JMLR 2012

119 papers

Exploration in Relational Domains for Model-based Reinforcement Learning

Tobias Lang, Marc Toussaint, Kristian Kersting

Security Analysis of Online Centroid Anomaly Detection

Marius Kloft, Pavel Laskov

Smoothing Multivariate Performance Measures

Xinhua Zhang, Ankan Saha, S.v.n. Vishwanathan

SVDFeature: A Toolkit for Feature-based Collaborative Filtering

Tianqi Chen, Weinan Zhang, Qiuxia Lu, Kailong Chen, Zhao Zheng

Learning Symbolic Representations of Hybrid Dynamical Systems

Daniel L. Ly, Hod Lipson

Regularized Bundle Methods for Convex and Non-Convex Risks

Trinh Minh Tri Do, Thierry Artières

DARWIN: A Framework for Machine Learning and Computer Vision Research and Development

Stephen Gould

PAC-Bayes Bounds with Data Dependent Priors

Emilio Parrado-hernández, Amiran Ambroladze, John Shawe-Taylor, Shiliang Sun

Fast Approximation of Matrix Coherence and Statistical Leverage

Petros Drineas, Malik Magdon-Ismail, Michael W. Mahoney, David P. Woodruff

Iterative Reweighted Algorithms for Matrix Rank Minimization

Karthik Mohan, Maryam Fazel

Learning Linear Cyclic Causal Models with Latent Variables

Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer

Sparse and Unique Nonnegative Matrix Factorization Through Data Preprocessing

Nicolas Gillis

Large-scale Linear Support Vector Regression

Chia-Hua Ho, Chih-Jen Lin

Human Gesture Recognition on Product Manifolds

Yui Man Lui

Linear Fitted-Q Iteration with Multiple Reward Functions

Daniel J. Lizotte, Michael Bowling, Susan A. Murphy

Sally: A Tool for Embedding Strings in Vector Spaces

Konrad Rieck, Christian Wressnegger, Alexander Bikadorov

Dynamic Policy Programming

Mohammad Gheshlaghi azar, Vicenç Gómez, Hilbert J. Kappen

Quantum Set Intersection and its Application to Associative Memory

Tamer Salman, Yoram Baram

Bayesian Mixed-Effects Inference on Classification Performance in Hierarchical Data Sets

Kay H. Brodersen, Christoph Mathys, Justin R. Chumbley, Jean Daunizeau, Cheng Soon Ong

Breaking the Curse of Kernelization: Budgeted Stochastic Gradient Descent for Large-Scale SVM Training

Zhuang Wang, Koby Crammer, Slobodan Vucetic

Discriminative Hierarchical Part-based Models for Human Parsing and Action Recognition

Yang Wang, Duan Tran, Zicheng Liao, David Forsyth

Finite-Sample Analysis of Least-Squares Policy Iteration

Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos

Multi-Instance Learning with Any Hypothesis Class

Sivan Sabato, Naftali Tishby

Oger: Modular Learning Architectures For Large-Scale Sequential Processing

David Verstraeten, Benjamin Schrauwen, Sander Dieleman, Philemon Brakel, Pieter Buteneers

Facilitating Score and Causal Inference Trees for Large Observational Studies

Xiaogang Su, Joseph Kang, Juanjuan Fan, Richard A. Levine, Xin Yan

Efficient Methods for Robust Classification Under Uncertainty in Kernel Matrices

Aharon Ben-tal, Sahely Bhadra, Chiranjib Bhattacharyya, Arkadi Nemirovski

Online Submodular Minimization

Elad Hazan, Satyen Kale

Local and Global Scaling Reduce Hubs in Space

Dominik Schnitzer, Arthur Flexer, Markus Schedl, Gerhard Widmer

A Unified View of Performance Metrics: Translating Threshold Choice into Expected Classification Loss

