JMLR Volume 13
- Distance Metric Learning with Eigenvalue Optimization
- Yiming Ying, Peng Li; (1):1−26, 2012.
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- Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection
- Gavin Brown, Adam Pocock, Ming-Jie Zhao, Mikel Luján; (2):27−66, 2012.
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- Plug-in Approach to Active Learning
- Stanislav Minsker; (3):67−90, 2012.
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- Refinement of Operator-valued Reproducing Kernels
- Haizhang Zhang, Yuesheng Xu, Qinghui Zhang; (4):91−136, 2012.
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- An Active Learning Algorithm for Ranking from Pairwise Preferences with an Almost Optimal Query Complexity
- Nir Ailon; (5):137−164, 2012.
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- Optimal Distributed Online Prediction Using Mini-Batches
- Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, Lin Xiao; (6):165−202, 2012.
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- Active Clustering of Biological Sequences
- Konstantin Voevodski, Maria-Florina Balcan, Heiko Röglin, Shang-Hua Teng, Yu Xia; (7):203−225, 2012.
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- Multi Kernel Learning with Online-Batch Optimization
- Francesco Orabona, Luo Jie, Barbara Caputo; (8):227−253, 2012.
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- Active Learning via Perfect Selective Classification
- Ran El-Yaniv, Yair Wiener; (9):255−279, 2012.
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- Random Search for Hyper-Parameter Optimization
- James Bergstra, Yoshua Bengio; (10):281−305, 2012.
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- Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics
- Michael U. Gutmann, Aapo Hyvärinen; (11):307−361, 2012.
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- Bounding the Probability of Error for High Precision Optical Character Recognition
- Gary B. Huang, Andrew Kae, Carl Doersch, Erik Learned-Miller; (12):363−387, 2012.
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- Minimax-Optimal Rates For Sparse Additive Models Over Kernel Classes Via Convex Programming
- Garvesh Raskutti, Martin J. Wainwright, Bin Yu; (13):389−427, 2012.
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- Online Learning in the Embedded Manifold of Low-rank Matrices
- Uri Shalit, Daphna Weinshall, Gal Chechik; (14):429−458, 2012.
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- Multi-Assignment Clustering for Boolean Data
- Mario Frank, Andreas P. Streich, David Basin, Joachim M. Buhmann; (15):459−489, 2012.
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- Eliminating Spammers and Ranking Annotators for Crowdsourced Labeling Tasks
- Vikas C. Raykar, Shipeng Yu; (16):491−518, 2012.
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- Metric and Kernel Learning Using a Linear Transformation
- Prateek Jain, Brian Kulis, Jason V. Davis, Inderjit S. Dhillon; (17):519−547, 2012.
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- MULTIBOOST: A Multi-purpose Boosting Package
- Djalel Benbouzid, Róbert Busa-Fekete, Norman Casagrande, François-David Collin, Balázs Kégl; (18):549−553, 2012. (Machine Learning Open Source Software Paper)
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[code]
- ML-Flex: A Flexible Toolbox for Performing Classification Analyses In Parallel
- Stephen R. Piccolo, Lewis J. Frey; (19):555−559, 2012. (Machine Learning Open Source Software Paper)
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[code]
- A Primal-Dual Convergence Analysis of Boosting
- Matus Telgarsky; (20):561−606, 2012.
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- Non-Sparse Multiple Kernel Fisher Discriminant Analysis
- Fei Yan, Josef Kittler, Krystian Mikolajczyk, Atif Tahir; (21):607−642, 2012.
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- Learning Algorithms for the Classification Restricted Boltzmann Machine
- Hugo Larochelle, Michael Mandel, Razvan Pascanu, Yoshua Bengio; (22):643−669, 2012.
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- Structured Sparsity and Generalization
- Andreas Maurer, Massimiliano Pontil; (23):671−690, 2012.
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- A Case Study on Meta-Generalising: A Gaussian Processes Approach
- Grigorios Skolidis, Guido Sanguinetti; (24):691−721, 2012.
