JMLR Volume 15

Bridging Viterbi and Posterior Decoding: A Generalized Risk Approach to Hidden Path Inference Based on Hidden Markov Models
Jüri Lember, Alexey A. Koloydenko; 15(Jan):1−58, 2014.
[abs][pdf][bib]

Fast SVM Training Using Approximate Extreme Points
Manu Nandan, Pramod P. Khargonekar, Sachin S. Talathi; 15(Jan):59−98, 2014.
[abs][pdf][bib]

Detecting Click Fraud in Online Advertising: A Data Mining Approach
Richard Oentaryo, Ee-Peng Lim, Michael Finegold, David Lo, Feida Zhu, Clifton Phua, Eng-Yeow Cheu, Ghim-Eng Yap, Kelvin Sim, Minh Nhut Nguyen, Kasun Perera, Bijay Neupane, Mustafa Faisal, Zeyar Aung, Wei Lee Woon, Wei Chen, Dhaval Patel, Daniel Berrar; 15(Jan):99−140, 2014.
[abs][pdf][bib]

EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines
Marc Claesen, Frank De Smet, Johan A.K. Suykens, Bart De Moor; 15(Jan):141−145, 2014.
[abs][pdf][bib]    [code][mloss.org]

A Junction Tree Framework for Undirected Graphical Model Selection
Divyanshu Vats, Robert D. Nowak; 15(Jan):147−191, 2014.
[abs][pdf][bib]

Axioms for Graph Clustering Quality Functions
Twan van Laarhoven, Elena Marchiori; 15(Jan):193−215, 2014.
[abs][pdf][bib]

Convex vs Non-Convex Estimators for Regression and Sparse Estimation: the Mean Squared Error Properties of ARD and GLasso
Aleksandr Aravkin, James V. Burke, Alessandro Chiuso, Gianluigi Pillonetto; 15(Jan):217−252, 2014.
[abs][pdf][bib]

Using Trajectory Data to Improve Bayesian Optimization for Reinforcement Learning
Aaron Wilson, Alan Fern, Prasad Tadepalli; 15(Jan):253−282, 2014.
[abs][pdf][bib]

Information Theoretical Estimators Toolbox
Zoltán Szabó; 15(Jan):283−287, 2014.
[abs][pdf][bib]    [code][mloss.org]

Off-policy Learning With Eligibility Traces: A Survey
Matthieu Geist, Bruno Scherrer; 15(Jan):289−333, 2014.
[abs][pdf][bib]

Early Stopping and Non-parametric Regression: An Optimal Data-dependent Stopping Rule
Garvesh Raskutti, Martin J. Wainwright, Bin Yu; 15(Jan):335−366, 2014.
[abs][pdf][bib]

Unbiased Generative Semi-Supervised Learning
Patrick Fox-Roberts, Edward Rosten; 15(Feb):367−443, 2014.
[abs][pdf][bib]

Node-Based Learning of Multiple Gaussian Graphical Models
Karthik Mohan, Palma London, Maryam Fazel, Daniela Witten, Su-In Lee; 15(Feb):445−488, 2014.
[abs][pdf][bib]

The FASTCLIME Package for Linear Programming and Large-Scale Precision Matrix Estimation in R
Haotian Pang, Han Liu, Robert Vanderbei; 15(Feb):489−493, 2014.
[abs][pdf][bib]    [code][mloss.org]

LIBOL: A Library for Online Learning Algorithms
Steven C.H. Hoi, Jialei Wang, Peilin Zhao; 15(Feb):495−499, 2014.
[abs][pdf][bib]    [code][mloss.org]

Improving Markov Network Structure Learning Using Decision Trees
Daniel Lowd, Jesse Davis; 15(Feb):501−532, 2014.
[abs][pdf][bib]

Ground Metric Learning
Marco Cuturi, David Avis; 15(Feb):533−564, 2014.
[abs][pdf][bib]

Link Prediction in Graphs with Autoregressive Features
Emile Richard, Stéphane Gaïffas, Nicolas Vayatis; 15(Feb):565−593, 2014.
[abs][pdf][bib]

Adaptivity of Averaged Stochastic Gradient Descent to Local Strong Convexity for Logistic Regression
Francis Bach; 15(Feb):595−627, 2014.
[abs][pdf][bib]

Random Intersection Trees
Rajen Dinesh Shah, Nicolai Meinshausen; 15(Feb):629−654, 2014.
[abs][pdf][bib]

