Zhang, Yuesheng Xu, Qinghui Zhang abs ][pdf ][bib ]
An Active Learning Algorithm for Ranking from Pairwise Preferences with an Almost Optimal Query Complexity
Nir Ailon abs ][pdf ][bib ]
Optimal Distributed Online Prediction Using Mini-Batches
Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, Lin Xiao abs ][pdf ][bib ]
Active Clustering of Biological Sequences
Konstantin Voevodski, Maria-Florina Balcan, Heiko Röglin, Shang-Hua Teng, Yu Xia abs ][pdf ][bib ]
Multi Kernel Learning with Online-Batch Optimization
Francesco Orabona, Luo Jie, Barbara Caputo abs ][pdf ][bib ]
Active Learning via Perfect Selective Classification
Ran El-Yaniv, Yair Wiener abs ][pdf ][bib ]
Random Search for Hyper-Parameter Optimization
James Bergstra, Yoshua Bengio abs ][pdf ][bib ]
Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics
Michael U. Gutmann, Aapo Hyvärinen abs ][pdf ][bib ]
Bounding the Probability of Error for High Precision Optical Character Recognition
Gary B. Huang, Andrew Kae, Carl Doersch, Erik Learned-Miller abs ][pdf ][bib ]
Minimax-Optimal Rates For Sparse Additive Models Over Kernel Classes Via Convex Programming
Garvesh Raskutti, Martin J. Wainwright, Bin Yu abs ][pdf ][bib ]
Online Learning in the Embedded Manifold of Low-rank Matrices
Uri Shalit, Daphna Weinshall, Gal Chechik abs ][pdf ][bib ]
Multi-Assignment Clustering for Boolean Data
Mario Frank, Andreas P. Streich, David Basin, Joachim M. Buhmann abs ][pdf ][bib ]
Eliminating Spammers and Ranking Annotators for Crowdsourced Labeling Tasks
Vikas C. Raykar, Shipeng Yu abs ][pdf ][bib ]
Metric and Kernel Learning Using a Linear Transformation
Prateek Jain, Brian Kulis, Jason V. Davis, Inderjit S. Dhillon abs ][pdf ][bib ]
MULTIBOOST: A Multi-purpose Boosting Package
Djalel Benbouzid, Róbert Busa-Fekete, Norman Casagrande, François-David Collin, Balázs Kégl (Machine Learning Open Source Software Paper) abs ][pdf ][bib ]
[code ]
ML-Flex: A Flexible Toolbox for Performing Classification Analyses In Parallel
Stephen R. Piccolo, Lewis J. Frey (Machine Learning Open Source Software Paper) abs ][pdf ][bib ]
[code ]
A Primal-Dual Convergence Analysis of Boosting
Matus Telgarsky abs ][pdf ][bib ]
Non-Sparse Multiple Kernel Fisher Discriminant Analysis
Fei Yan, Josef Kittler, Krystian Mikolajczyk, Atif Tahir abs ][pdf ][bib ]
Learning Algorithms for the Classification Restricted Boltzmann Machine
Hugo Larochelle, Michael Mandel, Razvan Pascanu, Yoshua Bengio abs ][pdf ][bib ]
Structured Sparsity and Generalization
Andreas Maurer, Massimiliano Pontil abs ][pdf ][bib ]
A Case Study on Meta-Generalising: A Gaussian Processes Approach
Grigorios Skolidis, Guido Sanguinetti abs ][pdf ][bib ]
A Kernel Two-Sample Test
Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander Smola abs ][pdf ][bib ]
GPLP: A Local and Parallel Computation Toolbox for Gaussian Process Regression
Chiwoo Park, Jianhua Z. Huang, Yu Ding (Machine Learning Open Source Software Paper) abs ][pdf ][bib ]
[code ]
Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso
Rahul Mazumder, Trevor Hastie abs ][pdf ][bib ]
Algorithms for Learning Kernels Based on Centered Alignment
Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh abs ][pdf ][bib ]
Causal Bounds and Observable Constraints for Non-deterministic Models
Roland R. Ramsahai abs ][pdf ][bib ]
NIMFA : A Python Library for Nonnegative Matrix Factorization
Marinka Žitnik, Blaž Zupan (Machine Learning Open Source Software Paper) abs ][pdf ][bib ]
[code ]
Algebraic Geometric Comparison of Probability Distributions
Franz J. Király, Paul von Bünau, Frank C. Meinecke, Duncan A.J. Blythe, Klaus-Robert Müller abs ][pdf ][bib ]
Stability of Density-Based Clustering
Alessandro Rinaldo, Aarti Singh, Rebecca Nugent, Larry Wasserman abs ][pdf ][bib ]
Mal-ID: Automatic Malware Detection Using Common Segment Analysis and Meta-Features