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- A Kernel Two-Sample Test
- Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander Smola; (25):723−773, 2012.
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- GPLP: A Local and Parallel Computation Toolbox for Gaussian Process Regression
- Chiwoo Park, Jianhua Z. Huang, Yu Ding; (26):775−779, 2012. (Machine Learning Open Source Software Paper)
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[code]
- Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso
- Rahul Mazumder, Trevor Hastie; (27):781−794, 2012.
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- Algorithms for Learning Kernels Based on Centered Alignment
- Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh; (28):795−828, 2012.
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- Causal Bounds and Observable Constraints for Non-deterministic Models
- Roland R. Ramsahai; (29):829−848, 2012.
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- NIMFA : A Python Library for Nonnegative Matrix Factorization
- Marinka Žitnik, Blaž Zupan; (30):849−853, 2012. (Machine Learning Open Source Software Paper)
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[code]
- Algebraic Geometric Comparison of Probability Distributions
- Franz J. Király, Paul von Bünau, Frank C. Meinecke, Duncan A.J. Blythe, Klaus-Robert Müller; (31):855−903, 2012.
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- Stability of Density-Based Clustering
- Alessandro Rinaldo, Aarti Singh, Rebecca Nugent, Larry Wasserman; (32):905−948, 2012.
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- Mal-ID: Automatic Malware Detection Using Common Segment Analysis and Meta-Features
- Gil Tahan, Lior Rokach, Yuval Shahar; (33):949−979, 2012.
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- Sampling Methods for the Nyström Method
- Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar; (34):981−1006, 2012.
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- Positive Semidefinite Metric Learning Using Boosting-like Algorithms
- Chunhua Shen, Junae Kim, Lei Wang, Anton van den Hengel; (35):1007−1036, 2012.
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- Consistent Model Selection Criteria on High Dimensions
- Yongdai Kim, Sunghoon Kwon, Hosik Choi; (36):1037−1057, 2012.
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- The huge Package for High-dimensional Undirected Graph Estimation in R
- Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, Larry Wasserman; (37):1059−1062, 2012. (Machine Learning Open Source Software Paper)
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[code]
- Analysis of a Random Forests Model
- Gérard Biau; (38):1063−1095, 2012.
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- Towards Integrative Causal Analysis of Heterogeneous Data Sets and Studies
- Ioannis Tsamardinos, Sofia Triantafillou, Vincenzo Lagani; (39):1097−1157, 2012.
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- Hope and Fear for Discriminative Training of Statistical Translation Models
- David Chiang; (40):1159−1187, 2012.
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- A Multi-Stage Framework for Dantzig Selector and LASSO
- Ji Liu, Peter Wonka, Jieping Ye; (41):1189−1219, 2012.
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- A Geometric Approach to Sample Compression
- Benjamin I.P. Rubinstein, J. Hyam Rubinstein; (42):1221−1261, 2012.
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- Minimax Manifold Estimation
- Christopher Genovese, Marco Perone-Pacifico, Isabella Verdinelli, Larry Wasserman; (43):1263−1291, 2012.
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- 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; (44):1293−1332, 2012.
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- Transfer in Reinforcement Learning via Shared Features
- George Konidaris, Ilya Scheidwasser, Andrew Barto; (45):1333−1371, 2012.
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- On Ranking and Generalization Bounds
- Wojciech Rejchel; (46):1373−1392, 2012.
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- Feature Selection via Dependence Maximization
- Le Song, Alex Smola, Arthur Gretton, Justin Bedo, Karsten Borgwardt; (47):1393−1434, 2012.
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- Structured Sparsity via Alternating Direction Methods
- Zhiwei Qin, Donald Goldfarb; (48):1435−1468, 2012.
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- Activized Learning: Transforming Passive to Active with Improved Label Complexity
- Steve Hanneke; (49):1469−1587, 2012.
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- A Model of the Perception of Facial Expressions of Emotion by Humans: Research Overview and Perspectives
- Aleix Martinez, Shichuan Du; (50):1589−1608, 2012.
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- A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models
- Neil D. Lawrence; (51):1609−1638, 2012.