Reinforcement Learning for Closed-Loop Propofol Anesthesia: A Study in Human Volunteers
Brett L Moore, Larry D Pyeatt, Vivekanand Kulkarni, Periklis Panousis, Kevin Padrez, Anthony G Doufas; 15(Feb):655−696, 2014.
[abs][pdf][bib]

Clustering Hidden Markov Models with Variational HEM
Emanuele Coviello, Antoni B. Chan, Gert R.G. Lanckriet; 15(Feb):697−747, 2014.
[abs][pdf][bib]

A Novel M-Estimator for Robust PCA
Teng Zhang, Gilad Lerman; 15(Feb):749−808, 2014.
[abs][pdf][bib]

Policy Evaluation with Temporal Differences: A Survey and Comparison
Christoph Dann, Gerhard Neumann, Jan Peters; 15(Mar):809−883, 2014.
[abs][pdf][bib]

Active Learning Using Smooth Relative Regret Approximations with Applications
Nir Ailon, Ron Begleiter, Esther Ezra; 15(Mar):885−920, 2014.
[abs][pdf][bib]

An Extension of Slow Feature Analysis for Nonlinear Blind Source Separation
Henning Sprekeler, Tiziano Zito, Laurenz Wiskott; 15(Mar):921−947, 2014.
[abs][pdf][bib]

Natural Evolution Strategies
Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters, Jürgen Schmidhuber; 15(Mar):949−980, 2014.
[abs][pdf][bib]

Conditional Random Field with High-order Dependencies for Sequence Labeling and Segmentation
Nguyen Viet Cuong, Nan Ye, Wee Sun Lee, Hai Leong Chieu; 15(Mar):981−1009, 2014.
[abs][pdf][bib]    [code][github.com]

Ellipsoidal Rounding for Nonnegative Matrix Factorization Under Noisy Separability
Tomohiko Mizutani; 15(Mar):1011−1039, 2014.
[abs][pdf][bib]

Improving Prediction from Dirichlet Process Mixtures via Enrichment
Sara Wade, David B. Dunson, Sonia Petrone, Lorenzo Trippa; 15(Mar):1041−1071, 2014.
[abs][pdf][bib]

Gibbs Max-margin Topic Models with Data Augmentation
Jun Zhu, Ning Chen, Hugh Perkins, Bo Zhang; 15(Mar):1073−1110, 2014.
[abs][pdf][bib]

A Reliable Effective Terascale Linear Learning System
Alekh Agarwal, Oliveier Chapelle, Miroslav Dudík, John Langford; 15(Mar):1111−1133, 2014.
[abs][pdf][bib]

New Learning Methods for Supervised and Unsupervised Preference Aggregation
Maksims N. Volkovs, Richard S. Zemel; 15(Mar):1135−1176, 2014.
[abs][pdf][bib]

Prediction and Clustering in Signed Networks: A Local to Global Perspective
Kai-Yang Chiang, Cho-Jui Hsieh, Nagarajan Natarajan, Inderjit S. Dhillon, Ambuj Tewari; 15(Mar):1177−1213, 2014.
[abs][pdf][bib]

Bayesian Nonparametric Comorbidity Analysis of Psychiatric Disorders
Francisco J. R. Ruiz, Isabel Valera, Carlos Blanco, Fernando Perez-Cruz; 15(Apr):1215−1247, 2014.
[abs][pdf][bib]

Robust Near-Separable Nonnegative Matrix Factorization Using Linear Optimization
Nicolas Gillis, Robert Luce; 15(Apr):1249−1280, 2014.
[abs][pdf][bib]

Follow the Leader If You Can, Hedge If You Must
Steven de Rooij, Tim van Erven, Peter D. Grünwald, Wouter M. Koolen; 15(Apr):1281−1316, 2014.
[abs][pdf][bib]

Structured Prediction via Output Space Search
Janardhan Rao Doppa, Alan Fern, Prasad Tadepalli; 15(Apr):1317−1350, 2014.
[abs][pdf][bib]

Fully Simplified Multivariate Normal Updates in Non-Conjugate Variational Message Passing
Matt P. Wand; 15(Apr):1351−1369, 2014.
[abs][pdf][bib]

Towards Ultrahigh Dimensional Feature Selection for Big Data
Mingkui Tan, Ivor W. Tsang, Li Wang; 15(Apr):1371−1429, 2014.
[abs][pdf][bib]