Gil Tahan, Lior Rokach, Yuval Shahar abs ][pdf ][bib ]
Sampling Methods for the Nyström Method
Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar abs ][pdf ][bib ]
Positive Semidefinite Metric Learning Using Boosting-like Algorithms
Chunhua Shen, Junae Kim, Lei Wang, Anton van den Hengel abs ][pdf ][bib ]
Consistent Model Selection Criteria on High Dimensions
Yongdai Kim, Sunghoon Kwon, Hosik Choi abs ][pdf ][bib ]
The huge Package for High-dimensional Undirected Graph Estimation in R
Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, Larry Wasserman (Machine Learning Open Source Software Paper) abs ][pdf ][bib ]
[code ]
Analysis of a Random Forests Model
Gérard Biau abs ][pdf ][bib ]
Towards Integrative Causal Analysis of Heterogeneous Data Sets and Studies
Ioannis Tsamardinos, Sofia Triantafillou, Vincenzo Lagani abs ][pdf ][bib ]
Hope and Fear for Discriminative Training of Statistical Translation Models
David Chiang abs ][pdf ][bib ]
A Multi-Stage Framework for Dantzig Selector and LASSO
Ji Liu, Peter Wonka, Jieping Ye abs ][pdf ][bib ]
A Geometric Approach to Sample Compression
Benjamin I.P. Rubinstein, J. Hyam Rubinstein abs ][pdf ][bib ]
Minimax Manifold Estimation
Christopher Genovese, Marco Perone-Pacifico, Isabella Verdinelli, Larry Wasserman abs ][pdf ][bib ]
Query Strategies for Evading Convex-Inducing Classifiers
Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang, Anthony D. Joseph, Steven J. Lee, Satish Rao, J. D. Tygar abs ][pdf ][bib ]
Transfer in Reinforcement Learning via Shared Features
George Konidaris, Ilya Scheidwasser, Andrew Barto abs ][pdf ][bib ]
On Ranking and Generalization Bounds
Wojciech Rejchel abs ][pdf ][bib ]
Feature Selection via Dependence Maximization
Le Song, Alex Smola, Arthur Gretton, Justin Bedo, Karsten Borgwardt abs ][pdf ][bib ]
Structured Sparsity via Alternating Direction Methods
Zhiwei Qin, Donald Goldfarb abs ][pdf ][bib ]
Activized Learning: Transforming Passive to Active with Improved Label Complexity
Steve Hanneke abs ][pdf ][bib ]
A Model of the Perception of Facial Expressions of Emotion by Humans: Research Overview and Perspectives
Aleix Martinez, Shichuan Du abs ][pdf ][bib ]
A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models
Neil D. Lawrence abs ][pdf ][bib ]
Mixability is Bayes Risk Curvature Relative to Log Loss
Tim van Erven, Mark D. Reid, Robert C. Williamson abs ][pdf ][bib ]
Restricted Strong Convexity and Weighted Matrix Completion: Optimal Bounds with Noise
Sahand Negahban, Martin J. Wainwright abs ][pdf ][bib ]
glm-ie: Generalised Linear Models Inference & Estimation Toolbox
Hannes Nickisch (Machine Learning Open Source Software Paper) abs ][pdf ][bib ]
[code ]
Manifold Identification in Dual Averaging for Regularized Stochastic Online Learning
Sangkyun Lee, Stephen J. Wright abs ][pdf ][bib ]
Variational Multinomial Logit Gaussian Process
Kian Ming A. Chai abs ][pdf ][bib ]
Entropy Search for Information-Efficient Global Optimization
Philipp Hennig, Christian J. Schuler abs ][pdf ][bib ]
Estimation and Selection via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications
Jian Huang, Cun-Hui Zhang abs ][pdf ][bib ]
Regularization Techniques for Learning with Matrices
Sham M. Kakade, Shai Shalev-Shwartz, Ambuj Tewari abs ][pdf ][bib ]
Confidence-Weighted Linear Classification for Text Categorization
Koby Crammer, Mark Dredze, Fernando Pereira abs ][pdf ][bib ]
Integrating a Partial Model into Model Free Reinforcement Learning
Aviv Tamar, Dotan Di Castro, Ron Meir abs ][pdf ][bib ]
Jstacs: A Java Framework for Statistical Analysis and Classification of Biological Sequences
Jan Grau, Jens Keilwagen, André Gohr, Berit Haldemann, Stefan Posch, Ivo Grosse (Machine Learning Open Source Software Paper) abs ][pdf ][bib ]
[code ]
Variable Selection in High-dimensional Varying-coefficient Models with Global Optimality