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- Mixability is Bayes Risk Curvature Relative to Log Loss
- Tim van Erven, Mark D. Reid, Robert C. Williamson; (52):1639−1663, 2012.
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- Restricted Strong Convexity and Weighted Matrix Completion: Optimal Bounds with Noise
- Sahand Negahban, Martin J. Wainwright; (53):1665−1697, 2012.
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- glm-ie: Generalised Linear Models Inference & Estimation Toolbox
- Hannes Nickisch; (54):1699−1703, 2012. (Machine Learning Open Source Software Paper)
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[code]
- Manifold Identification in Dual Averaging for Regularized Stochastic Online Learning
- Sangkyun Lee, Stephen J. Wright; (55):1705−1744, 2012.
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- Variational Multinomial Logit Gaussian Process
- Kian Ming A. Chai; (56):1745−1808, 2012.
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- Entropy Search for Information-Efficient Global Optimization
- Philipp Hennig, Christian J. Schuler; (57):1809−1837, 2012.
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- Estimation and Selection via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications
- Jian Huang, Cun-Hui Zhang; (58):1839−1864, 2012.
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- Regularization Techniques for Learning with Matrices
- Sham M. Kakade, Shai Shalev-Shwartz, Ambuj Tewari; (59):1865−1890, 2012.
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- Confidence-Weighted Linear Classification for Text Categorization
- Koby Crammer, Mark Dredze, Fernando Pereira; (60):1891−1926, 2012.
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- Integrating a Partial Model into Model Free Reinforcement Learning
- Aviv Tamar, Dotan Di Castro, Ron Meir; (61):1927−1966, 2012.
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- Jstacs: A Java Framework for Statistical Analysis and Classification of Biological Sequences
- Jan Grau, Jens Keilwagen, André Gohr, Berit Haldemann, Stefan Posch, Ivo Grosse; (62):1967−1971, 2012. (Machine Learning Open Source Software Paper)
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[code]
- Variable Selection in High-dimensional Varying-coefficient Models with Global Optimality
- Lan Xue, Annie Qu; (63):1973−1998, 2012.
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- An Improved GLMNET for L1-regularized Logistic Regression
- Guo-Xun Yuan, Chia-Hua Ho, Chih-Jen Lin; (64):1999−2030, 2012.
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- EP-GIG Priors and Applications in Bayesian Sparse Learning
- Zhihua Zhang, Shusen Wang, Dehua Liu, Michael I. Jordan; (65):2031−2061, 2012.
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- Pattern for Python
- Tom De Smedt, Walter Daelemans; (66):2063−2067, 2012. (Machine Learning Open Source Software Paper)
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[code]
- Optimistic Bayesian Sampling in Contextual-Bandit Problems
- Benedict C. May, Nathan Korda, Anthony Lee, David S. Leslie; (67):2069−2106, 2012.
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- A Comparison of the Lasso and Marginal Regression
- Christopher R. Genovese, Jiashun Jin, Larry Wasserman, Zhigang Yao; (68):2107−2143, 2012.
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- On the Necessity of Irrelevant Variables
- David P. Helmbold, Philip M. Long; (69):2145−2170, 2012.
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- DEAP: Evolutionary Algorithms Made Easy
- Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner, Marc Parizeau, Christian Gagné; (70):2171−2175, 2012. (Machine Learning Open Source Software Paper)
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[code]
- An Introduction to Artificial Prediction Markets for Classification
- Adrian Barbu, Nathan Lay; (71):2177−2204, 2012.
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- Sign Language Recognition using Sub-Units
- Helen Cooper, Eng-Jon Ong, Nicolas Pugeault, Richard Bowden; (72):2205−2231, 2012.
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- A Topic Modeling Toolbox Using Belief Propagation
- Jia Zeng; (73):2233−2236, 2012. (Machine Learning Open Source Software Paper)
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[code]
- MedLDA: Maximum Margin Supervised Topic Models
- Jun Zhu, Amr Ahmed, Eric P. Xing; (74):2237−2278, 2012.
