Journal of Machine Learning Research, Volume 10
Volume 10, 2009
Hugo Larochelle, Yoshua Bengio, Jérôme Louradour, Pascal Lamblin: Exploring Strategies for Training Deep Neural Networks. 1-40- M. Pawan Kumar, Vladimir Kolmogorov, Philip H. S. Torr: An Analysis of Convex Relaxations for MAP Estimation of Discrete MRFs. 71-106
- Xiaogang Su, Chih-Ling Tsai, Hansheng Wang, David M. Nickerson, Bogong Li: Subgroup Analysis via Recursive Partitioning. 141-158
- Abhik Shah, Peter J. Woolf: Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data. 159-162
- Vitaly Feldman: On The Power of Membership Queries in Agnostic Learning. 163-182
- Volkan Vural, Glenn Fung, Balaji Krishnapuram, Jennifer G. Dy, R. Bharat Rao: Using Local Dependencies within Batches to Improve Large Margin Classifiers. 183-206
- Kilian Q. Weinberger, Lawrence K. Saul: Distance Metric Learning for Large Margin Nearest Neighbor Classification. 207-244
- Sylvain Arlot, Pascal Massart: Data-driven Calibration of Penalties for Least-Squares Regression. 245-279
- Sanjoy Dasgupta, Adam Tauman Kalai, Claire Monteleoni: Analysis of Perceptron-Based Active Learning. 281-299
- Facundo Bromberg, Dimitris Margaritis: Improving the Reliability of Causal Discovery from Small Data Sets Using Argumentation. 301-340
- Brian Kulis, Mátyás A. Sustik, Inderjit S. Dhillon: Low-Rank Kernel Learning with Bregman Matrix Divergences. 341-376
- Petra Kralj Novak, Nada Lavrac, Geoffrey I. Webb: Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining. 377-403
- Hugo Jair Escalante, Manuel Montes-y-Gómez, Luis Enrique Sucar: Particle Swarm Model Selection. 405-440
- Shivani Agarwal, Partha Niyogi: Generalization Bounds for Ranking Algorithms via Algorithmic Stability. 441-474
- Junning Li, Z. Jane Wang: Controlling the False Discovery Rate of the Association/Causality Structure Learned with the PC Algorithm. 475-514
- Barnabás Póczos, András Lörincz: Identification of Recurrent Neural Networks by Bayesian Interrogation Techniques. 515-554
- Tong Zhang: On the Consistency of Feature Selection using Greedy Least Squares Regression. 555-568
- Pradip Ghanty, Samrat Paul, Nikhil R. Pal: NEUROSVM: An Architecture to Reduce the Effect of the Choice of Kernel on the Performance of SVM. 591-622
- Gábor Takács, István Pilászy, Bottyán Németh, Domonkos Tikk: Scalable Collaborative Filtering Approaches for Large Recommender Systems. 623-656
- Sébastien Bubeck, Ulrike von Luxburg: Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions. 657-698
- Tyler J. VanderWeele, James M. Robins: Properties of Monotonic Effects on Directed Acyclic Graphs. 699-718
- Junhui Wang, Xiaotong Shen, Wei Pan: On Efficient Large Margin Semisupervised Learning: Method and Theory. 719-742
- Francis Maes: Nieme: Large-Scale Energy-Based Models. 743-746
- Yihua Chen, Eric K. Garcia, Maya R. Gupta, Ali Rahimi, Luca Cazzanti: Similarity-based Classification: Concepts and Algorithms. 747-776
- Jacob Abernethy, Francis Bach, Theodoros Evgeniou, Jean-Philippe Vert: A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization. 803-826
- Leslie Foster, Alex Waagen, Nabeela Aijaz, Michael Hurley, Apolonio Luis, Joel Rinsky, Chandrika Satyavolu, Michael J. Way, Paul R. Gazis, Ashok Srivastava: Stable and Efficient Gaussian Process Calculations. 857-882
- Holger Höfling, Robert Tibshirani: Estimation of Sparse Binary Pairwise Markov Networks using Pseudo-likelihoods. 883-906
