Journal of Machine Learning Research, Volume 11
Volume 11, 2010
Erik Strumbelj, Igor Kononenko: An Efficient Explanation of Individual Classifications using Game Theory. 1-18- Julien Mairal, Francis Bach, Jean Ponce, Guillermo Sapiro: Online Learning for Matrix Factorization and Sparse Coding. 19-60
- Isabelle Guyon, Amir Saffari, Gideon Dror, Gavin C. Cawley: Model Selection: Beyond the Bayesian/Frequentist Divide. 61-87
- Ming Yuan, Marten H. Wegkamp: Classification Methods with Reject Option Based on Convex Risk Minimization. 111-130
- Yufeng Ding, Jeffrey S. Simonoff: An Investigation of Missing Data Methods for Classification Trees Applied to Binary Response Data. 131-170
- Constantin F. Aliferis, Alexander R. Statnikov, Ioannis Tsamardinos, Subramani Mani, Xenofon D. Koutsoukos: Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation. 171-234
- Constantin F. Aliferis, Alexander R. Statnikov, Ioannis Tsamardinos, Subramani Mani, Xenofon D. Koutsoukos: Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part II: Analysis and Extensions. 235-284
- Kaname Kojima, Eric Perrier, Seiya Imoto, Satoru Miyano: Optimal Search on Clustered Structural Constraint for Learning Bayesian Network Structure. 285-310
- Choon Hui Teo, S. V. N. Vishwanathan, Alex J. Smola, Quoc V. Le: Bundle Methods for Regularized Risk Minimization. 311-365
- Philippos Mordohai, Gérard G. Medioni: Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting. 411-450
- Jarkko Venna, Jaakko Peltonen, Kristian Nybo, Helena Aidos, Samuel Kaski: Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization. 451-490
- Michel Journée, Yurii Nesterov, Peter Richtárik, Rodolphe Sepulchre: Generalized Power Method for Sparse Principal Component Analysis. 517-553
- Daniil Ryabko: On Finding Predictors for Arbitrary Families of Processes. 581-602
- Dumitru Erhan, Yoshua Bengio, Aaron C. Courville, Pierre-Antoine Manzagol, Pascal Vincent, Samy Bengio: Why Does Unsupervised Pre-training Help Deep Learning? 625-660
- Christoforos Christoforou, Robert M. Haralick, Paul Sajda, Lucas C. Parra: Second-Order Bilinear Discriminant Analysis. 665-685
- Gérard Biau, Frédéric Cérou, Arnaud Guyader: On the Rate of Convergence of the Bagged Nearest Neighbor Estimate. 687-712
- Jianing Shi, Wotao Yin, Stanley Osher, Paul Sajda: A Fast Hybrid Algorithm for Large-Scale l1-Regularized Logistic Regression. 713-741
- Tom Schaul, Justin Bayer, Daan Wierstra, Yi Sun, Martin Felder, Frank Sehnke, Thomas Rückstieß, Jürgen Schmidhuber: PyBrain. 743-746
- Pannagadatta K. Shivaswamy, Tony Jebara: Maximum Relative Margin and Data-Dependent Regularization. 747-788
- Mehryar Mohri, Afshin Rostamizadeh: Stability Bounds for Stationary phi-mixing and beta-mixing Processes. 789-814
- Fang-Lan Huang, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin: Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models. 815-848
- Valero Laparra, Juan Gutierrez, Gustavo Camps-Valls, Jesús Malo: Image Denoising with Kernels Based on Natural Image Relations. 873-903
- Gideon S. Mann, Andrew McCallum: Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data. 955-984
- Jure Leskovec, Deepayan Chakrabarti, Jon M. Kleinberg, Christos Faloutsos, Zoubin Ghahramani: Kronecker Graphs: An Approach to Modeling Networks. 985-1042
- Pradeep D. Ravikumar, Alekh Agarwal, Martin J. Wainwright: Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes. 1043-1080
- Tong Zhang: Analysis of Multi-stage Convex Relaxation for Sparse Regularization. 1081-1107
- Gal Chechik, Varun Sharma, Uri Shalit, Samy Bengio: Large Scale Online Learning of Image Similarity Through Ranking. 1109-1135
- Christian R. Shelton, Yu Fan, William Lam, Joon Lee, Jing Xu: Continuous Time Bayesian Network Reasoning and Learning Engine. 1137-1140
- Andreas Krause: SFO: A Toolbox for Submodular Function Optimization. 1141-1144
- Jin Yu, S. V. N. Vishwanathan, Simon Günter, Nicol N. Schraudolph: A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning. 1145-1200
- S. V. N. Vishwanathan, Nicol N. Schraudolph, Risi Kondor, Karsten M. Borgwardt: Graph Kernels. 1201-1242
- Miki Aoyagi: Stochastic Complexity and Generalization Error of a Restricted Boltzmann Machine in Bayesian Estimation. 1243-1272