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- Pairwise Support Vector Machines and their Application to Large Scale Problems
- Carl Brunner, Andreas Fischer, Klaus Luig, Thorsten Thies; (75):2279−2292, 2012.
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- High-Dimensional Gaussian Graphical Model Selection: Walk Summability and Local Separation Criterion
- Animashree Anandkumar, Vincent Y.F. Tan, Furong Huang, Alan S. Willsky; (76):2293−2337, 2012.
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- 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; (77):2339−2365, 2012.
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- Multi-Target Regression with Rule Ensembles
- Timo Aho, Bernard Ženko, Sašo Džeroski, Tapio Elomaa; (78):2367−2407, 2012.
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- Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs
- Alain Hauser, Peter Bühlmann; (79):2409−2464, 2012.
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- On the Convergence Rate of lp-Norm Multiple Kernel Learning
- Marius Kloft, Gilles Blanchard; (80):2465−2502, 2012.
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- Trading Regret for Efficiency: Online Convex Optimization with Long Term Constraints
- Mehrdad Mahdavi, Rong Jin, Tianbao Yang; (81):2503−2528, 2012.
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- Robust Kernel Density Estimation
- JooSeuk Kim, Clayton D. Scott; (82):2529−2565, 2012.
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- Nonparametric Guidance of Autoencoder Representations using Label Information
- Jasper Snoek, Ryan P. Adams, Hugo Larochelle; (83):2567−2588, 2012.
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- Finding Recurrent Patterns from Continuous Sign Language Sentences for Automated Extraction of Signs
- Sunita Nayak, Kester Duncan, Sudeep Sarkar, Barbara Loeding; (84):2589−2615, 2012.
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- Static Prediction Games for Adversarial Learning Problems
- Michael Brückner, Christian Kanzow, Tobias Scheffer; (85):2617−2654, 2012.
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- Selective Sampling and Active Learning from Single and Multiple Teachers
- Ofer Dekel, Claudio Gentile, Karthik Sridharan; (86):2655−2697, 2012.
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- PREA: Personalized Recommendation Algorithms Toolkit
- Joonseok Lee, Mingxuan Sun, Guy Lebanon; (87):2699−2703, 2012. (Machine Learning Open Source Software Paper)
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[code]
- Coherence Functions with Applications in Large-Margin Classification Methods
- Zhihua Zhang, Dehua Liu, Guang Dai, Michael I. Jordan; (88):2705−2734, 2012.
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- Linear Regression With Random Projections
- Odalric-Ambrym Maillard, Rémi Munos; (89):2735−2772, 2012.
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- Multi-task Regression using Minimal Penalties
- Matthieu Solnon, Sylvain Arlot, Francis Bach; (90):2773−2812, 2012.
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- A Unified View of Performance Metrics: Translating Threshold Choice into Expected Classification Loss
- José Hernández-Orallo, Peter Flach, Cèsar Ferri; (91):2813−2869, 2012.
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- Local and Global Scaling Reduce Hubs in Space
- Dominik Schnitzer, Arthur Flexer, Markus Schedl, Gerhard Widmer; (92):2871−2902, 2012.
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- Online Submodular Minimization
- Elad Hazan, Satyen Kale; (93):2903−2922, 2012.
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- Efficient Methods for Robust Classification Under Uncertainty in Kernel Matrices
- Aharon Ben-Tal, Sahely Bhadra, Chiranjib Bhattacharyya, Arkadi Nemirovski; (94):2923−2954, 2012.
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- Facilitating Score and Causal Inference Trees for Large Observational Studies
- Xiaogang Su, Joseph Kang, Juanjuan Fan, Richard A. Levine, Xin Yan; (95):2955−2994, 2012.
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- Oger: Modular Learning Architectures For Large-Scale Sequential Processing
- David Verstraeten, Benjamin Schrauwen, Sander Dieleman, Philemon Brakel, Pieter Buteneers, Dejan Pecevski; (96):2995−2998, 2012. (Machine Learning Open Source Software Paper)
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[code]
- Multi-Instance Learning with Any Hypothesis Class
- Sivan Sabato, Naftali Tishby; (97):2999−3039, 2012.