- André F. T. Martins, Noah A. Smith, Eric P. Xing, Pedro M. Q. Aguiar, Mário A. T. Figueiredo: Nonextensive Information Theoretic Kernels on Measures. 935-975
- Wenxin Jiang: On Uniform Deviations of General Empirical Risks with Unboundedness, Dependence, and High Dimensionality. 977-996
- Jonathan Huang, Carlos Guestrin, Leonidas J. Guibas: Fourier Theoretic Probabilistic Inference over Permutations. 997-1070
- José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér: An Algorithm for Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity. 1071-1094
- Leonid Kontorovich, Boaz Nadler: Universal Kernel-Based Learning with Applications to Regular Languages. 1095-1129
- Hui Li, Xuejun Liao, Lawrence Carin: Multi-task Reinforcement Learning in Partially Observable Stochastic Environments. 1131-1186
- Ricardo Silva, Zoubin Ghahramani: The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models. 1187-1238
- Charles Dugas, Yoshua Bengio, François Bélisle, Claude Nadeau, René Garcia: Incorporating Functional Knowledge in Neural Networks. 1239-1262
- Ulrich Paquet, Ole Winther, Manfred Opper: Perturbation Corrections in Approximate Inference: Mixture Modelling Applications. 1263-1304
- Stijn Goedertier, David Martens, Jan Vanthienen, Bart Baesens: Robust Process Discovery with Artificial Negative Events. 1305-1340
- Eugene Tuv, Alexander Borisov, George C. Runger, Kari Torkkola: Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination. 1341-1366
- Marc Boullé: A Parameter-Free Classification Method for Large Scale Learning. 1367-1385
- Troy Raeder, Nitesh V. Chawla: Model Monitor (M2): Evaluating, Comparing, and Monitoring Models. 1387-1390
- Takafumi Kanamori, Shohei Hido, Masashi Sugiyama: A Least-squares Approach to Direct Importance Estimation. 1391-1445
- Jean Hausser, Korbinian Strimmer: Entropy Inference and the James-Stein Estimator, with Application to Nonlinear Gene Association Networks. 1469-1484
- Huan Xu, Constantine Caramanis, Shie Mannor: Robustness and Regularization of Support Vector Machines. 1485-1510
- Nikolai Slobodianik, Dmitry Zaporozhets, Neal Madras: Strong Limit Theorems for the Bayesian Scoring Criterion in Bayesian Networks. 1511-1526
- Raanan Yehezkel, Boaz Lerner: Bayesian Network Structure Learning by Recursive Autonomy Identification. 1527-1570
- Yuehua Xu, Alan Fern, Sung Wook Yoon: Learning Linear Ranking Functions for Beam Search with Application to Planning. 1571-1610
- Shaowei Lin, Bernd Sturmfels, Zhiqiang Xu: Marginal Likelihood Integrals for Mixtures of Independence Models. 1611-1631
- Matthew E. Taylor, Peter Stone: Transfer Learning for Reinforcement Learning Domains: A Survey. 1633-1685
- Eitan Greenshtein, Junyong Park: Application of Non Parametric Empirical Bayes Estimation to High Dimensional Classification. 1687-1704
- Antoine Bordes, Léon Bottou, Patrick Gallinari: SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent. 1737-1754
- Davis E. King: Dlib-ml: A Machine Learning Toolkit. 1755-1758
- Halbert White, Karim Chalak: Settable Systems: An Extension of Pearl's Causal Model with Optimization, Equilibrium, and Learning. 1759-1799
- David Newman, Arthur U. Asuncion, Padhraic Smyth, Max Welling: Distributed Algorithms for Topic Models. 1801-1828
- Roberto Esposito, Daniele P. Radicioni: CarpeDiem: Optimizing the Viterbi Algorithm and Applications to Supervised Sequential Learning. 1851-1880
- Dana Angluin, James Aspnes, Jiang Chen, David Eisenstat, Lev Reyzin: Learning Acyclic Probabilistic Circuits Using Test Paths. 1881-1911
