JMLR 2014

120 papers

Active Imitation Learning: Formal and Practical Reductions to I.I.D. Learning

Kshitij Judah, Alan P. Fern, Thomas G. Dietterich, Prasad Tadepalli

Parallelizing Exploration-Exploitation Tradeoffs in Gaussian Process Bandit Optimization

Thomas Desautels, Andreas Krause, Joel W. Burdick

Robust Hierarchical Clustering

Maria-Florina Balcan, Yingyu Liang, Pramod Gupta

Effective Sampling and Learning for Mallows Models with Pairwise-Preference Data

Tyler Lu, Craig Boutilier

Order-Independent Constraint-Based Causal Structure Learning

Diego Colombo, Marloes H. Maathuis

BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits

Ruben Martinez-Cantin

Semi-Supervised Eigenvectors for Large-Scale Locally-Biased Learning

Toke J. Hansen, Michael W. Mahoney

Transfer Learning Decision Forests for Gesture Recognition

Norberto A. Goussies, Sebastián Ubalde, Marta Mejail

New Results for Random Walk Learning

Jeffrey C. Jackson, Karl Wimmer

Revisiting Bayesian Blind Deconvolution

David Wipf, Haichao Zhang

What Regularized Auto-Encoders Learn from the Data-Generating Distribution

Guillaume Alain, Yoshua Bengio

Asymptotic Accuracy of Distribution-Based Estimation of Latent Variables

Keisuke Yamazaki

Seeded Graph Matching for Correlated Erdos-Renyi Graphs

Vince Lyzinski, Donniell E. Fishkind, Carey E. Priebe

Multi-Objective Reinforcement Learning using Sets of Pareto Dominating Policies

Kristof Van Moffaert, Ann Nowe

Revisiting Stein's Paradox: Multi-Task Averaging

Sergey Feldman, Maya R. Gupta, Béla A. Frigyik

Efficient Learning and Planning with Compressed Predictive States

William Hamilton, Mahdi Milani Fard, Joelle Pineau

SPMF: A Java Open-Source Pattern Mining Library

Philippe Fournier-Viger, Antonio Gomariz, Ted Gueniche, Azadeh Soltani, Cheng-Wei Wu

On the Bayes-Optimality of F-Measure Maximizers

Willem Waegeman, Krzysztof Dembczynski, Arkadiusz Jachnik, Weiwei Cheng, Eyke Hüllermeier

Convolutional Nets and Watershed Cuts for Real-Time Semantic Labeling of RGBD Videos

Camille Couprie, Clement Farabet, Laurent Najman, Yann LeCun

The Gesture Recognition Toolkit

Nicholas Gillian, Joseph A. Paradiso

Alternating Linearization for Structured Regularization Problems

Xiaodong Lin, Minh Pham, Andrzej Ruszczyński

Statistical Analysis of Metric Graph Reconstruction

Fabrizio Lecci, Alessandro Rinaldo, Larry Wasserman

Matrix Completion with the Trace Norm: Learning, Bounding, and Transducing

Ohad Shamir, Shai Shalev-shwartz

Active Contextual Policy Search

Alexander Fabisch, Jan Hendrik Metzen

Inconsistency of Pitman-Yor Process Mixtures for the Number of Components

Jeffrey W. Miller, Matthew T. Harrison

Learning Graphical Models With Hubs

Kean Ming Tan, Palma London, Karthik Mohan, Su-in Lee, Maryam Fazel

Set-Valued Approachability and Online Learning with Partial Monitoring

Shie Mannor, Vianney Perchet, Gilles Stoltz

Accelerating t-SNE using Tree-Based Algorithms

Laurens van der Maaten

Robust Online Gesture Recognition with Crowdsourced Annotations

Long-Van Nguyen-Dinh, Alberto Calatroni, Gerhard Tröster

ooDACE Toolbox: A Flexible Object-Oriented Kriging Implementation

Ivo Couckuyt, Tom Dhaene, Piet Demeester

Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?