José Hernández-Orallo, Peter Flach, Cesar Ferri

Multi-task Regression using Minimal Penalties

Matthieu Solnon, Sylvain Arlot, Francis Bach

Linear Regression With Random Projections

Odalric-ambrym Maillard, Rémi Munos

Coherence Functions with Applications in Large-Margin Classification Methods

Zhihua Zhang, Dehua Liu, Guang Dai, Michael I. Jordan

PREA: Personalized Recommendation Algorithms Toolkit

Joonseok Lee, Mingxuan Sun, Guy Lebanon

Selective Sampling and Active Learning from Single and Multiple Teachers

Ofer Dekel, Claudio Gentile, Karthik Sridharan

Static Prediction Games for Adversarial Learning Problems

Michael Brückner, Christian Kanzow, Tobias Scheffer

Finding Recurrent Patterns from Continuous Sign Language Sentences for Automated Extraction of Signs

Sunita Nayak, Kester Duncan, Sudeep Sarkar, Barbara Loeding

Nonparametric Guidance of Autoencoder Representations using Label Information

Jasper Snoek, Ryan P. Adams, Hugo Larochelle

Robust Kernel Density Estimation

Jooseuk Kim, Clayton D. Scott

Trading Regret for Efficiency: Online Convex Optimization with Long Term Constraints

Mehrdad Mahdavi, Rong Jin, Tianbao Yang

On the Convergence Rate of -Norm Multiple Kernel Learning

Marius Kloft, Gilles Blanchard

Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs

Alain Hauser, Peter Bühlmann

Multi-Target Regression with Rule Ensembles

Timo Aho, Bernard Ženko, Sašo Džeroski, Tapio Elomaa

A Local Spectral Method for Graphs: With Applications to Improving Graph Partitions and Exploring Data Graphs Locally

Michael W. Mahoney, Lorenzo Orecchia, Nisheeth K. Vishnoi

High-Dimensional Gaussian Graphical Model Selection: Walk Summability and Local Separation Criterion

Animashree Anandkumar, Vincent Y.F. Tan, Furong Huang, Alan S. Willsky

Pairwise Support Vector Machines and their Application to Large Scale Problems

Carl Brunner, Andreas Fischer, Klaus Luig, Thorsten Thies

MedLDA: Maximum Margin Supervised Topic Models

Jun Zhu, Amr Ahmed, Eric P. Xing

A Topic Modeling Toolbox Using Belief Propagation

Jia Zeng

Sign Language Recognition using Sub-Units

Helen Cooper, Eng-Jon Ong, Nicolas Pugeault, Richard Bowden

An Introduction to Artificial Prediction Markets for Classification

Adrian Barbu, Nathan Lay

DEAP: Evolutionary Algorithms Made Easy

Félix-Antoine Fortin, François-Michel De Rainville, Marc-Andre Gardner, Marc Parizeau, Christian Gagné

On the Necessity of Irrelevant Variables

David P. Helmbold, Philip M. Long

A Comparison of the Lasso and Marginal Regression

Christopher R. Genovese, Jiashun Jin, Larry Wasserman, Zhigang Yao

Optimistic Bayesian Sampling in Contextual-Bandit Problems

Benedict C. May, Nathan Korda, Anthony Lee, David S. Leslie

Pattern for Python

Tom De Smedt, Walter Daelemans

EP-GIG Priors and Applications in Bayesian Sparse Learning

Zhihua Zhang, Shusen Wang, Dehua Liu, Michael I. Jordan

An Improved GLMNET for L1-regularized Logistic Regression

Guo-Xun Yuan, Chia-Hua Ho, Chih-Jen Lin

Variable Selection in High-dimensional Varying-coefficient Models with Global Optimality

Lan Xue, Annie Qu

Jstacs: A Java Framework for Statistical Analysis and Classification of Biological Sequences

Jan Grau, Jens Keilwagen, André Gohr, Berit Haldemann, Stefan Posch

Integrating a Partial Model into Model Free Reinforcement Learning

Aviv Tamar, Dotan Di Castro, Ron Meir

Confidence-Weighted Linear Classification for Text Categorization

Koby Crammer, Mark Dredze, Fernando Pereira

Regularization Techniques for Learning with Matrices

Sham M. Kakade, Shai Shalev-shwartz, Ambuj Tewari

Estimation and Selection via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications

Jian Huang, Cun-hui Zhang

Entropy Search for Information-Efficient Global Optimization

Philipp Hennig, Christian J. Schuler

Variational Multinomial Logit Gaussian Process

Kian Ming A. Chai

Manifold Identification in Dual Averaging for Regularized Stochastic Online Learning

Sangkyun Lee, Stephen J. Wright

glm-ie: Generalised Linear Models Inference & Estimation Toolbox

Hannes Nickisch

Restricted Strong Convexity and Weighted Matrix Completion: Optimal Bounds with Noise

Sahand Negahban, Martin J. Wainwright

Mixability is Bayes Risk Curvature Relative to Log Loss

Tim van Erven, Mark D. Reid, Robert C. Williamson

A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models

Neil D. Lawrence

A Model of the Perception of Facial Expressions of Emotion by Humans: Research Overview and Perspectives

Aleix Martinez, Shichuan Du

Activized Learning: Transforming Passive to Active with Improved Label Complexity

Steve Hanneke

Structured Sparsity via Alternating Direction Methods

Zhiwei Qin, Donald Goldfarb

Feature Selection via Dependence Maximization

Le Song, Alex Smola, Arthur Gretton, Justin Bedo, Karsten Borgwardt

On Ranking and Generalization Bounds

Wojciech Rejchel

Transfer in Reinforcement Learning via Shared Features

George Konidaris, Ilya Scheidwasser, Andrew Barto

Query Strategies for Evading Convex-Inducing Classifiers

Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang, Anthony D. Joseph, Steven J. Lee

Minimax Manifold Estimation

Christopher Genovese, Marco Perone-Pacifico, Isabella Verdinelli, Larry Wasserman

A Geometric Approach to Sample Compression

Benjamin I.P. Rubinstein, J. Hyam Rubinstein

A Multi-Stage Framework for Dantzig Selector and LASSO

Ji Liu, Peter Wonka, Jieping Ye

Hope and Fear for Discriminative Training of Statistical Translation Models

David Chiang

Towards Integrative Causal Analysis of Heterogeneous Data Sets and Studies

Ioannis Tsamardinos, Sofia Triantafillou, Vincenzo Lagani

Analysis of a Random Forests Model

Gérard Biau

The huge Package for High-dimensional Undirected Graph Estimation in R

Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, Larry Wasserman

Consistent Model Selection Criteria on High Dimensions

Yongdai Kim, Sunghoon Kwon, Hosik Choi

Positive Semidefinite Metric Learning Using Boosting-like Algorithms

Chunhua Shen, Junae Kim, Lei Wang, Anton van den Hengel

Sampling Methods for the Nyström Method

Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar

Mal-ID: Automatic Malware Detection Using Common Segment Analysis and Meta-Features

Gil Tahan, Lior Rokach, Yuval Shahar

Stability of Density-Based Clustering

Alessandro Rinaldo, Aarti Singh, Rebecca Nugent, Larry Wasserman

Algebraic Geometric Comparison of Probability Distributions

Franz J. Király, Paul von Bünau, Frank C. Meinecke, Duncan A.J. Blythe, Klaus-Robert Müller

NIMFA : A Python Library for Nonnegative Matrix Factorization

Marinka Zitnik, Blaž Zupan

Causal Bounds and Observable Constraints for Non-deterministic Models

Roland R. Ramsahai

Algorithms for Learning Kernels Based on Centered Alignment

Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh

Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso

Rahul Mazumder, Trevor Hastie

GPLP: A Local and Parallel Computation Toolbox for Gaussian Process Regression

Chiwoo Park, Jianhua Z. Huang, Yu Ding

A Kernel Two-Sample Test

Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander Smola

A Case Study on Meta-Generalising: A Gaussian Processes Approach

Grigorios Skolidis, Guido Sanguinetti

Structured Sparsity and Generalization

Andreas Maurer, Massimiliano Pontil

Learning Algorithms for the Classification Restricted Boltzmann Machine

Hugo Larochelle, Michael Mandel, Razvan Pascanu, Yoshua Bengio

Non-Sparse Multiple Kernel Fisher Discriminant Analysis

Fei Yan, Josef Kittler, Krystian Mikolajczyk, Atif Tahir

A Primal-Dual Convergence Analysis of Boosting

Matus Telgarsky