Elements of information theory
Elements of information theory
Neural Computation
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
The nature of statistical learning theory
The nature of statistical learning theory
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
On the influence of the kernel on the consistency of support vector machines
The Journal of Machine Learning Research
Kernel independent component analysis
The Journal of Machine Learning Research
Rademacher and gaussian complexities: risk bounds and structural results
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces
The Journal of Machine Learning Research
Exponential families for conditional random fields
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Variational Chernoff bounds for graphical models
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Kernel Methods for Measuring Independence
The Journal of Machine Learning Research
Supervised feature selection via dependence estimation
Proceedings of the 24th international conference on Machine learning
Gene selection via the BAHSIC family of algorithms
Bioinformatics
Measuring statistical dependence with hilbert-schmidt norms
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Maximum entropy distribution estimation with generalized regularization
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Unifying divergence minimization and statistical inference via convex duality
COLT'06 Proceedings of the 19th annual conference on Learning Theory
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Rademacher penalties and structural risk minimization
IEEE Transactions on Information Theory
Unsupervised Classifier Selection Based on Two-Sample Test
DS '08 Proceedings of the 11th International Conference on Discovery Science
A test of independence based on a generalized correlation function
Signal Processing
Hilbert Space Embeddings and Metrics on Probability Measures
The Journal of Machine Learning Research
Characteristic kernels on structured domains excel in robotics and human action recognition
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
A spectral approach for probabilistic grammatical inference on trees
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
A PAC-bayes bound for tailored density estimation
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
Matching samples of multiple views
Data Mining and Knowledge Discovery
Comparing distributions and shapes using the kernel distance
Proceedings of the twenty-seventh annual symposium on Computational geometry
The Journal of Machine Learning Research
On minimum distribution discrepancy support vector machine for domain adaptation
Pattern Recognition
Gaussianity measures for detecting the direction of causal time series
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Strictly positive-definite spike train kernels for point-process divergences
Neural Computation
Kernelized temporal cut for online temporal segmentation and recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Transfer joint embedding for cross-domain named entity recognition
ACM Transactions on Information Systems (TOIS)
Path integral control by reproducing kernel Hilbert space embedding
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a reproducing kernel Hilbert space. Applications of this technique can be found in two-sample tests, which are used for determining whether two sets of observations arise from the same distribution, covariate shift correction, local learning, measures of independence, and density estimation.