Lan Xue, Annie Qu abs ][pdf ][bib ]
An Improved GLMNET for L1-regularized Logistic Regression
Guo-Xun Yuan, Chia-Hua Ho, Chih-Jen Lin abs ][pdf ][bib ]
EP-GIG Priors and Applications in Bayesian Sparse Learning
Zhihua Zhang, Shusen Wang, Dehua Liu, Michael I. Jordan abs ][pdf ][bib ]
Pattern for Python
Tom De Smedt, Walter Daelemans (Machine Learning Open Source Software Paper) abs ][pdf ][bib ]
[code ]
Optimistic Bayesian Sampling in Contextual-Bandit Problems
Benedict C. May, Nathan Korda, Anthony Lee, David S. Leslie abs ][pdf ][bib ]
A Comparison of the Lasso and Marginal Regression
Christopher R. Genovese, Jiashun Jin, Larry Wasserman, Zhigang Yao abs ][pdf ][bib ]
On the Necessity of Irrelevant Variables
David P. Helmbold, Philip M. Long abs ][pdf ][bib ]
DEAP: Evolutionary Algorithms Made Easy
Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner, Marc Parizeau, Christian Gagné (Machine Learning Open Source Software Paper) abs ][pdf ][bib ]
[code ]
An Introduction to Artificial Prediction Markets for Classification
Adrian Barbu, Nathan Lay abs ][pdf ][bib ]
Sign Language Recognition using Sub-Units
Helen Cooper, Eng-Jon Ong, Nicolas Pugeault, Richard Bowden abs ][pdf ][bib ]
A Topic Modeling Toolbox Using Belief Propagation
Jia Zeng (Machine Learning Open Source Software Paper) abs ][pdf ][bib ]
[code ]
MedLDA: Maximum Margin Supervised Topic Models
Jun Zhu, Amr Ahmed, Eric P. Xing abs ][pdf ][bib ]
Pairwise Support Vector Machines and their Application to Large Scale Problems
Carl Brunner, Andreas Fischer, Klaus Luig, Thorsten Thies abs ][pdf ][bib ]
High-Dimensional Gaussian Graphical Model Selection: Walk Summability and Local Separation Criterion
Animashree Anandkumar, Vincent Y.F. Tan, Furong Huang, Alan S. Willsky abs ][pdf ][bib ]
A Local Spectral Method for Graphs: With Applications to Improving Graph Partitions and Exploring Data Graphs Locally
Michael W. Mahoney, Lorenzo Orecchia, Nisheeth K. Vishnoi abs ][pdf ][bib ]
Multi-Target Regression with Rule Ensembles
Timo Aho, Bernard Ženko, Sašo Džeroski, Tapio Elomaa abs ][pdf ][bib ]
Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs
Alain Hauser, Peter Bühlmann abs ][pdf ][bib ]
On the Convergence Rate of l p -Norm Multiple Kernel Learning
Marius Kloft, Gilles Blanchard abs ][pdf ][bib ]
Trading Regret for Efficiency: Online Convex Optimization with Long Term Constraints
Mehrdad Mahdavi, Rong Jin, Tianbao Yang abs ][pdf ][bib ]
Robust Kernel Density Estimation
JooSeuk Kim, Clayton D. Scott abs ][pdf ][bib ]
Nonparametric Guidance of Autoencoder Representations using Label Information
Jasper Snoek, Ryan P. Adams, Hugo Larochelle abs ][pdf ][bib ]
Finding Recurrent Patterns from Continuous Sign Language Sentences for Automated Extraction of Signs
Sunita Nayak, Kester Duncan, Sudeep Sarkar, Barbara Loeding abs ][pdf ][bib ]
Static Prediction Games for Adversarial Learning Problems
Michael Brückner, Christian Kanzow, Tobias Scheffer abs ][pdf ][bib ]
Selective Sampling and Active Learning from Single and Multiple Teachers
Ofer Dekel, Claudio Gentile, Karthik Sridharan abs ][pdf ][bib ]
PREA: Personalized Recommendation Algorithms Toolkit
Joonseok Lee, Mingxuan Sun, Guy Lebanon (Machine Learning Open Source Software Paper) abs ][pdf ][bib ]
[code ]
Coherence Functions with Applications in Large-Margin Classification Methods
Zhihua Zhang, Dehua Liu, Guang Dai, Michael I. Jordan abs ][pdf ][bib ]
Linear Regression With Random Projections
Odalric-Ambrym Maillard, Rémi Munos abs ][pdf ][bib ]
Multi-task Regression using Minimal Penalties
Matthieu Solnon, Sylvain Arlot, Francis Bach abs ][pdf ][bib ]
A Unified View of Performance Metrics: Translating Threshold Choice into Expected Classification Loss
José Hernández-Orallo, Peter Flach, Cèsar Ferri abs ][pdf ][bib ]
Local and Global Scaling Reduce Hubs in Space
