JMLR 2009

100 papers

A Survey of Accuracy Evaluation Metrics of Recommendation Tasks

Asela Gunawardana, Guy Shani

Efficient Online and Batch Learning Using Forward Backward Splitting

John Duchi, Yoram Singer

Online Learning with Samples Drawn from Non-identical Distributions

Ting Hu, Ding-Xuan Zhou

Adaptive False Discovery Rate Control under Independence and Dependence

Gilles Blanchard, &Eacute

Cautious Collective Classification

Luke K. McDowell, Kalyan Moy Gupta, David W. Aha

Reproducing Kernel Banach Spaces for Machine Learning

Haizhang Zhang, Yuesheng Xu, Jun Zhang

Learning Halfspaces with Malicious Noise

Adam R. Klivans, Philip M. Long, Rocco A. Servedio

Structure Spaces

Brijnesh J. Jain, Klaus Obermayer

Bounded Kernel-Based Online Learning

Francesco Orabona, Joseph Keshet, Barbara Caputo

DL-Learner: Learning Concepts in Description Logics

Jens Lehmann

Hash Kernels for Structured Data

Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alex Smola

Learning When Concepts Abound

Omid Madani, Michael Connor, Wiley Greiner

Maximum Entropy Discrimination Markov Networks

Jun Zhu, Eric P. Xing

When Is There a Representer Theorem? Vector Versus Matrix Regularizers

Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil

Bi-Level Path Following for Cross Validated Solution of Kernel Quantile Regression

Saharon Rosset

Prediction With Expert Advice For The Brier Game

Vladimir Vovk, Fedor Zhdanov

Reinforcement Learning in Finite MDPs: PAC Analysis

Alexander L. Strehl, Lihong Li, Michael L. Littman

Exploiting Product Distributions to Identify Relevant Variables of Correlation Immune Functions

Lisa Hellerstein, Bernard Rosell, Eric Bach, Soumya Ray, David Page

Estimating Labels from Label Proportions

Novi Quadrianto, Alex J. Smola, Tibério S. Caetano, Quoc V. Le

Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors

Mathias Drton, Michael Eichler, Thomas S. Richardson

The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs

Han Liu, John Lafferty, Larry Wasserman

Learning Nondeterministic Classifiers

Juan José del Coz, Jorge Díez, Antonio Bahamonde

The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List

Cynthia Rudin

Margin-based Ranking and an Equivalence between AdaBoost and RankBoost

Cynthia Rudin, Robert E. Schapire

Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization

Vojt&#x011B

Discriminative Learning Under Covariate Shift

Steffen Bickel, Michael Brückner, Tobias Scheffer

RL-Glue: Language-Independent Software for Reinforcement-Learning Experiments

Brian Tanner, Adam White

Deterministic Error Analysis of Support Vector Regression and Related Regularized Kernel Methods

Christian Rieger, Barbara Zwicknagl

An Anticorrelation Kernel for Subsystem Training in Multiple Classifier Systems

Luciana Ferrer, Kemal Sönmez, Elizabeth Shriberg

Evolutionary Model Type Selection for Global Surrogate Modeling

Dirk Gorissen, Tom Dhaene, Filip De Turck

Ultrahigh Dimensional Feature Selection: Beyond The Linear Model

Jianqing Fan, Richard Samworth, Yichao Wu

Fast Approximate kNN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection

Jie Chen, Haw-ren Fang, Yousef Saad

Provably Efficient Learning with Typed Parametric Models

Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, Nicholas Roy

Hybrid MPI/OpenMP Parallel Linear Support Vector Machine Training

Kristian Woodsend, Jacek Gondzio

Learning Approximate Sequential Patterns for Classification

Zeeshan Syed, Piotr Indyk, John Guttag

Learning Acyclic Probabilistic Circuits Using Test Paths

Dana Angluin, James Aspnes, Jiang Chen, David Eisenstat, Lev Reyzin

CarpeDiem: Optimizing the Viterbi Algorithm and Applications to Supervised Sequential Learning

Roberto Esposito, Daniele P. Radicioni

Nonlinear Models Using Dirichlet Process Mixtures

Babak Shahbaba, Radford Neal

Distributed Algorithms for Topic Models

David Newman, Arthur Asuncion, Padhraic Smyth, Max Welling

Settable Systems: An Extension of Pearl's Causal Model with Optimization, Equilibrium, and Learning

Halbert White, Karim Chalak

Dlib-ml: A Machine Learning Toolkit

Davis E. King

SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent

Antoine Bordes, Léon Bottou, Patrick Gallinari

Learning Permutations with Exponential Weights

David P. Helmbold, Manfred K. Warmuth

Application of Non Parametric Empirical Bayes Estimation to High Dimensional Classification

Eitan Greenshtein, Junyong Park

Transfer Learning for Reinforcement Learning Domains: A Survey

Matthew E. Taylor, Peter Stone

Marginal Likelihood Integrals for Mixtures of Independence Models

Shaowei Lin, Bernd Sturmfels, Zhiqiang Xu

Learning Linear Ranking Functions for Beam Search with Application to Planning

Yuehua Xu, Alan Fern, Sungwook Yoon

Bayesian Network Structure Learning by Recursive Autonomy Identification

Raanan Yehezkel, Boaz Lerner

Strong Limit Theorems for the Bayesian Scoring Criterion in Bayesian Networks

Nikolai Slobodianik, Dmitry Zaporozhets, Neal Madras

Robustness and Regularization of Support Vector Machines

Huan Xu, Constantine Caramanis, Shie Mannor

Entropy Inference and the James-Stein Estimator, with Application to Nonlinear Gene Association Networks