- Vicenç Gómez, Hilbert J. Kappen, Michael Chertkov: Approximate Inference on Planar Graphs using Loop Calculus and Belief Propagation. 1273-1296
- Vikas C. Raykar, Shipeng Yu, Linda H. Zhao, Gerardo Hermosillo Valadez, Charles Florin, Luca Bogoni, Linda Moy: Learning From Crowds. 1297-1322
- Pinar Donmez, Guy Lebanon, Krishnakumar Balasubramanian: Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels. 1323-1351
- Sayed Kamaledin Ghiasi Shirazi, Reza Safabakhsh, Mostafa Shamsi: Learning Translation Invariant Kernels for Classification. 1353-1390
- Gunnar E. Carlsson, Facundo Mémoli: Characterization, Stability and Convergence of Hierarchical Clustering Methods. 1425-1470
- Yin-Wen Chang, Cho-Jui Hsieh, Kai-Wei Chang, Michael Ringgaard, Chih-Jen Lin: Training and Testing Low-degree Polynomial Data Mappings via Linear SVM. 1471-1490
- Irene Rodriguez-Lujan, Ramón Huerta, Charles Elkan, Carlos Santa Cruz: Quadratic Programming Feature Selection. 1491-1516
- Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Bernhard Schölkopf, Gert R. G. Lanckriet: Hilbert Space Embeddings and Metrics on Probability Measures. 1517-1561
- Thomas Jaksch, Ronald Ortner, Peter Auer: Near-optimal Regret Bounds for Reinforcement Learning. 1563-1600
- Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer: MOA: Massive Online Analysis. 1601-1604
- Peter Spirtes: Introduction to Causal Inference. 1643-1662
- Pedro A. Forero, Alfonso Cano, Georgios B. Giannakis: Consensus-Based Distributed Support Vector Machines. 1663-1707
- Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, Patrik O. Hoyer: Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity. 1709-1731
- Ariel Jaimovich, Ofer Meshi, Ian McGraw, Gal Elidan: FastInf: An Efficient Approximate Inference Library. 1733-1736
- Phillip Verbancsics, Kenneth O. Stanley: Evolving Static Representations for Task Transfer. 1737-1769
- Ryo Yoshida, Mike West: Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing. 1771-1798
- Sören Sonnenburg, Gunnar Rätsch, Sebastian Henschel, Christian Widmer, Jonas Behr, Alexander Zien, Fabio De Bona, Alexander Binder, Christian Gehl, Vojtech Franc: The SHOGUN Machine Learning Toolbox. 1799-1802
- David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, Klaus-Robert Müller: How to Explain Individual Classification Decisions. 1803-1831
- Miguel Lázaro-Gredilla, Joaquin Quiñonero Candela, Carl Edward Rasmussen, Aníbal R. Figueiras-Vidal: Sparse Spectrum Gaussian Process Regression. 1865-1881
- Liva Ralaivola, Marie Szafranski, Guillaume Stempfel: Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary β-Mixing Processes. 1927-1956
- Alexander Ilin, Tapani Raiko: Practical Approaches to Principal Component Analysis in the Presence of Missing Values. 1957-2000
- Kuzman Ganchev, João Graça, Jennifer Gillenwater, Ben Taskar: Posterior Regularization for Structured Latent Variable Models. 2001-2049
- Dirk Gorissen, Ivo Couckuyt, Piet Demeester, Tom Dhaene, Karel Crombecq: A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design. 2051-2055
- Raghunandan H. Keshavan, Andrea Montanari, Sewoong Oh: Matrix Completion from Noisy Entries. 2057-2078
- Gavin C. Cawley, Nicola L. C. Talbot: On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation. 2079-2107
- Torsten Hothorn, Peter Bühlmann, Thomas Kneib, Matthias Schmid, Benjamin Hofner: Model-based Boosting 2.0. 2109-2113
- Yu Fan, Jing Xu, Christian R. Shelton: Importance Sampling for Continuous Time Bayesian Networks. 2115-2140
- Guoqiang Yu, Yuanjian Feng, David J. Miller, Jianhua Xuan, Eric P. Hoffman, Robert Clarke, Ben Davidson, Ie-Ming Shih, Yue Joseph Wang: Matched Gene Selection and Committee Classifier for Molecular Classification of Heterogeneous Diseases. 2141-2167
- Joris M. Mooij: libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models. 2169-2173
- Qiang Wu, Justin Guinney, Mauro Maggioni, Sayan Mukherjee: Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence. 2175-2198
- Zhihua Zhang, Guang Dai, Congfu Xu, Michael I. Jordan: Regularized Discriminant Analysis, Ridge Regression and Beyond. 2199-2228