Dominik Schnitzer, Arthur Flexer, Markus Schedl, Gerhard Widmer abs ][pdf ][bib ]
Online Submodular Minimization
Elad Hazan, Satyen Kale abs ][pdf ][bib ]
Efficient Methods for Robust Classification Under Uncertainty in Kernel Matrices
Aharon Ben-Tal, Sahely Bhadra, Chiranjib Bhattacharyya, Arkadi Nemirovski abs ][pdf ][bib ]
Facilitating Score and Causal Inference Trees for Large Observational Studies
Xiaogang Su, Joseph Kang, Juanjuan Fan, Richard A. Levine, Xin Yan abs ][pdf ][bib ]
Oger: Modular Learning Architectures For Large-Scale Sequential Processing
David Verstraeten, Benjamin Schrauwen, Sander Dieleman, Philemon Brakel, Pieter Buteneers, Dejan Pecevski (Machine Learning Open Source Software Paper) abs ][pdf ][bib ]
[code ]
Multi-Instance Learning with Any Hypothesis Class
Sivan Sabato, Naftali Tishby abs ][pdf ][bib ]
Finite-Sample Analysis of Least-Squares Policy Iteration
Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos abs ][pdf ][bib ]
Discriminative Hierarchical Part-based Models for Human Parsing and Action Recognition
Yang Wang, Duan Tran, Zicheng Liao, David Forsyth abs ][pdf ][bib ]
Breaking the Curse of Kernelization: Budgeted Stochastic Gradient Descent for Large-Scale SVM Training
Zhuang Wang, Koby Crammer, Slobodan Vucetic abs ][pdf ][bib ]
Bayesian Mixed-Effects Inference on Classification Performance in Hierarchical Data Sets
Kay H. Brodersen, Christoph Mathys, Justin R. Chumbley, Jean Daunizeau, Cheng Soon Ong, Joachim M. Buhmann, Klaas E. Stephan abs ][pdf ][bib ]
Quantum Set Intersection and its Application to Associative Memory
Tamer Salman, Yoram Baram abs ][pdf ][bib ]
Dynamic Policy Programming
Mohammad Gheshlaghi Azar, Vicenç Gómez, Hilbert J. Kappen abs ][pdf ][bib ]
Sally: A Tool for Embedding Strings in Vector Spaces
Konrad Rieck, Christian Wressnegger, Alexander Bikadorov (Machine Learning Open Source Software Paper) abs ][pdf ][bib ]
[code ]
Linear Fitted-Q Iteration with Multiple Reward Functions
Daniel J. Lizotte, Michael Bowling, Susan A. Murphy abs ][pdf ][bib ]
Human Gesture Recognition on Product Manifolds
Yui Man Lui abs ][pdf ][bib ]
Large-scale Linear Support Vector Regression
Chia-Hua Ho, Chih-Jen Lin abs ][pdf ][bib ]
Sparse and Unique Nonnegative Matrix Factorization Through Data Preprocessing
Nicolas Gillis abs ][pdf ][bib ]
Learning Linear Cyclic Causal Models with Latent Variables
Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer abs ][pdf ][bib ]
Iterative Reweighted Algorithms for Matrix Rank Minimization
Karthik Mohan, Maryam Fazel abs ][pdf ][bib ]
Fast Approximation of Matrix Coherence and Statistical Leverage
Petros Drineas, Malik Magdon-Ismail, Michael W. Mahoney, David P. Woodruff abs ][pdf ][bib ]
PAC-Bayes Bounds with Data Dependent Priors
Emilio Parrado-Hernández, Amiran Ambroladze, John Shawe-Taylor, Shiliang Sun abs ][pdf ][bib ]
DARWIN: A Framework for Machine Learning and Computer Vision Research and Development
Stephen Gould (Machine Learning Open Source Software Paper) abs ][pdf ][bib ]
[code ]
Regularized Bundle Methods for Convex and Non-Convex Risks
Trinh Minh Tri Do, Thierry Artières abs ][pdf ][bib ]
Learning Symbolic Representations of Hybrid Dynamical Systems
Daniel L. Ly, Hod Lipson abs ][pdf ][bib ]
SVDFeature: A Toolkit for Feature-based Collaborative Filtering
Tianqi Chen, Weinan Zhang, Qiuxia Lu, Kailong Chen, Zhao Zheng, Yong Yu (Machine Learning Open Source Software Paper) abs ][pdf ][bib ]
[code ]
Smoothing Multivariate Performance Measures
Xinhua Zhang, Ankan Saha, S.V.N. Vishwanathan abs ][pdf ][bib ]
Security Analysis of Online Centroid Anomaly Detection
Marius Kloft, Pavel Laskov abs ][pdf ][bib ]
Exploration in Relational Domains for Model-based Reinforcement Learning
Tobias Lang, Marc Toussaint, Kristian Kersting abs ][pdf ][bib ]
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