Adaptive Sampling for Large Scale Boosting
Charles Dubout, Francois Fleuret; 15(Apr):1431−1453, 2014.
[abs][pdf][bib]

Manopt, a Matlab Toolbox for Optimization on Manifolds
Nicolas Boumal, Bamdev Mishra, P.-A. Absil, Rodolphe Sepulchre; 15(Apr):1455−1459, 2014.
[abs][pdf][bib]    [code][manopt.org]

Training Highly Multiclass Classifiers
Maya R. Gupta, Samy Bengio, Jason Weston; 15(Apr):1461−1492, 2014.
[abs][pdf][bib]

Locally Adaptive Factor Processes for Multivariate Time Series
Daniele Durante, Bruno Scarpa, David B. Dunson; 15(Apr):1493−1522, 2014.
[abs][pdf][bib]

Iteration Complexity of Feasible Descent Methods for Convex Optimization
Po-Wei Wang, Chih-Jen Lin; 15(Apr):1523−1548, 2014.
[abs][pdf][bib]

High-Dimensional Covariance Decomposition into Sparse Markov and Independence Models
Majid Janzamin, Animashree Anandkumar; 15(Apr):1549−1591, 2014.
[abs][pdf][bib]

The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
Matthew D. Hoffman, Andrew Gelman; 15(Apr):1593−1623, 2014.
[abs][pdf][bib]

Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife
Stefan Wager, Trevor Hastie, Bradley Efron; 15(May):1625−1651, 2014.
[abs][pdf][bib]

Surrogate Regret Bounds for Bipartite Ranking via Strongly Proper Losses
Shivani Agarwal; 15(May):1653−1674, 2014.
[abs][pdf][bib]

Adaptive Minimax Regression Estimation over Sparse $\ell_q$-Hulls
Zhan Wang, Sandra Paterlini, Fuchang Gao, Yuhong Yang; 15(May):1675−1711, 2014.
[abs][pdf][bib]

Graph Estimation From Multi-Attribute Data
Mladen Kolar, Han Liu, Eric P. Xing; 15(May):1713−1750, 2014.
[abs][pdf][bib]

Hitting and Commute Times in Large Random Neighborhood Graphs
Ulrike von Luxburg, Agnes Radl, Matthias Hein; 15(May):1751−1798, 2014.
[abs][pdf][bib]

Bayesian Inference with Posterior Regularization and Applications to Infinite Latent SVMs
Jun Zhu, Ning Chen, Eric P. Xing; 15(May):1799−1847, 2014.
[abs][pdf][bib]

Expectation Propagation for Neural Networks with Sparsity-Promoting Priors
Pasi Jylänki, Aapo Nummenmaa, Aki Vehtari; 15(May):1849−1901, 2014.
[abs][pdf][bib]

Pattern Alternating Maximization Algorithm for Missing Data in High-Dimensional Problems
Nicolas Städler, Daniel J. Stekhoven, Peter Bühlmann; 15(Jun):1903−1928, 2014.
[abs][pdf][bib]

Dropout: A Simple Way to Prevent Neural Networks from Overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov; 15(Jun):1929−1958, 2014.
[abs][pdf][bib]

Sparse Factor Analysis for Learning and Content Analytics
Andrew S. Lan, Andrew E. Waters, Christoph Studer, Richard G. Baraniuk; 15(Jun):1959−2008, 2014.
[abs][pdf][bib]

Causal Discovery with Continuous Additive Noise Models
Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf; 15(Jun):2009−2053, 2014.
[abs][pdf][bib]

pystruct - Learning Structured Prediction in Python
Andreas C. Müller, Sven Behnke; 15(Jun):2055−2060, 2014.
[abs][pdf][bib]    [code][github.io]

The Student-t Mixture as a Natural Image Patch Prior with Application to Image Compression
Aäron van den Oord, Benjamin Schrauwen; 15(Jun):2061−2086, 2014.
[abs][pdf][bib]

Parallel MCMC with Generalized Elliptical Slice Sampling
Robert Nishihara, Iain Murray, Ryan P. Adams; 15(Jun):2087−2112, 2014.
[abs][pdf][bib]

Classifier Cascades and Trees for Minimizing Feature Evaluation Cost
Zhixiang (Eddie) Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen, Olivier Chapelle; 15(Jun):2113−2144, 2014.
[abs][pdf][bib]

