ML Anthology

JMLR 2010

108 papers

A Comparison of Optimization Methods and Software for Large-Scale L1-Regularized Linear Classification Guo-Xun Yuan, Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin
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A Convergent Online Single Time Scale Actor Critic Algorithm Dotan Di Castro, Ron Meir
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A Fast Hybrid Algorithm for Large-Scale L1-Regularized Logistic Regression Jianing Shi, Wotao Yin, Stanley Osher, Paul Sajda
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A Generalized Path Integral Control Approach to Reinforcement Learning Evangelos Theodorou, Jonas Buchli, Stefan Schaal
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A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning Jin Yu, S.V.N. Vishwanathan, Simon Günter, Nicol N. Schraudolph
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A Rotation Test to Verify Latent Structure Patrick O. Perry, Art B. Owen
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A Streaming Parallel Decision Tree Algorithm Yael Ben-Haim, Elad Tom-Tov
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An Efficient Explanation of Individual Classifications Using Game Theory Erik Štrumbelj, Igor Kononenko
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An Exponential Model for Infinite Rankings Marina Meilă, Le Bao
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An Investigation of Missing Data Methods for Classification Trees Applied to Binary Response Data Yufeng Ding, Jeffrey S. Simonoff
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Analysis of Multi-Stage Convex Relaxation for Sparse Regularization Tong Zhang
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Approximate Inference on Planar Graphs Using Loop Calculus and Belief Propagation Vicenç Gómez, Hilbert J. Kappen, Michael Chertkov
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Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes Antti Honkela, Tapani Raiko, Mikael Kuusela, Matti Tornio, Juha Karhunen
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Approximate Tree Kernels Konrad Rieck, Tammo Krueger, Ulf Brefeld, Klaus-Robert Müller
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Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory Sumio Watanabe
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Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing Ryo Yoshida, Mike West
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Bundle Methods for Regularized Risk Minimization Choon Hui Teo, S.V.N. Vishwanthan, Alex J. Smola, Quoc V. Le
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Characterization, Stability and Convergence of Hierarchical Clustering Methods Gunnar Carlsson, Facundo Mémoli
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Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary -Mixing Processes Liva Ralaivola, Marie Szafranski, Guillaume Stempfel
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Classification Methods with Reject Option Based on Convex Risk Minimization Ming Yuan, Marten Wegkamp
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Classification Using Geometric Level Sets Kush R. Varshney, Alan S. Willsky
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Classification with Incomplete Data Using Dirichlet Process Priors Chunping Wang, Xuejun Liao, Lawrence Carin, David B. Dunson
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Collective Inference for Extraction MRFs Coupled with Symmetric Clique Potentials Rahul Gupta, Sunita Sarawagi, Ajit A. Diwan
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Composite Binary Losses Mark D. Reid, Robert C. Williamson
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Consensus-Based Distributed Support Vector Machines Pedro A. Forero, Alfonso Cano, Georgios B. Giannakis
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Consistent Nonparametric Tests of Independence Arthur Gretton, László Györfi
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Covariance in Unsupervised Learning of Probabilistic Grammars Shay B. Cohen, Noah A. Smith
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Dimensionality Estimation, Manifold Learning and Function Approximation Using Tensor Voting Philippos Mordohai, Gérard Medioni
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Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization Lin Xiao
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Efficient Algorithms for Conditional Independence Inference Remco Bouckaert, Raymond Hemmecke, Silvia Lindner, Milan Studený
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Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers Franz Pernkopf, Jeff A. Bilmes
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Erratum: SGDQN Is Less Careful than Expected Antoine Bordes, Léon Bottou, Patrick Gallinari, Jonathan Chang, S. Alex Smith
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Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, Patrik O. Hoyer
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Evolving Static Representations for Task Transfer Phillip Verbancsics, Kenneth O. Stanley
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Expectation Truncation and the Benefits of Preselection in Training Generative Models Jörg Lücke, Julian Eggert
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Fast and Scalable Local Kernel Machines Nicola Segata, Enrico Blanzieri
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Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data Gideon S. Mann, Andrew McCallum
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Generalized Power Method for Sparse Principal Component Analysis Michel Journée, Yurii Nesterov, Peter Richtárik, Rodolphe Sepulchre