Jean Hausser, Korbinian Strimmer

Classification with Gaussians and Convex Loss

Dao-Hong Xiang, Ding-Xuan Zhou

A Least-squares Approach to Direct Importance Estimation

Takafumi Kanamori, Shohei Hido, Masashi Sugiyama

Model Monitor (M2): Evaluating, Comparing, and Monitoring Models

Troy Raeder, Nitesh V. Chawla

A Parameter-Free Classification Method for Large Scale Learning

Marc Boullé

Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination

Eugene Tuv, Alexander Borisov, George Runger, Kari Torkkola

Robust Process Discovery with Artificial Negative Events

Stijn Goedertier, David Martens, Jan Vanthienen, Bart Baesens

Perturbation Corrections in Approximate Inference: Mixture Modelling Applications

Ulrich Paquet, Ole Winther, Manfred Opper

Incorporating Functional Knowledge in Neural Networks

Charles Dugas, Yoshua Bengio, François Bélisle, Claude Nadeau, René Garcia

The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models

Ricardo Silva, Zoubin Ghahramani

Multi-task Reinforcement Learning in Partially Observable Stochastic Environments

Hui Li, Xuejun Liao, Lawrence Carin

Universal Kernel-Based Learning with Applications to Regular Languages

Leonid (Aryeh) Kontorovich, Boaz Nadler

An Algorithm for Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity

Jose M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér

Fourier Theoretic Probabilistic Inference over Permutations

Jonathan Huang, Carlos Guestrin, Leonidas Guibas

On Uniform Deviations of General Empirical Risks with Unboundedness, Dependence, and High Dimensionality

Wenxin Jiang

Nonextensive Information Theoretic Kernels on Measures

André F. T. Martins, Noah A. Smith, Eric P. Xing, Pedro M. Q. Aguiar, Mario A. T. Figueiredo

Java-ML: A Machine Learning Library

Thomas Abeel, Yves Van de Peer, Yvan Saeys

Polynomial-Delay Enumeration of Monotonic Graph Classes

Jan Ramon, Siegfried Nijssen

Estimation of Sparse Binary Pairwise Markov Networks using Pseudo-likelihoods

Holger Höfling, Robert Tibshirani

Stable and Efficient Gaussian Process Calculations

Leslie Foster, Alex Waagen, Nabeela Aijaz, Michael Hurley, Apolonio Luis

Consistency and Localizability

Alon Zakai, Ya'acov Ritov

A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization

Jacob Abernethy, Francis Bach, Theodoros Evgeniou, Jean-philippe Vert

Sparse Online Learning via Truncated Gradient

John Langford, Lihong Li, Tong Zhang

Yihua Chen, Eric K. Garcia, Maya R. Gupta, Ali Rahimi, Luca Cazzanti

Nieme: Large-Scale Energy-Based Models

Francis Maes

On Efficient Large Margin Semisupervised Learning: Method and Theory

Junhui Wang, Xiaotong Shen, Wei Pan

Properties of Monotonic Effects on Directed Acyclic Graphs

Tyler J. VanderWeele, James M. Robins

Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions

Sébastien Bubeck, Ulrike Von Luxburg

Scalable Collaborative Filtering Approaches for Large Recommender Systems

Gábor Takács, István Pilászy, Bottyán Németh, Domonkos Tikk

NEUROSVM: An Architecture to Reduce the Effect of the Choice of Kernel on the Performance of SVM

Pradip Ghanty, Samrat Paul, Nikhil R. Pal

Online Learning with Sample Path Constraints

Shie Mannor, John N. Tsitsiklis, Jia Yuan Yu

On the Consistency of Feature Selection using Greedy Least Squares Regression

Tong Zhang

Identification of Recurrent Neural Networks by Bayesian Interrogation Techniques

Barnabás Póczos, András Loőrincz

Controlling the False Discovery Rate of the Association/Causality Structure Learned with the PC Algorithm

Junning Li, Z. Jane Wang

Generalization Bounds for Ranking Algorithms via Algorithmic Stability

Shivani Agarwal, Partha Niyogi

Particle Swarm Model Selection

Hugo Jair Escalante, Manuel Montes, Luis Enrique Sucar

Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining

Petra Kralj Novak, Nada Lavra&#269

Low-Rank Kernel Learning with Bregman Matrix Divergences

Brian Kulis, Mátyás A. Sustik, Inderjit S. Dhillon

Improving the Reliability of Causal Discovery from Small Data Sets Using Argumentation

Facundo Bromberg, Dimitris Margaritis

Analysis of Perceptron-Based Active Learning

Sanjoy Dasgupta, Adam Tauman Kalai, Claire Monteleoni

Data-driven Calibration of Penalties for Least-Squares Regression

Sylvain Arlot, Pascal Massart

Distance Metric Learning for Large Margin Nearest Neighbor Classification

Kilian Q. Weinberger, Lawrence K. Saul

Using Local Dependencies within Batches to Improve Large Margin Classifiers

Volkan Vural, Glenn Fung, Balaji Krishnapuram, Jennifer G. Dy, Bharat Rao

On The Power of Membership Queries in Agnostic Learning

Vitaly Feldman

Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data

Abhik Shah, Peter Woolf

Subgroup Analysis via Recursive Partitioning

Xiaogang Su, Chih-Ling Tsai, Hansheng Wang, David M. Nickerson, Bogong Li

Refinement of Reproducing Kernels

Yuesheng Xu, Haizhang Zhang

An Analysis of Convex Relaxations for MAP Estimation of Discrete MRFs

M. Pawan Kumar, Vladimir Kolmogorov, Philip H.S. Torr

Markov Properties for Linear Causal Models with Correlated Errors

Changsung Kang, Jin Tian

Exploring Strategies for Training Deep Neural Networks

Hugo Larochelle, Yoshua Bengio, Jérôme Louradour, Pascal Lamblin