Particle Gibbs with Ancestor Sampling
Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön; 15(Jun):2145−2184, 2014.
[abs][pdf][bib]

Ramp Loss Linear Programming Support Vector Machine
Xiaolin Huang, Lei Shi, Johan A.K. Suykens; 15(Jun):2185−2211, 2014.
[abs][pdf][bib]

Clustering Partially Observed Graphs via Convex Optimization
Yudong Chen, Ali Jalali, Sujay Sanghavi, Huan Xu; 15(Jun):2213−2238, 2014.
[abs][pdf][bib]

A Tensor Approach to Learning Mixed Membership Community Models
Animashree Anandkumar, Rong Ge, Daniel Hsu, Sham M. Kakade; 15(Jun):2239−2312, 2014.
[abs][pdf][bib]

Cover Tree Bayesian Reinforcement Learning
Nikolaos Tziortziotis, Christos Dimitrakakis, Konstantinos Blekas; 15(Jun):2313−2335, 2014.
[abs][pdf][bib]

Efficient State-Space Inference of Periodic Latent Force Models
Steven Reece, Siddhartha Ghosh, Alex Rogers, Stephen Roberts, Nicholas R. Jennings; 15(Jul):2337−2397, 2014.
[abs][pdf][bib]    [code]

Spectral Learning of Latent-Variable PCFGs: Algorithms and Sample Complexity
Shay B. Cohen, Karl Stratos, Michael Collins, Dean P. Foster, Lyle Ungar; 15(Jul):2399−2449, 2014.
[abs][pdf][bib]

On Multilabel Classification and Ranking with Bandit Feedback
Claudio Gentile, Francesco Orabona; 15(Jul):2451−2487, 2014.
[abs][pdf][bib]

Beyond the Regret Minimization Barrier: Optimal Algorithms for Stochastic Strongly-Convex Optimization
Elad Hazan, Satyen Kale; 15(Jul):2489−2512, 2014.
[abs][pdf][bib]

One-Shot-Learning Gesture Recognition using HOG-HOF Features
Jakub Konecny, Michal Hagara; 15(Jul):2513−2532, 2014.
[abs][pdf][bib]

Contextual Bandits with Similarity Information
Aleksandrs Slivkins; 15(Jul):2533−2568, 2014.
[abs][pdf][bib]

Boosting Algorithms for Detector Cascade Learning
Mohammad Saberian, Nuno Vasconcelos; 15(Jul):2569−2605, 2014.
[abs][pdf][bib]

Efficient and Accurate Methods for Updating Generalized Linear Models with Multiple Feature Additions
Amit Dhurandhar, Marek Petrik; 15(Jul):2607−2627, 2014.
[abs][pdf][bib]

Bayesian Estimation of Causal Direction in Acyclic Structural Equation Models with Individual-specific Confounder Variables and Non-Gaussian Distributions
Shohei Shimizu, Kenneth Bollen; 15(Aug):2629−2652, 2014.
[abs][pdf][bib]

A Truncated EM Approach for Spike-and-Slab Sparse Coding
Abdul-Saboor Sheikh, Jacquelyn A. Shelton, Jörg Lücke; 15(Aug):2653−2687, 2014.
[abs][pdf][bib]

Efficient Occlusive Components Analysis
Marc Henniges, Richard E. Turner, Maneesh Sahani, Julian Eggert, Jörg Lücke; 15(Aug):2689−2722, 2014.
[abs][pdf][bib]

Optimality of Graphlet Screening in High Dimensional Variable Selection
Jiashun Jin, Cun-Hui Zhang, Qi Zhang; 15(Aug):2723−2772, 2014.
[abs][pdf][bib]

Tensor Decompositions for Learning Latent Variable Models
Animashree Anandkumar, Rong Ge, Daniel Hsu, Sham M. Kakade, Matus Telgarsky; 15(Aug):2773−2832, 2014.
[abs][pdf][bib]

Bayesian Entropy Estimation for Countable Discrete Distributions
Evan Archer, Il Memming Park, Jonathan W. Pillow; 15(Oct):2833−2868, 2014.
[abs][pdf][bib]

Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
Adel Javanmard, Andrea Montanari; 15(Oct):2869−2909, 2014.
[abs][pdf][bib]

