Lectures on statistical inference
Lectures on statistical inference
Asymptotic methods in statistical theory
Asymptotic methods in statistical theory
On the relationship between the information measures and the bayes probability of error
IEEE Transactions on Information Theory
COLT '89 Proceedings of the second annual workshop on Computational learning theory
Duality relationships for entropy-like minimization problems
SIAM Journal on Control and Optimization
American Mathematical Monthly
Machine Learning
Weighted Parzen Windows for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fat-shattering and the learnability of real-valued functions
Journal of Computer and System Sciences
Learning in graphical models
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Statistical and neural classifiers: an integrated approach to design
Statistical and neural classifiers: an integrated approach to design
Sorting things out: classification and its consequences
Sorting things out: classification and its consequences
AI Game Programming Wisdom
Data mining standards initiatives
Communications of the ACM - Evolving data mining into solutions for insights
Structure and Interpretation of Computer Programs
Structure and Interpretation of Computer Programs
Parallel Optimization: Theory, Algorithms and Applications
Parallel Optimization: Theory, Algorithms and Applications
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Learning from Examples with Information Theoretic Criteria
Journal of VLSI Signal Processing Systems
Restricted Bayes Optimal Classifiers
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
When Can Two Unsupervised Learners Achieve PAC Separation?
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Divergence function, duality, and convex analysis
Neural Computation
Algorithmic Learning in a Random World
Algorithmic Learning in a Random World
Error limiting reductions between classification tasks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Machine Learning
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Neural Computation
Principled Hybrids of Generative and Discriminative Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
ROC graphs with instance-varying costs
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Estimation of Dependences Based on Empirical Data: Empirical Inference Science (Information Science and Statistics)
Clustering with Bregman Divergences
The Journal of Machine Learning Research
Some Equivalences between Kernel Methods and Information Theoretic Methods
Journal of VLSI Signal Processing Systems
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
Considering Cost Asymmetry in Learning Classifiers
The Journal of Machine Learning Research
Self-financed wagering mechanisms for forecasting
Proceedings of the 9th ACM conference on Electronic commerce
Information and Complexity in Statistical Modeling
Information and Complexity in Statistical Modeling
Learning binary relations and total orders
SFCS '89 Proceedings of the 30th Annual Symposium on Foundations of Computer Science
Parametric estimation and tests through divergences and the duality technique
Journal of Multivariate Analysis
The Proximal Average: Basic Theory
SIAM Journal on Optimization
Support Vector Machines
Confliction of the Convexity and Metric Properties in f-Divergences
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Surrogate regret bounds for proper losses
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Repairing concavities in ROC curves
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A symmetric information divergence measure of the Csiszár's f-divergence class and its bounds
Computers & Mathematics with Applications
A theory of learning from different domains
Machine Learning
Kernel methods and the exponential family
Neurocomputing
The Journal of Machine Learning Research
Unifying divergence minimization and statistical inference via convex duality
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Discriminative learning can succeed where generative learning fails
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Subset ranking using regression
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Kernel principal components are maximum entropy projections
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Sensitive error correcting output codes
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Discriminative vs. generative classifiers for cost sensitive learning
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
A survey of convergence results on particle filtering methods forpractitioners
IEEE Transactions on Signal Processing
Regression Level Set Estimation Via Cost-Sensitive Classification
IEEE Transactions on Signal Processing
Some inequalities relating different measures of divergence between two probability distributions
IEEE Transactions on Information Theory
Some inequalities for information divergence and related measures of discrimination
IEEE Transactions on Information Theory
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Unified design of iterative receivers using factor graphs
IEEE Transactions on Information Theory
Refinements of Pinsker's inequality
IEEE Transactions on Information Theory
On the optimality of conditional expectation as a Bregman predictor
IEEE Transactions on Information Theory
On Divergences and Informations in Statistics and Information Theory
IEEE Transactions on Information Theory
The Journal of Machine Learning Research
Mixability is bayes risk curvature relative to log loss
The Journal of Machine Learning Research
Risk bounds of learning processes for Lévy processes
The Journal of Machine Learning Research
The Journal of Machine Learning Research
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We unify f-divergences, Bregman divergences, surrogate regret bounds, proper scoring rules, cost curves, ROC-curves and statistical information. We do this by systematically studying integral and variational representations of these objects and in so doing identify their representation primitives which all are related to cost-sensitive binary classification. As well as developing relationships between generative and discriminative views of learning, the new machinery leads to tight and more general surrogate regret bounds and generalised Pinsker inequalities relating f-divergences to variational divergence. The new viewpoint also illuminates existing algorithms: it provides a new derivation of Support Vector Machines in terms of divergences and relates maximum mean discrepancy to Fisher linear discriminants.