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ORCIDJournal of Machine Learning Research, Volume 14
Volume 14, Number 1, January 2013
- Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan, Ryota Tomioka:
Global analytic solution of fully-observed variational Bayesian matrix factorization. 1-37 - Stéphan Clémençon, Marine Depecker, Nicolas Vayatis:
Ranking forests. 39-73 - Jaakko Riihimäki, Pasi Jylänki, Aki Vehtari:
Nested expectation propagation for Gaussian process classification. 75-109 - Aapo Hyvärinen, Stephen M. Smith:
Pairwise likelihood ratios for estimation of non-Gaussian structural equation models. 111-152 - Robert Hable:
Universal consistency of localized versions of regularized kernel methods. 153-186 - Antoine Salomon, Jean-Yves Audibert, Issam El Alaoui:
Lower bounds and selectivity of weak-consistent policies in stochastic multi-armed bandit problem. 187-207 - Daniel Kyu Hwa Kohlsdorf, Thad Starner:
MAGIC summoning: towards automatic suggesting and testing of gestures with low probability of false positives during use. 209-242 - Pierre Alquier, Gérard Biau:
Sparse single-index model. 243-280 - Kris De Brabanter, Jos De Brabanter, Bart De Moor, Irène Gijbels:
Derivative estimation with local polynomial fitting. 281-301 - Vinod K. Valsalam, Risto Miikkulainen:
Using symmetry and evolutionary search to minimize sorting networks. 303-331 - Krzysztof Chalupka, Christopher K. I. Williams, Iain Murray:
A framework for evaluating approximation methods for Gaussian process regression. 333-350 - Chao Zhang, Dacheng Tao:
Risk bounds of learning processes for Lévy processes. 351-376 - Ting Hu, Jun Fan, Qiang Wu, Ding-Xuan Zhou:
Learning theory approach to minimum error entropy criterion. 377-397 - Aleksandrs Slivkins, Filip Radlinski, Sreenivas Gollapudi:
Ranked bandits in metric spaces: learning diverse rankings over large document collections. 399-436 - Indraneel Mukherjee, Robert E. Schapire:
A theory of multiclass boosting. 437-497 - Alexander R. Statnikov, Jan Lemeire, Constantin F. Aliferis:
Algorithms for discovery of multiple Markov boundaries. 499-566 - Shai Shalev-Shwartz, Tong Zhang:
Stochastic dual coordinate ascent methods for regularized loss. 567-599 - Sébastien Bubeck, Damien Ernst, Aurélien Garivier:
Optimal discovery with probabilistic expert advice: finite time analysis and macroscopic optimality. 601-623 - Hervé Frezza-Buet, Matthieu Geist:
A C++ template-based reinforcement learning library: fitting the code to the mathematics. 625-628 - Fang Han, Tuo Zhao, Han Liu:
CODA: high dimensional copula discriminant analysis. 629-671 - Matthew J. Johnson, Alan S. Willsky:
Bayesian nonparametric hidden semi-Markov models. 673-701 - Rob Hall, Alessandro Rinaldo, Larry A. Wasserman:
Differential privacy for functions and functional data. 703-727 - Sébastien Gerchinovitz:
Sparsity regret bounds for individual sequences in online linear regression. 729-769 - Jun Wang, Tony Jebara, Shih-Fu Chang:
Semi-supervised learning using greedy max-cut. 771-800 - Ryan R. Curtin, James R. Cline, N. P. Slagle, William B. March, Parikshit Ram, Nishant A. Mehta, Alexander G. Gray:
MLPACK: a scalable C++ machine learning library. 801-805 - Sohail Bahmani, Bhiksha Raj, Petros T. Boufounos:
Greedy sparsity-constrained optimization. 807-841 - Philipp Hennig, Martin Kiefel:
Quasi-Newton methods: a new direction. 843-865 - Sumio Watanabe:
A widely applicable Bayesian information criterion. 867-897 - Xiao-Tong Yuan, Tong Zhang:
Truncated power method for sparse eigenvalue problems. 899-925 - Joachim Niehren, Jérôme Champavère, Aurélien Lemay, Rémi Gilleron:
Query induction with schema-guided pruning strategies. 927-964 - Arto Klami, Seppo Virtanen, Samuel Kaski:
Bayesian Canonical correlation analysis. 965-1003 - Chong Wang, David M. Blei:
Variational inference in nonconjugate models. 1005-1031 - Ming-Jie Zhao, Narayanan Unny Edakunni, Adam Craig Pocock, Gavin Brown:
Beyond Fano's inequality: bounds on the optimal F-score, BER, and cost-sensitive risk and their implications. 1033-1090 - Markus Thom, Günther Palm:
Sparse activity and sparse connectivity in supervised learning. 1091-1143 - Lisha Chen, Andreas Buja:
Stress functions for nonlinear dimension reduction, proximity analysis, and graph drawing. 1145-1173 - Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, Aki Vehtari:
GPstuff: Bayesian modeling with Gaussian processes. 1175-1179 - Bruno Scherrer:
Performance bounds for λ policy iteration and application to the game of Tetris. 1181-1227 - Partha Niyogi:
Manifold regularization and semi-supervised learning: some theoretical analyses. 1229-1250 - Nicolò Cesa-Bianchi, Claudio Gentile, Fabio Vitale, Giovanni Zappella:
Random spanning trees and the prediction ofweighted graphs. 1251-1284 - Samuel Gerber, Ross T. Whitaker:
Regularization-free principal curve estimation. 1285-1302 - Matthew D. Hoffman, David M. Blei, Chong Wang, John W. Paisley:
Stochastic variational inference. 1303-1347 - Chong Zhang, Yufeng Liu:
Multicategory large-margin unified machines. 1349-1386 - Pekka Parviainen, Mikko Koivisto:
Finding optimal Bayesian networks using precedence constraints. 1387-1415 - David Picard, Nicolas Thome, Matthieu Cord:
JKernelMachines: a simple framework for kernel machine. 1417-1421 - Pierre Neuvial:
Asymptotic results on adaptive false discovery rate controlling procedures based on kernel estimators. 1423-1459 - Takafumi Kanamori, Akiko Takeda, Taiji Suzuki:
Conjugate relation between loss functions and uncertainty sets in classification problems. 1461-1504 - Paramveer S. Dhillon, Dean P. Foster, Sham M. Kakade, Lyle H. Ungar:
A risk comparison of ordinary least squares vs ridge regression. 1505-1511 - Dana Angluin, James Aspnes, Sarah Eisenstat, Aryeh Kontorovich:
On the learnability of shuffle ideals. 1513-1531 - Edward McFowland, Skyler Speakman, Daniel B. Neill:
Fast generalized subset scan for anomalous pattern detection. 1533-1561 - Rami Mahdi, Jason G. Mezey:
Sub-local constraint-based learning of Bayesian networks using a joint dependence criterion. 1563-1603 - Reza Bosagh Zadeh, Ashish Goel:
Dimension independent similarity computation. 1605-1626 - Anastasios Roussos, Stavros Theodorakis, Vassilis Pitsikalis, Petros Maragos:
Dynamic affine-invariant shape-appearance handshape features and classification in sign language videos. 1627-1663 - Lorenzo Rosasco, Silvia Villa, Sofia Mosci, Matteo Santoro, Alessandro Verri:
Nonparametric sparsity and regularization. 1665-1714 - Raman Arora, Maya R. Gupta, Amol Kapila, Maryam Fazel:
Similarity-based clustering by left-stochastic matrix factorization. 1715-1746 - Ery Arias-Castro, Bruno Pelletier:
On the convergence of maximum variance unfolding. 1747-1770 - Antony Joseph:
Variable selection in high-dimension with random designs and orthogonal matching pursuit. 1771-1800 - Matthew Urry, Peter Sollich:
Random walk kernels and learning curves for Gaussian process regression on random graphs. 1801-1835 - T. Tony Cai, Jianqing Fan, Tiefeng Jiang:
Distributions of angles in random packing on spheres. 1837-1864 - Wei Pan, Xiaotong Shen, Binghui Liu:
Cluster analysis: unsupervised learning via supervised learning with a non-convex penalty. 1865-1889 - Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Pierre Dupont:
Generalized spike-and-slab priors for Bayesian group feature selection using expectation propagation. 1891-1945 - Nayyar Abbas Zaidi, Jesús Cerquides, Mark James Carman, Geoffrey I. Webb:
Alleviating naive Bayes attribute independence assumption by attribute weighting. 1947-1988 - Theja Tulabandhula, Cynthia Rudin:
Machine learning with operational costs. 1989-2028 - Michael Chertkov, Adam B. Yedidia:
Approximating the permanent with fractional belief propagation. 2029-2066 - Wendelin Böhmer, Steffen Grünewälder, Yun Shen, Marek Musial, Klaus Obermayer:
Construction of approximation spaces for reinforcement learning. 2067-2118 - Sivan Sabato, Nathan Srebro, Naftali Tishby:
Distribution-dependent sample complexity of large margin learning. 2119-2149 - Yu-Feng Li, Ivor W. Tsang, James T. Kwok, Zhi-Hua Zhou:
Convex and scalable weakly labeled SVMs. 2151-2188 - Manavender R. Malgireddy, Ifeoma Nwogu, Venu Govindaraju:
Language-motivated approaches to action recognition. 2189-2212 - Dan Stowell, Mark D. Plumbley:
Segregating event streams and noise with a Markov renewal process model. 2213-2238 - Edward Challis, David Barber:
Gaussian Kullback-Leibler approximate inference. 2239-2286 - Nicholas Ruozzi
, Sekhar Tatikonda:
Message-passing algorithms for quadratic minimization. 2287-2314 - Indraneel Mukherjee, Cynthia Rudin, Robert E. Schapire:
The rate of convergence of AdaBoost. 2315-2347 - Janez Demsar, Tomaz Curk, Ales Erjavec, Crtomir Gorup, Tomaz Hocevar, Mitar Milutinovic, Martin Mozina, Matija Polajnar, Marko Toplak, Anze Staric, Miha Stajdohar, Lan Umek, Lan Zagar, Jure Zbontar, Marinka Zitnik, Blaz Zupan:
Orange: data mining toolbox in python. 2349-2353 - Sergey Lisitsyn, Christian Widmer, Fernando J. Iglesias Garcia:
Tapkee: an efficient dimension reduction library. 2355-2359 - Arnaud Guyader, Nick Hengartner:
On the mutual nearest neighbors estimate in regression. 2361-2376 - Nakul Verma:
Distance preserving embeddings for general n-dimensional manifolds. 2415-2448 - Julien Mairal, Bin Yu:
Supervised feature selection in graphs with path coding penalties and network flows. 2449-2485 - Eva L. Dyer, Aswin C. Sankaranarayanan, Richard G. Baraniuk:
Greedy feature selection for subspace clustering. 2487-2517 - Wei Wu, Zhengdong Lu, Hang Li:
Learning bilinear model for matching queries and documents. 2519-2548 - Jun Wan, Qiuqi Ruan, Wei Li, Shuang Deng:
One-shot learning gesture recognition from RGB-D data using bag of features. 2549-2582 - Alon Gonen, Sivan Sabato, Shai Shalev-Shwartz:
Efficient active learning of halfspaces: an aggressive approach. 2583-2615 - Sean Ryan Fanello, Ilaria Gori, Giorgio Metta, Francesca Odone:
Keep it simple and sparse: real-time action recognition. 2617-2640 - Gang Niu, Bo Dai, Lin Shang, Masashi Sugiyama:
Maximum volume clustering: a new discriminative clustering approach. 2641-2687 - Aleksandr Y. Aravkin, James V. Burke, Gianluigi Pillonetto:
Sparse/robust estimation and Kalman smoothing with nonsmooth log-concave densities: modeling, computation, and theory. 2689-2728 - Shusen Wang, Zhihua Zhang:
Improving CUR matrix decomposition and the Nyström approximation via adaptive sampling. 2729-2769 - Philemon Brakel, Dirk Stroobandt, Benjamin Schrauwen:
Training energy-based models for time-series imputation. 2771-2797 - Nima Noorshams, Martin J. Wainwright:
Belief propagation for continuous state spaces: stochastic message-passing with quantitative guarantees. 2799-2835 - Daniil Ryabko, Jérémie Mary:
A binary-classification-based metric between time-series distributions and its use in statistical and learning problems. 2837-2856 - Manfred Opper, Ulrich Paquet, Ole Winther:
Perturbative corrections for approximate inference in Gaussian latent variable models. 2857-2898 - Niya Wang, Fan Meng, Li Chen, Subha Madhavan, Robert Clarke, Eric P. Hoffman, Jianhua Xuan, Yue Joseph Wang:
The CAM software for nonnegative blind source separation in R-Java. 2899-2903 - Kamalika Chaudhuri, Anand D. Sarwate, Kaushik Sinha:
A near-optimal algorithm for differentially-private principal components. 2905-2943 - Binbin Lin, Xiaofei He, Chiyuan Zhang, Ming Ji:
Parallel vector field embedding. 2945-2977 - Pinghua Gong, Jieping Ye, Changshui Zhang:
Multi-stage multi-task feature learning. 2979-3010 - Xin Tong:
A plug-in approach to neyman-pearson classification. 3011-3040 - Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer:
Experiment selection for causal discovery. 3041-3071 - Yuejia He, Yiyuan She, Dapeng Wu:
Stationary-sparse causality network learning. 3073-3104 - Philip M. Long, Rocco A. Servedio:
Algorithms and hardness results for parallel large margin learning. 3105-3128 - Ameet Talwalkar, Sanjiv Kumar, Mehryar Mohri, Henry A. Rowley:
Large-scale SVD and manifold learning. 3129-3152 - Ansaf Salleb-Aouissi, Christel Vrain, Cyril Nortet, Xiangrong Kong, Vivek Rathod, Daniel Cassard:
QuantMiner for mining quantitative association rules. 3153-3157 - Joshua M. Lewis, Virginia R. de Sa, Laurens van der Maaten:
Divvy: fast and intuitive exploratory data analysis. 3159-3163 - Qiang Liu, Alexander Ihler:
Variational algorithms for marginal MAP. 3165-3200 - Andrea Tacchetti, Pavan Kumar Mallapragada, Matteo Santoro, Lorenzo Rosasco:
GURLS: a least squares library for supervised learning. 3201-3205 - Léon Bottou, Jonas Peters, Joaquin Quiñonero Candela, Denis Xavier Charles, Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Y. Simard, Ed Snelson:
Counterfactual reasoning and learning systems: the example of computational advertising. 3207-3260 - Lauren Hannah, David B. Dunson:
Multivariate convex regression with adaptive partitioning. 3261-3294 - Vinayak A. Rao, Yee Whye Teh:
Fast MCMC sampling for Markov jump processes and extensions. 3295-3320 - Yuchen Zhang, John C. Duchi, Martin J. Wainwright:
Communication-efficient algorithms for statistical optimization. 3321-3363 - Naftali Harris, Mathias Drton:
PC algorithm for nonparanormal graphical models. 3365-3383 - Tingni Sun, Cun-Hui Zhang:
Sparse matrix inversion with scaled Lasso. 3385-3418 - Wei Sun, Junhui Wang, Yixin Fang:
Consistent selection of tuning parameters via variable selection stability. 3419-3440 - Cynthia Rudin, Benjamin Letham, David Madigan:
Learning theory analysis for association rules and sequential event prediction. 3441-3492 - Yahya Forghani, Hadi Sadoghi Yazdi:
Comment on "Robustness and regularization of support vector machines" by H. Xu et al. (Journal of machine learning research, volume 10, pp 1485-1510, 2009). 3493-3494 - Vinay Jethava, Anders Martinsson, Chiranjib Bhattacharyya, Devdatt P. Dubhashi:
Lovász ϑ function, SVMs and finding dense subgraphs. 3495-3536 - Alexander Clark:
Learning trees from strings: a strong learning algorithm for some context-free grammars. 3537-3559 - Nathan Parrish, Hyrum S. Anderson, Maya R. Gupta, Dun-Yu Hsiao:
Classifying with confidence from incomplete information. 3561-3589 - Ran Gilad-Bachrach, Christopher J. C. Burges:
Classifier selection using the predicate depth. 3591-3618 - Tony Cai, Wen-Xin Zhou:
A max-norm constrained minimization approach to 1-bit matrix completion. 3619-3647 - John Ahlgren, Shiu Yin Yuen:
Efficient program synthesis using constraint satisfaction in inductive logic programming. 3649-3682 - Alberto N. Escalante, Laurenz Wiskott:
How to solve classification and regression problems on high-dimensional data with a supervised extension of slow feature analysis. 3683-3719 - Mark Vere Culp, Kenneth Joseph Ryan:
Joint harmonic functions and their supervised connections. 3721-3752 - Kenji Fukumizu, Le Song, Arthur Gretton:
Kernel Bayes' rule: Bayesian inference with positive definite kernels. 3753-3783 - Karthik H. Shankar, Marc W. Howard:
Optimally fuzzy temporal memory. 3785-3812 - Nemanja Djuric, Liang Lan, Slobodan Vucetic, Zhuang Wang:
BudgetedSVM: a toolbox for scalable SVM approximations. 3813-3817
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