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- Finite-Sample Analysis of Least-Squares Policy Iteration
- Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos; (98):3041−3074, 2012.
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- Discriminative Hierarchical Part-based Models for Human Parsing and Action Recognition
- Yang Wang, Duan Tran, Zicheng Liao, David Forsyth; (99):3075−3102, 2012.
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- Breaking the Curse of Kernelization: Budgeted Stochastic Gradient Descent for Large-Scale SVM Training
- Zhuang Wang, Koby Crammer, Slobodan Vucetic; (100):3103−3131, 2012.
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- 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; (101):3133−3176, 2012.
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- Quantum Set Intersection and its Application to Associative Memory
- Tamer Salman, Yoram Baram; (102):3177−3206, 2012.
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- Dynamic Policy Programming
- Mohammad Gheshlaghi Azar, Vicenç Gómez, Hilbert J. Kappen; (103):3207−3245, 2012.
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- Sally: A Tool for Embedding Strings in Vector Spaces
- Konrad Rieck, Christian Wressnegger, Alexander Bikadorov; (104):3247−3251, 2012. (Machine Learning Open Source Software Paper)
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[code]
- Linear Fitted-Q Iteration with Multiple Reward Functions
- Daniel J. Lizotte, Michael Bowling, Susan A. Murphy; (105):3253−3295, 2012.
[abs][pdf][bib]
- Human Gesture Recognition on Product Manifolds
- Yui Man Lui; (106):3297−3321, 2012.
[abs][pdf][bib]
- Large-scale Linear Support Vector Regression
- Chia-Hua Ho, Chih-Jen Lin; (107):3323−3348, 2012.
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- Sparse and Unique Nonnegative Matrix Factorization Through Data Preprocessing
- Nicolas Gillis; (108):3349−3386, 2012.
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- Learning Linear Cyclic Causal Models with Latent Variables
- Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer; (109):3387−3439, 2012.
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- Iterative Reweighted Algorithms for Matrix Rank Minimization
- Karthik Mohan, Maryam Fazel; (110):3441−3473, 2012.
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- Fast Approximation of Matrix Coherence and Statistical Leverage
- Petros Drineas, Malik Magdon-Ismail, Michael W. Mahoney, David P. Woodruff; (111):3475−3506, 2012.
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- PAC-Bayes Bounds with Data Dependent Priors
- Emilio Parrado-Hernández, Amiran Ambroladze, John Shawe-Taylor, Shiliang Sun; (112):3507−3531, 2012.
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- DARWIN: A Framework for Machine Learning and Computer Vision Research and Development
- Stephen Gould; (113):3533−3537, 2012. (Machine Learning Open Source Software Paper)
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[code]
- Regularized Bundle Methods for Convex and Non-Convex Risks
- Trinh Minh Tri Do, Thierry Artières; (114):3539−3583, 2012.
[abs][pdf][bib]
- Learning Symbolic Representations of Hybrid Dynamical Systems
- Daniel L. Ly, Hod Lipson; (115):3585−3618, 2012.
[abs][pdf][bib]
- SVDFeature: A Toolkit for Feature-based Collaborative Filtering
- Tianqi Chen, Weinan Zhang, Qiuxia Lu, Kailong Chen, Zhao Zheng, Yong Yu; (116):3619−3622, 2012. (Machine Learning Open Source Software Paper)
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[code]
- Smoothing Multivariate Performance Measures
- Xinhua Zhang, Ankan Saha, S.V.N. Vishwanathan; (117):3623−3680, 2012.
[abs][pdf][bib]
- Security Analysis of Online Centroid Anomaly Detection
- Marius Kloft, Pavel Laskov; (118):3681−3724, 2012.
[abs][pdf][bib]
- Exploration in Relational Domains for Model-based Reinforcement Learning
- Tobias Lang, Marc Toussaint, Kristian Kersting; (119):3725−3768, 2012.
[abs][pdf][bib]
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