Manuel Fernández-Delgado, Eva Cernadas, Senén Barro, Dinani Amorim

Recursive Teaching Dimension, VC-Dimension and Sample Compression

Thorsten Doliwa, Gaojian Fan, Hans Ulrich Simon, Sandra Zilles

High-Dimensional Learning of Linear Causal Networks via Inverse Covariance Estimation

Po-ling Loh, Peter Bühlmann

Effective String Processing and Matching for Author Disambiguation

Wei-Sheng Chin, Yong Zhuang, Yu-Chin Juan, Felix Wu, Hsiao-Yu Tung

Bayesian Co-Boosting for Multi-modal Gesture Recognition

Jiaxiang Wu, Jian Cheng

Optimal Data Collection For Informative Rankings Expose Well-Connected Graphs

Braxton Osting, Christoph Brune, Stanley J. Osher

Multimodal Learning with Deep Boltzmann Machines

Nitish Srivastava, Ruslan Salakhutdinov

QUIC: Quadratic Approximation for Sparse Inverse Covariance Estimation

Cho-jui Hsieh, Mátyás A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar

Confidence Intervals and Hypothesis Testing for High-Dimensional Regression

Adel Javanmard, Andrea Montanari

Bayesian Entropy Estimation for Countable Discrete Distributions

Evan Archer, Il Memming Park, Jonathan W. Pillow

Tensor Decompositions for Learning Latent Variable Models

Animashree Anandkumar, Rong Ge, Daniel Hsu, Sham M. Kakade, Matus Telgarsky

Optimality of Graphlet Screening in High Dimensional Variable Selection

Jiashun Jin, Cun-hui Zhang, Qi Zhang

Efficient Occlusive Components Analysis

Marc Henniges, Richard E. Turner, Maneesh Sahani, Julian Eggert, Jörg Lücke

A Truncated EM Approach for Spike-and-Slab Sparse Coding

Abdul-saboor Sheikh, Jacquelyn A. Shelton, Jörg Lücke

Bayesian Estimation of Causal Direction in Acyclic Structural Equation Models with Individual-specific Confounder Variables and Non-Gaussian Distributions

Shohei Shimizu, Kenneth Bollen

Efficient and Accurate Methods for Updating Generalized Linear Models with Multiple Feature Additions

Amit Dhurandhar, Marek Petrik

Boosting Algorithms for Detector Cascade Learning

Mohammad Saberian, Nuno Vasconcelos

Contextual Bandits with Similarity Information

Aleksandrs Slivkins

One-Shot-Learning Gesture Recognition using HOG-HOF Features

Jakub Konečný, Michal Hagara

Beyond the Regret Minimization Barrier: Optimal Algorithms for Stochastic Strongly-Convex Optimization

Elad Hazan, Satyen Kale

On Multilabel Classification and Ranking with Bandit Feedback

Claudio Gentile, Francesco Orabona

Spectral Learning of Latent-Variable PCFGs: Algorithms and Sample Complexity

Shay B. Cohen, Karl Stratos, Michael Collins, Dean P. Foster, Lyle Ungar

Efficient State-Space Inference of Periodic Latent Force Models

Steven Reece, Siddhartha Ghosh, Alex Rogers, Stephen Roberts, Nicholas R. Jennings

Cover Tree Bayesian Reinforcement Learning

Nikolaos Tziortziotis, Christos Dimitrakakis, Konstantinos Blekas

A Tensor Approach to Learning Mixed Membership Community Models

Animashree Anandkumar, Rong Ge, Daniel Hsu, Sham M. Kakade

Clustering Partially Observed Graphs via Convex Optimization

Yudong Chen, Ali Jalali, Sujay Sanghavi, Huan Xu

Ramp Loss Linear Programming Support Vector Machine

Xiaolin Huang, Lei Shi, Johan A.K. Suykens

Particle Gibbs with Ancestor Sampling

Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön

Classifier Cascades and Trees for Minimizing Feature Evaluation Cost

Zhixiang (Eddie) Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen, Olivier Chapelle

Parallel MCMC with Generalized Elliptical Slice Sampling

Robert Nishihara, Iain Murray, Ryan P. Adams

The Student-t Mixture as a Natural Image Patch Prior with Application to Image Compression

Aaron van den Oord, Benjamin Schrauwen

pystruct - Learning Structured Prediction in Python

Andreas C. Müller, Sven Behnke

Causal Discovery with Continuous Additive Noise Models

Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf

Sparse Factor Analysis for Learning and Content Analytics

Andrew S. Lan, Andrew E. Waters, Christoph Studer, Richard G. Baraniuk

Dropout: A Simple Way to Prevent Neural Networks from Overfitting

Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov

Pattern Alternating Maximization Algorithm for Missing Data in High-Dimensional Problems

Nicolas Städler, Daniel J. Stekhoven, Peter Bühlmann

Expectation Propagation for Neural Networks with Sparsity-Promoting Priors

Pasi Jylänki, Aapo Nummenmaa, Aki Vehtari

Bayesian Inference with Posterior Regularization and Applications to Infinite Latent SVMs