- Antoine Bordes, Léon Bottou, Patrick Gallinari, Jonathan Chang, S. Alex Smith: Erratum: SGDQN is Less Careful than Expected. 2229-2240
- Garvesh Raskutti, Martin J. Wainwright, Bin Yu: Restricted Eigenvalue Properties for Correlated Gaussian Designs. 2241-2259
- Ming Yuan: High Dimensional Inverse Covariance Matrix Estimation via Linear Programming. 2261-2286
- Rahul Mazumder, Trevor Hastie, Robert Tibshirani: Spectral Regularization Algorithms for Learning Large Incomplete Matrices. 2287-2322
- Franz Pernkopf, Jeff A. Bilmes: Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers. 2323-2360
- Dapo Omidiran, Martin J. Wainwright: High-dimensional Variable Selection with Sparse Random Projections: Measurement Sparsity and Statistical Efficiency. 2361-2386
- Shiliang Sun, John Shawe-Taylor: Sparse Semi-supervised Learning Using Conjugate Functions. 2423-2455
- Vladimir Koltchinskii: Rademacher Complexities and Bounding the Excess Risk in Active Learning. 2457-2485
- Milos Radovanovic, Alexandros Nanopoulos, Mirjana Ivanovic: Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data. 2487-2531
- Remco R. Bouckaert, Eibe Frank, Mark A. Hall, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten: WEKA - Experiences with a Java Open-Source Project. 2533-2541
- Lin Xiao: Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization. 2543-2596
- Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan: Learnability, Stability and Uniform Convergence. 2635-2670
- Jitkomut Songsiri, Lieven Vandenberghe: Topology Selection in Graphical Models of Autoregressive Processes. 2671-2705
- Alexander Clark, Rémi Eyraud, Amaury Habrard: Using Contextual Representations to Efficiently Learn Context-Free Languages. 2707-2744
- Ido Cohn, Tal El-Hay, Nir Friedman, Raz Kupferman: Mean Field Variational Approximation for Continuous-Time Bayesian Networks. 2745-2783
- Jean-Yves Audibert, Sébastien Bubeck: Regret Bounds and Minimax Policies under Partial Monitoring. 2785-2836
- Xuan Vinh Nguyen, Julien Epps, James Bailey: Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance. 2837-2854
- Jörg Lücke, Julian Eggert: Expectation Truncation and the Benefits of Preselection In Training Generative Models. 2855-2900
- Giovanni Cavallanti, Nicolò Cesa-Bianchi, Claudio Gentile: Linear Algorithms for Online Multitask Classification. 2901-2934
- Fu Chang, Chien-Yang Guo, Xiao-Rong Lin, Chi-Jen Lu: Tree Decomposition for Large-Scale SVM Problems. 2935-2972
- Carl Edward Rasmussen, Hannes Nickisch: Gaussian Processes for Machine Learning (GPML) Toolbox. 3011-3015
- Shay B. Cohen, Noah A. Smith: Covariance in Unsupervised Learning of Probabilistic Grammars. 3017-3051
- Rahul Gupta, Sunita Sarawagi, Ajit A. Diwan: Collective Inference for Extraction MRFs Coupled with Symmetric Clique Potentials. 3097-3135
- Evangelos Theodorou, Jonas Buchli, Stefan Schaal: A Generalized Path Integral Control Approach to Reinforcement Learning. 3137-3181
- Guo-Xun Yuan, Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin: A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification. 3183-3234
- Antti Honkela, Tapani Raiko, Mikael Kuusela, Matti Tornio, Juha Karhunen: Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes. 3235-3268
- Chunping Wang, Xuejun Liao, Lawrence Carin, David B. Dunson: Classification with Incomplete Data Using Dirichlet Process Priors. 3269-3311
- Jacek P. Dmochowski, Paul Sajda, Lucas C. Parra: Maximum Likelihood in Cost-Sensitive Learning: Model Specification, Approximations, and Upper Bounds. 3313-3332
- Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol: Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion. 3371-3408
- Remco R. Bouckaert, Raymond Hemmecke, Silvia Lindner, Milan Studený: Efficient Algorithms for Conditional Independence Inference. 3453-3479
- Fei Ye, Cun-Hui Zhang: Rate Minimaxity of the Lasso and Dantzig Selector for the lq Loss in lr Balls. 3519-3540
- Sumio Watanabe: Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory. 3571-3594
- Joshua W. Robinson, Alexander J. Hartemink: Learning Non-Stationary Dynamic Bayesian Networks. 3647-3680
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last updated on 2013-06-18T214307 by the dblp team