QUIC: Quadratic Approximation for Sparse Inverse Covariance Estimation
Cho-Jui Hsieh, Mátyás A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar; 15(Oct):2911−2947, 2014.
[abs][pdf][bib]

Multimodal Learning with Deep Boltzmann Machines
Nitish Srivastava, Ruslan Salakhutdinov; 15(Oct):2949−2980, 2014.
[abs][pdf][bib]

Optimal Data Collection For Informative Rankings Expose Well-Connected Graphs
Braxton Osting, Christoph Brune, Stanley J. Osher; 15(Oct):2981−3012, 2014.
[abs][pdf][bib]

Bayesian Co-Boosting for Multi-modal Gesture Recognition
Jiaxiang Wu, Jian Cheng; 15(Oct):3013−3036, 2014.
[abs][pdf][bib]

Effective String Processing and Matching for Author Disambiguation
Wei-Sheng Chin, Yong Zhuang, Yu-Chin Juan, Felix Wu, Hsiao-Yu Tung, Tong Yu, Jui-Pin Wang, Cheng-Xia Chang, Chun-Pai Yang, Wei-Cheng Chang, Kuan-Hao Huang, Tzu-Ming Kuo, Shan-Wei Lin, Young-San Lin, Yu-Chen Lu, Yu-Chuan Su, Cheng-Kuang Wei, Tu-Chun Yin, Chun-Liang Li, Ting-Wei Lin, Cheng-Hao Tsai, Shou-De Lin, Hsuan-Tien Lin, Chih-Jen Lin; 15(Oct):3037−3064, 2014.
[abs][pdf][bib]

High-Dimensional Learning of Linear Causal Networks via Inverse Covariance Estimation
Po-Ling Loh, Peter Bühlmann; 15(Oct):3065−3105, 2014.
[abs][pdf][bib]

Recursive Teaching Dimension, VC-Dimension and Sample Compression
Thorsten Doliwa, Gaojian Fan, Hans Ulrich Simon, Sandra Zilles; 15(Oct):3107−3131, 2014.
[abs][pdf][bib]

Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?
Manuel Fernández-Delgado, Eva Cernadas, Senén Barro, Dinani Amorim; 15(Oct):3133−3181, 2014.
[abs][pdf][bib]

ooDACE Toolbox: A Flexible Object-Oriented Kriging Implementation
Ivo Couckuyt, Tom Dhaene, Piet Demeester; 15(Oct):3183−3186, 2014.
[abs][pdf][bib]    [code][sumo.intec.ugent.be]

Robust Online Gesture Recognition with Crowdsourced Annotations
Long-Van Nguyen-Dinh, Alberto Calatroni, Gerhard Tröster; 15(Oct):3187−3220, 2014.
[abs][pdf][bib]

Accelerating t-SNE using Tree-Based Algorithms
Laurens van der Maaten; 15(Oct):3221−3245, 2014.
[abs][pdf][bib]

Set-Valued Approachability and Online Learning with Partial Monitoring
Shie Mannor, Vianney Perchet, Gilles Stoltz; 15(Oct):3247−3295, 2014.
[abs][pdf][bib]

Learning Graphical Models With Hubs
Kean Ming Tan, Palma London, Karthik Mohan, Su-In Lee, Maryam Fazel, Daniela Witten; 15(Oct):3297−3331, 2014.
[abs][pdf][bib]

Inconsistency of Pitman-Yor Process Mixtures for the Number of Components
Jeffrey W. Miller, Matthew T. Harrison; 15(Oct):3333−3370, 2014.
[abs][pdf][bib]

Active Contextual Policy Search
Alexander Fabisch, Jan Hendrik Metzen; 15(Oct):3371−3399, 2014.
[abs][pdf][bib]

Matrix Completion with the Trace Norm: Learning, Bounding, and Transducing
Ohad Shamir, Shai Shalev-Shwartz; 15(Oct):3401−3423, 2014.
[abs][pdf][bib]

Statistical Analysis of Metric Graph Reconstruction
Fabrizio Lecci, Alessandro Rinaldo, Larry Wasserman; 15(Oct):3425−3446, 2014.
[abs][pdf][bib]

Alternating Linearization for Structured Regularization Problems
Xiaodong Lin, Minh Pham, Andrzej Ruszczy\'{n}ski; 15(Oct):3447−3481, 2014.
[abs][pdf][bib]