Jun Zhu, Ning Chen, Eric P. Xing

Hitting and Commute Times in Large Random Neighborhood Graphs

Ulrike Von Luxburg, Agnes Radl, Matthias Hein

Graph Estimation From Multi-Attribute Data

Mladen Kolar, Han Liu, Eric P. Xing

Adaptive Minimax Regression Estimation over Sparse ℓ_q-Hulls

Zhan Wang, Sandra Paterlini, Fuchang Gao, Yuhong Yang

Surrogate Regret Bounds for Bipartite Ranking via Strongly Proper Losses

Shivani Agarwal

Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife

Stefan Wager, Trevor Hastie, Bradley Efron

The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo

Matthew D. Hoffman, Andrew Gelman

High-Dimensional Covariance Decomposition into Sparse Markov and Independence Models

Majid Janzamin, Animashree Anandkumar

Iteration Complexity of Feasible Descent Methods for Convex Optimization

Po-Wei Wang, Chih-Jen Lin

Locally Adaptive Factor Processes for Multivariate Time Series

Daniele Durante, Bruno Scarpa, David B. Dunson

Training Highly Multiclass Classifiers

Maya R. Gupta, Samy Bengio, Jason Weston

Manopt, a Matlab Toolbox for Optimization on Manifolds

Nicolas Boumal, Bamdev Mishra, P.-A. Absil, Rodolphe Sepulchre

Adaptive Sampling for Large Scale Boosting

Charles Dubout, Francois Fleuret

Towards Ultrahigh Dimensional Feature Selection for Big Data

Mingkui Tan, Ivor W. Tsang, Li Wang

Fully Simplified Multivariate Normal Updates in Non-Conjugate Variational Message Passing

Matt P. Wand

Structured Prediction via Output Space Search

Janardhan Rao Doppa, Alan Fern, Prasad Tadepalli

Follow the Leader If You Can, Hedge If You Must

Steven de Rooij, Tim van Erven, Peter D. Grünwald, Wouter M. Koolen

Robust Near-Separable Nonnegative Matrix Factorization Using Linear Optimization

Nicolas Gillis, Robert Luce

Bayesian Nonparametric Comorbidity Analysis of Psychiatric Disorders

Francisco J. R. Ruiz, Isabel Valera, Carlos Blanco, Fernando Pérez-Cruz

Prediction and Clustering in Signed Networks: A Local to Global Perspective

Kai-Yang Chiang, Cho-jui Hsieh, Nagarajan Natarajan, Inderjit S. Dhillon, Ambuj Tewari

New Learning Methods for Supervised and Unsupervised Preference Aggregation

Maksims N. Volkovs, Richard S. Zemel

A Reliable Effective Terascale Linear Learning System

Alekh Agarwal, Oliveier Chapelle, Miroslav Dudík, John Langford

Gibbs Max-margin Topic Models with Data Augmentation

Jun Zhu, Ning Chen, Hugh Perkins, Bo Zhang

Improving Prediction from Dirichlet Process Mixtures via Enrichment

Sara Wade, David B. Dunson, Sonia Petrone, Lorenzo Trippa

Ellipsoidal Rounding for Nonnegative Matrix Factorization Under Noisy Separability

Tomohiko Mizutani

Conditional Random Field with High-order Dependencies for Sequence Labeling and Segmentation

Nguyen Viet Cuong, Nan Ye, Wee Sun Lee, Hai Leong Chieu

Natural Evolution Strategies

Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters

An Extension of Slow Feature Analysis for Nonlinear Blind Source Separation

Henning Sprekeler, Tiziano Zito, Laurenz Wiskott

Active Learning Using Smooth Relative Regret Approximations with Applications

Nir Ailon, Ron Begleiter, Esther Ezra

Policy Evaluation with Temporal Differences: A Survey and Comparison

Christoph Dann, Gerhard Neumann, Jan Peters

A Novel M-Estimator for Robust PCA

Teng Zhang, Gilad Lerman

Clustering Hidden Markov Models with Variational HEM

Emanuele Coviello, Antoni B. Chan, Gert R.G. Lanckriet

Reinforcement Learning for Closed-Loop Propofol Anesthesia: A Study in Human Volunteers

Brett L Moore, Larry D Pyeatt, Vivekanand Kulkarni, Periklis Panousis, Kevin Padrez