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Graph Kernels S.V.N. Vishwanathan, Nicol N. Schraudolph, Risi Kondor, Karsten M. Borgwardt
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High Dimensional Inverse Covariance Matrix Estimation via Linear Programming Ming Yuan
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High-Dimensional Variable Selection with Sparse Random Projections: Measurement Sparsity and Statistical Efficiency Dapo Omidiran, Martin J. Wainwright
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Hilbert Space Embeddings and Metrics on Probability Measures Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Bernhard Schölkopf, Gert R.G. Lanckriet
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How to Explain Individual Classification Decisions David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, Klaus-Robert Müller
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Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data Miloš Radovanović, Alexandros Nanopoulos, Mirjana Ivanović
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Image Denoising with Kernels Based on Natural Image Relations Valero Laparra, Juan Gutiérrez, Gustavo Camps-Valls, Jesús Malo
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Importance Sampling for Continuous Time Bayesian Networks Yu Fan, Jing Xu, Christian R. Shelton
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Incremental Sigmoid Belief Networks for Grammar Learning James Henderson, Ivan Titov
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Inducing Tree-Substitution Grammars Trevor Cohn, Phil Blunsom, Sharon Goldwater
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Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization Jarkko Venna, Jaakko Peltonen, Kristian Nybo, Helena Aidos, Samuel Kaski
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Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance Nguyen Xuan Vinh, Julien Epps, James Bailey
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Introduction to Causal Inference Peter Spirtes
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Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models Fang-Lan Huang, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin
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Kronecker Graphs: An Approach to Modeling Networks Jure Leskovec, Deepayan Chakrabarti, Jon Kleinberg, Christos Faloutsos, Zoubin Ghahramani
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Large Scale Online Learning of Image Similarity Through Ranking Gal Chechik, Varun Sharma, Uri Shalit, Samy Bengio
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Learnability, Stability and Uniform Convergence Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan
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Learning from Crowds Vikas C. Raykar, Shipeng Yu, Linda H. Zhao, Gerardo Hermosillo Valadez, Charles Florin, Luca Bogoni, Linda Moy
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Learning Gradients: Predictive Models That Infer Geometry and Statistical Dependence Qiang Wu, Justin Guinney, Mauro Maggioni, Sayan Mukherjee
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Learning Instance-Specific Predictive Models Shyam Visweswaran, Gregory F. Cooper
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Learning Non-Stationary Dynamic Bayesian Networks Joshua W. Robinson, Alexander J. Hartemink
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Learning Translation Invariant Kernels for Classification Kamaledin Ghiasi-Shirazi, Reza Safabakhsh, Mostafa Shamsi
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Linear Algorithms for Online Multitask Classification Giovanni Cavallanti, Nicoló Cesa-Bianchi, Claudio Gentile
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Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation Constantin F. Aliferis, Alexander Statnikov, Ioannis Tsamardinos, Subramani Mani, Xenofon D. Koutsoukos
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Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part II: Analysis and Extensions Constantin F. Aliferis, Alexander Statnikov, Ioannis Tsamardinos, Subramani Mani, Xenofon D. Koutsoukos
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Lp-Nested Symmetric Distributions Fabian Sinz, Matthias Bethge
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Matched Gene Selection and Committee Classifier for Molecular Classification of Heterogeneous Diseases Guoqiang Yu, Yuanjian Feng, David J. Miller, Jianhua Xuan, Eric P. Hoffman, Robert Clarke, Ben Davidson, Ie-Ming Shih, Yue Wang
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Matrix Completion from Noisy Entries Raghunandan H. Keshavan, Andrea Montanari, Sewoong Oh
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Maximum Likelihood in Cost-Sensitive Learning: Model Specification, Approximations, and Upper Bounds Jacek P. Dmochowski, Paul Sajda, Lucas C. Parra
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Maximum Relative Margin and Data-Dependent Regularization Pannagadatta K. Shivaswamy, Tony Jebara
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Mean Field Variational Approximation for Continuous-Time Bayesian Networks Ido Cohn, Tal El-Hay, Nir Friedman, Raz Kupferman
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Message-Passing for Graph-Structured Linear Programs: Proximal Methods and Rounding Schemes Pradeep Ravikumar, Alekh Agarwal, Martin J. Wainwright
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Model Selection: Beyond the Bayesian/Frequentist Divide Isabelle Guyon, Amir Saffari, Gideon Dror, Gavin Cawley
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Near-Optimal Regret Bounds for Reinforcement Learning Thomas Jaksch, Ronald Ortner, Peter Auer