The Gesture Recognition Toolkit
Nicholas Gillian, Joseph A. Paradiso; 15(Oct):3483−3487, 2014.
[abs][pdf][bib]    [code][github.com]

Convolutional Nets and Watershed Cuts for Real-Time Semantic Labeling of RGBD Videos
Camille Couprie, Clément Farabet, Laurent Najman, Yann LeCun; 15(Oct):3489−3511, 2014.
[abs][pdf][bib]

On the Bayes-Optimality of F-Measure Maximizers
Willem Waegeman, Krzysztof Dembczynski, Arkadiusz Jachnik, Weiwei Cheng, Eyke Hüllermeier; 15(Nov):3513−3568, 2014.
[abs][pdf][bib]

SPMF: A Java Open-Source Pattern Mining Library
Philippe Fournier-Viger, Antonio Gomariz, Ted Gueniche, Azadeh Soltani, Cheng-Wei Wu, Vincent S. Tseng; 15(Nov):3569−3573, 2014.
[abs][pdf][bib]    [code][www.philippe-fournier-viger.com]

Efficient Learning and Planning with Compressed Predictive States
William Hamilton, Mahdi Milani Fard, Joelle Pineau; 15(Nov):3575−3619, 2014.
[abs][pdf][bib]

Revisiting Stein's Paradox: Multi-Task Averaging
Sergey Feldman, Maya R. Gupta, Bela A. Frigyik; 15(Nov):3621−3662, 2014.
[abs][pdf][bib]

Multi-Objective Reinforcement Learning using Sets of Pareto Dominating Policies
Kristof Van Moffaert, Ann Nowé; 15(Nov):3663−3692, 2014.
[abs][pdf][bib]

Seeded Graph Matching for Correlated Erdos-Renyi Graphs
Vince Lyzinski, Donniell E. Fishkind, Carey E. Priebe; 15(Nov):3693−3720, 2014.
[abs][pdf][bib]

Asymptotic Accuracy of Distribution-Based Estimation of Latent Variables
Keisuke Yamazaki; 15(Nov):3721−3742, 2014.
[abs][pdf][bib]

What Regularized Auto-Encoders Learn from the Data-Generating Distribution
Guillaume Alain, Yoshua Bengio; 15(Nov):3743−3773, 2014.
[abs][pdf][bib]

Revisiting Bayesian Blind Deconvolution
David Wipf, Haichao Zhang; 15(Nov):3775−3814, 2014.
[abs][pdf][bib]

New Results for Random Walk Learning
Jeffrey C. Jackson, Karl Wimmer; 15(Nov):3815−3846, 2014.
[abs][pdf][bib]

Transfer Learning Decision Forests for Gesture Recognition
Norberto A. Goussies, Sebastián Ubalde, Marta Mejail; 15(Nov):3847−3870, 2014.
[abs][pdf][bib]

Semi-Supervised Eigenvectors for Large-Scale Locally-Biased Learning
Toke J. Hansen, Michael W. Mahoney; 15(Nov):3871−3914, 2014.
[abs][pdf][bib]

BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits
Ruben Martinez-Cantin; 15(Nov):3915−3919, 2014.
[abs][pdf][bib]    [code][bitbucket.org]

Order-Independent Constraint-Based Causal Structure Learning
Diego Colombo, Marloes H. Maathuis; 15(Nov):3921−3962, 2014.
[abs][pdf][bib]

Effective Sampling and Learning for Mallows Models with Pairwise-Preference Data
Tyler Lu, Craig Boutilier; 15(Dec):3963−4009, 2014.
[abs][pdf][bib]

Robust Hierarchical Clustering
Maria-Florina Balcan, Yingyu Liang, Pramod Gupta; 15(Dec):4011−4051, 2014.
[abs][pdf][bib]

Parallelizing Exploration-Exploitation Tradeoffs in Gaussian Process Bandit Optimization
Thomas Desautels, Andreas Krause, Joel W. Burdick; 15(Dec):4053−4103, 2014.
[abs][pdf][bib]     [appendix]

Active Imitation Learning: Formal and Practical Reductions to I.I.D. Learning
Kshitij Judah, Alan P. Fern, Thomas G. Dietterich, Prasad Tadepalli; 15(Dec):4105−4143, 2014.
[abs][pdf][bib]




Home Page

Papers

Submissions

News

Editorial Board

Announcements

Proceedings

Open Source Software

Search

Statistics

Login

Contact Us



RSS Feed