Volume 13, 2012
- Yiming Ying, Peng Li:
Distance Metric Learning with Eigenvalue Optimization.
1-26
- Gavin Brown, Adam Pocock, Ming-Jie Zhao, Mikel Luján:
Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection.
27-66
- Stanislav Minsker:
Plug-in Approach to Active Learning.
67-90
- Haizhang Zhang, Yuesheng Xu, Qinghui Zhang:
Refinement of Operator-valued Reproducing Kernels.
91-136
- Nir Ailon:
An Active Learning Algorithm for Ranking from Pairwise Preferences with an Almost Optimal Query Complexity.
137-164
- Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, Lin Xiao:
Optimal Distributed Online Prediction Using Mini-Batches.
165-202
- Konstantin Voevodski, Maria-Florina Balcan, Heiko Röglin, Shang-Hua Teng, Yu Xia:
Active Clustering of Biological Sequences.
203-225
- Francesco Orabona, Jie Luo, Barbara Caputo:
Multi Kernel Learning with Online-Batch Optimization.
227-253
- Ran El-Yaniv, Yair Wiener:
Active Learning via Perfect Selective Classification.
255-279
- James Bergstra, Yoshua Bengio:
Random Search for Hyper-Parameter Optimization.
281-305
- Michael Gutmann, Aapo Hyvärinen:
Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics.
307-361
- Gary B. Huang, Andrew Kae, Carl Doersch, Erik G. Learned-Miller:
Bounding the Probability of Error for High Precision Optical Character Recognition.
363-387
- Garvesh Raskutti, Martin J. Wainwright, Bin Yu:
Minimax-Optimal Rates For Sparse Additive Models Over Kernel Classes Via Convex Programming.
389-427
- Uri Shalit, Daphna Weinshall, Gal Chechik:
Online Learning in the Embedded Manifold of Low-rank Matrices.
429-458
- Mario Frank, Andreas P. Streich, David A. Basin, Joachim M. Buhmann:
Multi-Assignment Clustering for Boolean Data.
459-489
- Vikas C. Raykar, Shipeng Yu:
Eliminating Spammers and Ranking Annotators for Crowdsourced Labeling Tasks.
491-518
- Prateek Jain, Brian Kulis, Jason V. Davis, Inderjit S. Dhillon:
Metric and Kernel Learning Using a Linear Transformation.
519-547
- Djalel Benbouzid, Róbert Busa-Fekete, Norman Casagrande, François-David Collin, Balázs Kégl:
MULTIBOOST: A Multi-purpose Boosting Package.
549-553
- Stephen R. Piccolo, Lewis J. Frey:
ML-Flex: A Flexible Toolbox for Performing Classification Analyses In Parallel.
555-559
- Matus Telgarsky:
A Primal-Dual Convergence Analysis of Boosting.
561-606
- Fei Yan, Josef Kittler, Krystian Mikolajczyk, Muhammad Atif Tahir:
Non-Sparse Multiple Kernel Fisher Discriminant Analysis.
607-642
- Hugo Larochelle, Michael I. Mandel, Razvan Pascanu, Yoshua Bengio:
Learning Algorithms for the Classification Restricted Boltzmann Machine.
643-669
- Andreas Maurer, Massimiliano Pontil:
Structured Sparsity and Generalization.
671-690
- Grigorios Skolidis, Guido Sanguinetti:
A Case Study on Meta-Generalising: A Gaussian Processes Approach.
691-721
- Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Two-Sample Test.
723-773
- Chiwoo Park, Jianhua Z. Huang, Yu Ding:
GPLP: A Local and Parallel Computation Toolbox for Gaussian Process Regression.
775-779
- Rahul Mazumder, Trevor Hastie:
Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso.
781-794
- Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh:
Algorithms for Learning Kernels Based on Centered Alignment.
795-828
- Roland Ramsahai:
Causal Bounds and Observable Constraints for Non-deterministic Models.
829-848
- Marinka Zitnik, Blaz Zupan:
NIMFA: A Python Library for Nonnegative Matrix Factorization.
849-853
- Franz J. Király, Paul von Bünau, Frank C. Meinecke, Duncan A. J. Blythe, Klaus-Robert Müller:
Algebraic Geometric Comparison of Probability Distributions.
855-903
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