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On Finding Predictors for Arbitrary Families of Processes Daniil Ryabko
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On Learning with Integral Operators Lorenzo Rosasco, Mikhail Belkin, Ernesto De Vito
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On Over-Fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation Gavin C. Cawley, Nicola L. C. Talbot
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On Spectral Learning Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil
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On the Foundations of Noise-Free Selective Classification Ran El-Yaniv, Yair Wiener
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On the Rate of Convergence of the Bagged Nearest Neighbor Estimate Gérard Biau, Frédéric Cérou, Arnaud Guyader
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On-Line Sequential Bin Packing András György, Gábor Lugosi, György Ottucsàk
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Online Learning for Matrix Factorization and Sparse Coding Julien Mairal, Francis Bach, Jean Ponce, Guillermo Sapiro
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Optimal Search on Clustered Structural Constraint for Learning Bayesian Network Structure Kaname Kojima, Eric Perrier, Seiya Imoto, Satoru Miyano
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PAC-Bayesian Analysis of Co-Clustering and Beyond Yevgeny Seldin, Naftali Tishby
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Permutation Tests for Studying Classifier Performance Markus Ojala, Gemma C. Garriga
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Posterior Regularization for Structured Latent Variable Models Kuzman Ganchev, João Graça, Jennifer Gillenwater, Ben Taskar
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Practical Approaches to Principal Component Analysis in the Presence of Missing Values Alexander Ilin, Tapani Raiko
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PyBrain Tom Schaul, Justin Bayer, Daan Wierstra, Yi Sun, Martin Felder, Frank Sehnke, Thomas Rückstieß, Jürgen Schmidhuber
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Quadratic Programming Feature Selection Irene Rodriguez-Lujan, Ramon Huerta, Charles Elkan, Carlos Santa Cruz
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Rademacher Complexities and Bounding the Excess Risk in Active Learning Vladimir Koltchinskii
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Rate Minimaxity of the Lasso and Dantzig Selector for the Lq Loss in Lr Balls Fei Ye, Cun-Hui Zhang
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Regret Bounds and Minimax Policies Under Partial Monitoring Jean-Yves Audibert, Sébastien Bubeck
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Regularized Discriminant Analysis, Ridge Regression and Beyond Zhihua Zhang, Guang Dai, Congfu Xu, Michael I. Jordan
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Restricted Eigenvalue Properties for Correlated Gaussian Designs Garvesh Raskutti, Martin J. Wainwright, Bin Yu
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Second-Order Bilinear Discriminant Analysis Christoforos Christoforou, Robert Haralick, Paul Sajda, Lucas C. Parra
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Semi-Supervised Novelty Detection Gilles Blanchard, Gyemin Lee, Clayton Scott
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Sparse Semi-Supervised Learning Using Conjugate Functions Shiliang Sun, John Shawe-Taylor
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Sparse Spectrum Gaussian Process Regression Miguel Lázaro-Gredilla, Joaquin Quiñnero-Candela, Carl Edward Rasmussen, Aníbal R. Figueiras-Vidal
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Spectral Regularization Algorithms for Learning Large Incomplete Matrices Rahul Mazumder, Trevor Hastie, Robert Tibshirani
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Stability Bounds for Stationary -mixing and -mixing Processes Mehryar Mohri, Afshin Rostamizadeh
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Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol
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Stochastic Complexity and Generalization Error of a Restricted Boltzmann Machine in Bayesian Estimation Miki Aoyagi
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Stochastic Composite Likelihood Joshua V. Dillon, Guy Lebanon
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Topology Selection in Graphical Models of Autoregressive Processes Jitkomut Songsiri, Lieven Vandenberghe
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Training and Testing Low-Degree Polynomial Data Mappings via Linear SVM Yin-Wen Chang, Cho-Jui Hsieh, Kai-Wei Chang, Michael Ringgaard, Chih-Jen Lin
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Tree Decomposition for Large-Scale SVM Problems Fu Chang, Chien-Yang Guo, Xiao-Rong Lin, Chi-Jen Lu
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Unsupervised Supervised Learning I: Estimating Classification and Regression Errors Without Labels Pinar Donmez, Guy Lebanon, Krishnakumar Balasubramanian
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Using Contextual Representations to Efficiently Learn Context-Free Languages Alexander Clark, Rémi Eyraud, Amaury Habrard
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WEKA−Experiences with a Java Open-Source Project Remco R. Bouckaert, Eibe Frank, Mark A. Hall, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten
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Why Does Unsupervised Pre-Training Help Deep Learning? Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, Samy Bengio
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