- Zeeshan Syed, Piotr Indyk, John V. Guttag: Learning Approximate Sequential Patterns for Classification. 1913-1936
- Kristian Woodsend, Jacek Gondzio: Hybrid MPI/OpenMP Parallel Linear Support Vector Machine Training. 1937-1953
- Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, Nicholas Roy: Provably Efficient Learning with Typed Parametric Models. 1955-1988
- Jie Chen, Haw-ren Fang, Yousef Saad: Fast Approximate kNN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection. 1989-2012
- Jianqing Fan, Richard Samworth, Yichao Wu: Ultrahigh Dimensional Feature Selection: Beyond The Linear Model. 2013-2038
- Dirk Gorissen, Tom Dhaene, Filip De Turck: Evolutionary Model Type Selection for Global Surrogate Modeling. 2039-2078
- Luciana Ferrer, M. Kemal Sönmez, Elizabeth Shriberg: An Anticorrelation Kernel for Subsystem Training in Multiple Classifier Systems. 2079-2114
- Christian Rieger, Barbara Zwicknagl: Deterministic Error Analysis of Support Vector Regression and Related Regularized Kernel Methods. 2115-2132
- Brian Tanner, Adam M. White: RL-Glue: Language-Independent Software for Reinforcement-Learning Experiments. 2133-2136
- Steffen Bickel, Michael Brückner, Tobias Scheffer: Discriminative Learning Under Covariate Shift. 2137-2155
- Vojtech Franc, Sören Sonnenburg: Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization. 2157-2192
- Cynthia Rudin, Robert E. Schapire: Margin-based Ranking and an Equivalence between AdaBoost and RankBoost. 2193-2232
- Cynthia Rudin: The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List. 2233-2271
- Han Liu, John D. Lafferty, Larry A. Wasserman: The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs. 2295-2328
- Mathias Drton, Michael Eichler, Thomas S. Richardson: Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors. 2329-2348
- Novi Quadrianto, Alex J. Smola, Tibério S. Caetano, Quoc V. Le: Estimating Labels from Label Proportions. 2349-2374
- Lisa Hellerstein, Bernard Rosell, Eric Bach, Soumya Ray, David Page: Exploiting Product Distributions to Identify Relevant Variables of Correlation Immune Functions. 2374-2411
- Alexander L. Strehl, Lihong Li, Michael L. Littman: Reinforcement Learning in Finite MDPs: PAC Analysis. 2413-2444
- Saharon Rosset: Bi-Level Path Following for Cross Validated Solution of Kernel Quantile Regression. 2473-2505
- Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil: When Is There a Representer Theorem? Vector Versus Matrix Regularizers. 2507-2529
- Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alexander J. Smola, S. V. N. Vishwanathan: Hash Kernels for Structured Data. 2615-2637
- Jens Lehmann: DL-Learner: Learning Concepts in Description Logics. 2639-2642
- Adam R. Klivans, Philip M. Long, Rocco A. Servedio: Learning Halfspaces with Malicious Noise. 2715-2740
- Haizhang Zhang, Yuesheng Xu, Jun Zhang: Reproducing Kernel Banach Spaces for Machine Learning. 2741-2775
- Gilles Blanchard, Étienne Roquain: Adaptive False Discovery Rate Control under Independence and Dependence. 2837-2871
- Ting Hu, Ding-Xuan Zhou: Online Learning with Samples Drawn from Non-identical Distributions. 2873-2898
- John C. Duchi, Yoram Singer: Efficient Online and Batch Learning Using Forward Backward Splitting. 2899-2934
- Asela Gunawardana, Guy Shani: A Survey of Accuracy Evaluation Metrics of Recommendation Tasks. 2935-2962
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last updated on 2013-06-18T211851 by the dblp team