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Journal of Machine Learning Research, Volume 15
Volume 15, Number 1, January 2014
- Jüri Lember, Alexey A. Koloydenko:
Bridging Viterbi and posterior decoding: a generalized risk approach to hidden path inference based on hidden Markov models. 1-58 - Manu Nandan, Pramod P. Khargonekar, Sachin S. Talathi:
Fast SVM training using approximate extreme points. 59-98 - Richard Jayadi Oentaryo, Ee-Peng Lim, Michael Finegold, David Lo, Feida Zhu, Clifton Phua, Eng-Yeow Cheu, Ghim-Eng Yap, Kelvin Sim, Minh Nhut Nguyen, Kasun S. Perera, Bijay Neupane, Mustafa Amir Faisal, Zeyar Aung, Wei Lee Woon, Wei Chen, Dhaval Patel, Daniel Berrar:
Detecting click fraud in online advertising: a data mining approach. 99-140 - Marc Claesen, Frank De Smet, Johan A. K. Suykens, Bart De Moor:
EnsembleSVM: a library for ensemble learning using support vector machines. 141-145 - Divyanshu Vats, Robert D. Nowak:
A junction tree framework for undirected graphical model selection. 147-191 - Twan van Laarhoven, Elena Marchiori:
Axioms for graph clustering quality functions. 193-215 - Aleksandr Y. Aravkin, James V. Burke, Alessandro Chiuso, Gianluigi Pillonetto:
Convex vs non-convex estimators for regression and sparse estimation: the mean squared error properties of ARD and GLasso. 217-252 - Aaron Wilson, Alan Fern, Prasad Tadepalli
:
Using trajectory data to improve bayesian optimization for reinforcement learning. 253-282 - Zoltán Szabó:
Information theoretical estimators toolbox. 283-287 - Matthieu Geist, Bruno Scherrer:
Off-policy learning with eligibility traces: a survey. 289-333 - Garvesh Raskutti, Martin J. Wainwright, Bin Yu:
Early stopping and non-parametric regression: an optimal data-dependent stopping rule. 335-366 - Patrick Fox-Roberts, Edward Rosten:
Unbiased generative semi-supervised learning. 367-443 - Karthik Mohan, Palma London, Maryam Fazel, Daniela M. Witten, Su-In Lee:
Node-based learning of multiple Gaussian graphical models. 445-488 - Haotian Pang, Han Liu, Robert J. Vanderbei:
The fastclime package for linear programming and large-scale precision matrix estimation in R. 489-493 - Steven C. H. Hoi, Jialei Wang, Peilin Zhao:
LIBOL: a library for online learning algorithms. 495-499 - Daniel Lowd, Jesse Davis:
Improving Markov network structure learning using decision trees. 501-532 - Marco Cuturi, David Avis:
Ground metric learning. 533-564 - Emile Richard, Stéphane Gaïffas, Nicolas Vayatis:
Link prediction in graphs with autoregressive features. 565-593 - Francis R. Bach:
Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression. 595-627 - Rajen Dinesh Shah, Nicolai Meinshausen:
Random intersection trees. 629-654 - Brett L. Moore, Larry D. Pyeatt, Vivekanand Kulkarni, Periklis Panousis, Kevin Padrez, Anthony G. Doufas:
Reinforcement learning for closed-loop propofol anesthesia: a study in human volunteers. 655-696 - Emanuele Coviello, Antoni B. Chan, Gert R. G. Lanckriet:
Clustering hidden Markov models with variational HEM. 697-747 - Teng Zhang, Gilad Lerman:
A novel M-estimator for robust PCA. 749-808 - Christoph Dann, Gerhard Neumann, Jan Peters:
Policy evaluation with temporal differences: a survey and comparison. 809-883 - Nir Ailon, Ron Begleiter, Esther Ezra:
Active learning using smooth relative regret approximations with applications. 885-920 - Henning Sprekeler, Tiziano Zito, Laurenz Wiskott:
An extension of slow feature analysis for nonlinear blind source separation. 921-947 - Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters, Jürgen Schmidhuber:
Natural evolution strategies. 949-980 - Viet Cuong Nguyen, Nan Ye, Wee Sun Lee, Hai Leong Chieu:
Conditional random field with high-order dependencies for sequence labeling and segmentation. 981-1009 - Tomohiko Mizutani:
Ellipsoidal rounding for nonnegative matrix factorization under noisy separability. 1011-1039 - Sara Wade, David B. Dunson, Sonia Petrone, Lorenzo Trippa:
Improving prediction from dirichlet process mixtures via enrichment. 1041-1071 - Jun Zhu, Ning Chen, Hugh Perkins, Bo Zhang:
Gibbs max-margin topic models with data augmentation. 1073-1110 - Alekh Agarwal, Olivier Chapelle, Miroslav Dudík, John Langford:
A reliable effective terascale linear learning system. 1111-1133 - Maksims Volkovs, Richard S. Zemel:
New learning methods for supervised and unsupervised preference aggregation. 1135-1176 - Kai-Yang Chiang, Cho-Jui Hsieh, Nagarajan Natarajan, Inderjit S. Dhillon, Ambuj Tewari:
Prediction and clustering in signed networks: a local to global perspective. 1177-1213 - Francisco J. R. Ruiz, Isabel Valera, Carlos Blanco, Fernando Pérez-Cruz:
Bayesian nonparametric comorbidity analysis of psychiatric disorders. 1215-1247 - Nicolas Gillis, Robert Luce:
Robust near-separable nonnegative matrix factorization using linear optimization. 1249-1280 - Steven de Rooij, Tim van Erven, Peter D. Grünwald, Wouter M. Koolen:
Follow the leader if you can, hedge if you must. 1281-1316 - Janardhan Rao Doppa, Alan Fern, Prasad Tadepalli
Structured prediction via output space search. 1317-1350 - Matt P. Wand:
Fully simplified multivariate normal updates in non-conjugate variational message passing. 1351-1369 - Mingkui Tan, Ivor W. Tsang, Li Wang:
Towards ultrahigh dimensional feature selection for big data. 1371-1429 - Charles Dubout, François Fleuret:
Adaptive sampling for large scale boosting. 1431-1453 - Nicolas Boumal, Bamdev Mishra, Pierre-Antoine Absil, Rodolphe Sepulchre:
Manopt, a matlab toolbox for optimization on manifolds. 1455-1459 - Maya R. Gupta, Samy Bengio, Jason Weston:
Training highly multiclass classifiers. 1461-1492 - Daniele Durante, Bruno Scarpa, David B. Dunson:
Locally adaptive factor processes for multivariate time series. 1493-1522 - Po-Wei Wang, Chih-Jen Lin:
Iteration complexity of feasible descent methods for convex optimization. 1523-1548 - Majid Janzamin, Animashree Anandkumar:
High-dimensional covariance decomposition into sparse Markov and independence models. 1549-1591 - Matthew D. Hoffman, Andrew Gelman:
The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. 1593-1623 - Stefan Wager, Trevor Hastie, Bradley Efron:
Confidence intervals for random forests: the jackknife and the infinitesimal jackknife. 1625-1651 - Shivani Agarwal:
Surrogate regret bounds for bipartite ranking via strongly proper losses. 1653-1674 - Zhan Wang, Sandra Paterlini, Fuchang Gao, Yuhong Yang:
Adaptive minimax regression estimation over sparse lq-hulls. 1675-1711 - Mladen Kolar, Han Liu, Eric P. Xing:
Graph estimation from multi-attribute data. 1713-1750 - Ulrike von Luxburg, Agnes Radl, Matthias Hein:
Hitting and commute times in large random neighborhood graphs. 1751-1798 - Jun Zhu, Ning Chen, Eric P. Xing:
Bayesian inference with posterior regularization and applications to infinite latent SVMs. 1799-1847 - Pasi Jylänki, Aapo Nummenmaa, Aki Vehtari:
Expectation propagation for neural networks with sparsity-promoting priors. 1849-1901 - Nicolas Städler, Daniel J. Stekhoven, Peter Bühlmann:
Pattern alternating maximization algorithm for missing data in high-dimensional problems. 1903-1928 - Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov:
Dropout: a simple way to prevent neural networks from overfitting. 1929-1958 - Andrew S. Lan, Andrew E. Waters, Christoph Studer, Richard G. Baraniuk:
Sparse factor analysis for learning and content analytics. 1959-2008 - Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf:
Causal discovery with continuous additive noise models. 2009-2053 - Andreas C. Müller, Sven Behnke:
PyStruct: learning structured prediction in python. 2055-2060 - Aäron van den Oord, Benjamin Schrauwen:
The student-t mixture as a natural image patch prior with application to image compression. 2061-2086 - Robert Nishihara, Iain Murray, Ryan P. Adams:
Parallel MCMC with generalized elliptical slice sampling. 2087-2112 - Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen, Olivier Chapelle:
Classifier cascades and trees for minimizing feature evaluation cost. 2113-2144 - Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön:
Particle gibbs with ancestor sampling. 2145-2184 - Xiaolin Huang, Lei Shi, Johan A. K. Suykens:
Ramp loss linear programming support vector machine. 2185-2211 - Yudong Chen, Ali Jalali, Sujay Sanghavi, Huan Xu:
Clustering partially observed graphs via convex optimization. 2213-2238 - Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade:
A tensor approach to learning mixed membership community models. 2239-2312 - Nikolaos Tziortziotis, Christos Dimitrakakis, Konstantinos Blekas:
Cover tree Bayesian reinforcement learning. 2313-2335 - Steven Reece, Siddhartha Ghosh, Alex Rogers, Stephen J. Roberts, Nicholas R. Jennings:
Efficient state-space inference of periodic latent force models. 2337-2397 - Shay B. Cohen, Karl Stratos, Michael Collins, Dean P. Foster, Lyle H. Ungar:
Spectral learning of latent-variable PCFGs: algorithms and sample complexity. 2399-2449 - Claudio Gentile, Francesco Orabona:
On multilabel classification and ranking with bandit feedback. 2451-2487 - Elad Hazan, Satyen Kale:
Beyond the regret minimization barrier: optimal algorithms for stochastic strongly-convex optimization. 2489-2512 - Jakub Konecný, Michal Hagara:
One-shot-learning gesture recognition using HOG-HOF features. 2513-2532 - Aleksandrs Slivkins:
Contextual bandits with similarity information. 2533-2568 - Mohammad J. Saberian, Nuno Vasconcelos:
Boosting algorithms for detector cascade learning. 2569-2605 - Amit Dhurandhar, Marek Petrik:
Efficient and accurate methods for updating generalized linear models with multiple feature additions. 2607-2627 - Shohei Shimizu, Kenneth Bollen:
Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions. 2629-2652 - Abdul-Saboor Sheikh, Jacquelyn A. Shelton, Jörg Lücke:
A truncated EM approach for spike-and-slab sparse coding. 2653-2687 - Marc Henniges, Richard E. Turner, Maneesh Sahani, Julian Eggert, Jörg Lücke:
Efficient occlusive components analysis. 2689-2722 - Jiashun Jin, Cun-Hui Zhang, Qi Zhang:
Optimality of graphlet screening in high dimensional variable selection. 2723-2772 - Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky:
Tensor decompositions for learning latent variable models. 2773-2832 - Evan Archer, Il Memming Park, Jonathan W. Pillow:
Bayesian entropy estimation for countable discrete distributions. 2833-2868 - Adel Javanmard, Andrea Montanari:
Confidence intervals and hypothesis testing for high-dimensional regression. 2869-2909 - Cho-Jui Hsieh, Mátyás A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar:
QUIC: quadratic approximation for sparse inverse covariance estimation. 2911-2947 - Nitish Srivastava, Ruslan Salakhutdinov:
Multimodal learning with deep Boltzmann machines. 2949-2980 - Braxton Osting, Christoph Brune, Stanley J. Osher:
Optimal data collection for informative rankings expose well-connected graphs. 2981-3012 - Jiaxiang Wu, Jian Cheng:
Bayesian co-boosting for multi-modal gesture recognition. 3013-3036 - Wei-Sheng Chin, Yong Zhuang, Yu-Chin Juan, Felix Wu, Hsiao-Yu Tung, Tong Yu, Jui-Pin Wang, Cheng-Xia Chang, Chun-Pai Yang, Wei-Cheng Chang, Kuan-Hao Huang, Tzu-Ming Kuo, Shan-Wei Lin, Young-San Lin, Yu-Chen Lu, Yu-Chuan Su, Cheng-Kuang Wei, Tu-Chun Yin, Chun-Liang Li, Ting-Wei Lin, Cheng-Hao Tsai, Shou-De Lin, Hsuan-Tien Lin, Chih-Jen Lin:
Effective string processing and matching for author disambiguation. 3037-3064 - Po-Ling Loh, Peter Bühlmann:
High-dimensional learning of linear causal networks via inverse covariance estimation. 3065-3105 - Thorsten Doliwa, Gaojian Fan, Hans Ulrich Simon, Sandra Zilles:
