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ORCIDJournal 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 - Changsung Kang, Jin Tian:
Markov Properties for Linear Causal Models with Correlated Errors. 41-70 - M. Pawan Kumar, Vladimir Kolmogorov, Philip H. S. Torr:
An Analysis of Convex Relaxations for MAP Estimation of Discrete MRFs. 71-106 - Yuesheng Xu, Haizhang Zhang:
Refinement of Reproducing Kernels. 107-140 - 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 - Shie Mannor, John N. Tsitsiklis, Jia Yuan Yu:
Online Learning with Sample Path Constraints. 569-590 - 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 - John Langford, Lihong Li, Tong Zhang:
Sparse Online Learning via Truncated Gradient. 777-801 - Jacob D. Abernethy, Francis R. Bach, Theodoros Evgeniou, Jean-Philippe Vert:
A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization. 803-826 - Alon Zakai, Yaacov Ritov:
Consistency and Localizability. 827-856 - 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 - Jan Ramon, Siegfried Nijssen:
Polynomial-Delay Enumeration of Monotonic Graph Classes. 907-929 - Thomas Abeel, Yves Van de Peer, Yvan Saeys:
Java-ML: A Machine Learning Library. 931-934 - 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 Bezerra de Andrade e 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 - Dao-Hong Xiang, Ding-Xuan Zhou:
Classification with Gaussians and Convex Loss. 1447-1468 - 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 Yu. 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 - David P. Helmbold, Manfred K. Warmuth:
Learning Permutations with Exponential Weights. 1705-1736 - 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 - Babak Shahbaba, Radford M. Neal:
Nonlinear Models Using Dirichlet Process Mixtures. 1829-1850 - Roberto Esposito, Daniele Paolo 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 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 - Juan José del Coz, Jorge Díez, Antonio Bahamonde:
Learning Nondeterministic Classifiers. 2273-2293 - 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, Alexander 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 - Vladimir Vovk, Fedor Zhdanov:
Prediction With Expert Advice For The Brier Game. 2445-2471 - 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 - Jun Zhu, Eric P. Xing:
Maximum Entropy Discrimination Markov Networks. 2531-2569 - Omid Madani, Michael Connor, Wiley Greiner:
Learning When Concepts Abound. 2571-2613 - 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 - Francesco Orabona, Joseph Keshet, Barbara Caputo:
Bounded Kernel-Based Online Learning. 2643-2666 - Brijnesh J. Jain, Klaus Obermayer:
Structure Spaces. 2667-2714 - 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 - Luke K. McDowell, Kalyan Moy Gupta, David W. Aha:
Cautious Collective Classification. 2777-2836 - 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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