Recursive teaching dimension, VC-dimension and sample compression. 3107-3131 - Manuel Fernández Delgado, Eva Cernadas, Senén Barro, Dinani Gomes Amorim:
Do we need hundreds of classifiers to solve real world classification problems? 3133-3181 - Ivo Couckuyt, Tom Dhaene, Piet Demeester:
ooDACE toolbox: a flexible object-oriented Kriging implementation. 3183-3186 - Long-Van Nguyen-Dinh, Alberto Calatroni, Gerhard Tröster:
Robust online gesture recognition with crowdsourced annotations. 3187-3220 - Laurens van der Maaten:
Accelerating t-SNE using tree-based algorithms. 3221-3245 - Shie Mannor, Vianney Perchet, Gilles Stoltz:
Set-valued approachability and online learning with partial monitoring. 3247-3295 - Kean Ming Tan, Palma London, Karthik Mohan, Su-In Lee, Maryam Fazel, Daniela M. Witten:
Learning graphical models with hubs. 3297-3331 - Jeffrey W. Miller, Matthew T. Harrison:
Inconsistency of Pitman-Yor process mixtures for the number of components. 3333-3370 - Alexander Fabisch, Jan Hendrik Metzen:
Active contextual policy search. 3371-3399 - Ohad Shamir, Shai Shalev-Shwartz:
Matrix completion with the trace norm: learning, bounding, and transducing. 3401-3423 - Fabrizio Lecci, Alessandro Rinaldo, Larry A. Wasserman:
Statistical analysis of metric graph reconstruction. 3425-3446 - Xiaodong Lin, Minh Pham, Andrzej Ruszczynski:
Alternating linearization for structured regularization problems. 3447-3481 - Nicholas Edward Gillian, Joseph A. Paradiso:
The gesture recognition toolkit. 3483-3487 - Camille Couprie, Clément Farabet, Laurent Najman, Yann LeCun:
Convolutional nets and watershed cuts for real-time semantic Labeling of RGBD videos. 3489-3511 - Willem Waegeman, Krzysztof Dembczynski, Arkadiusz Jachnik, Weiwei Cheng, Eyke Hüllermeier:
On the bayes-optimality of F-measure maximizers. 3333-3388 - Philippe Fournier-Viger, Antonio Gomariz, Ted Gueniche, Azadeh Soltani, Cheng-Wei Wu, Vincent S. Tseng:
SPMF: a Java open-source pattern mining library. 3389-3393 - William L. Hamilton, Mahdi Milani Fard, Joelle Pineau:
Efficient learning and planning with compressed predictive states. 3395-3439 - Sergey Feldman, Maya R. Gupta, Bela A. Frigyik:
Revisiting Stein's paradox: multi-task averaging. 3441-3482 - Kristof Van Moffaert, Ann Nowé:
Multi-objective reinforcement learning using sets of pareto dominating policies. 3483-3512 - Vince Lyzinski, Donniell E. Fishkind, Carey E. Priebe:
Seeded graph matching for correlated Erdös-Rényi graphs. 3513-3540 - Keisuke Yamazaki:
Asymptotic accuracy of distribution-based estimation of latent variables. 3541-3562 - Guillaume Alain, Yoshua Bengio:
What regularized auto-encoders learn from the data-generating distribution. 3563-3593 - David P. Wipf, Haichao Zhang:
Revisiting Bayesian blind deconvolution. 3595-3634 - Jeffrey C. Jackson, Karl Wimmer:
New results for random walk learning. 3635-3666 - Norberto A. Goussies, Sebastián Ubalde, Marta Mejail:
Transfer learning decision forests for gesture recognition. 3667-3690 - Toke Jansen Hansen, Michael W. Mahoney:
Semi-supervised eigenvectors for large-scale locally-biased learning. 3691-3734 - Ruben Martinez-Cantin:
BayesOpt: a Bayesian optimization library for nonlinear optimization, experimental design and bandits. 3735-3739 - Diego Colombo, Marloes H. Maathuis:
Order-independent constraint-based causal structure learning. 3741-3782 - Tyler Lu, Craig Boutilier:
Effective sampling and learning for mallows models with pairwise-preference data. 3783-3829 - Maria-Florina Balcan, Yingyu Liang, Pramod Gupta:
Robust hierarchical clustering. 3831-3871 - Thomas Desautels, Andreas Krause, Joel W. Burdick:
Parallelizing exploration-exploitation tradeoffs in Gaussian process bandit optimization. 3873-3923 - Kshitij Judah, Alan Paul Fern, Thomas G. Dietterich, Prasad Tadepalli
Active lmitation learning: formal and practical reductions to I.I.D. learning. 3925-3963
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