COLT '90 Proceedings of the third annual workshop on Computational learning theory
The weighted majority algorithm
Information and Computation
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
Predicting Nearly As Well As the Best Pruning of a Decision Tree
Machine Learning - Special issue on the eighth annual conference on computational learning theory, (COLT '95)
Journal of the ACM (JACM)
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Machine Learning - Special issue on context sensitivity and concept drift
The Nonstochastic Multiarmed Bandit Problem
SIAM Journal on Computing
PAC-Bayesian Stochastic Model Selection
Machine Learning
Tracking the best linear predictor
The Journal of Machine Learning Research
Tracking a small set of experts by mixing past posteriors
The Journal of Machine Learning Research
Path kernels and multiplicative updates
The Journal of Machine Learning Research
A polynomial-time approximation algorithm for the permanent of a matrix with nonnegative entries
Journal of the ACM (JACM)
Linear Programming and Network Flows
Linear Programming and Network Flows
Efficient algorithms for online decision problems
Journal of Computer and System Sciences - Special issue: Learning theory 2003
Prediction, Learning, and Games
Prediction, Learning, and Games
Online kernel PCA with entropic matrix updates
Proceedings of the 24th international conference on Machine learning
Complexity of combinatorial market makers
Proceedings of the 9th ACM conference on Electronic commerce
Fourier Theoretic Probabilistic Inference over Permutations
The Journal of Machine Learning Research
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Optimum follow the leader algorithm
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Relative loss bounds for single neurons
IEEE Transactions on Neural Networks
A new understanding of prediction markets via no-regret learning
Proceedings of the 11th ACM conference on Electronic commerce
Learning probability distributions over permutations by means of fourier coefficients
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
Online linear optimization over permutations
ISAAC'11 Proceedings of the 22nd international conference on Algorithms and Computation
Online prediction under submodular constraints
ALT'12 Proceedings of the 23rd international conference on Algorithmic Learning Theory
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Efficient Market Making via Convex Optimization, and a Connection to Online Learning
ACM Transactions on Economics and Computation - Special Issue on Algorithmic Game Theory
Combining initial segments of lists
Theoretical Computer Science
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We give an algorithm for the on-line learning of permutations. The algorithm maintains its uncertainty about the target permutation as a doubly stochastic weight matrix, and makes predictions using an efficient method for decomposing the weight matrix into a convex combination of permutations. The weight matrix is updated by multiplying the current matrix entries by exponential factors, and an iterative procedure is needed to restore double stochasticity. Even though the result of this procedure does not have a closed form, a new analysis approach allows us to prove an optimal (up to small constant factors) bound on the regret of our algorithm. This regret bound is significantly better than that of either Kalai and Vempala's more efficient Follow the Perturbed Leader algorithm or the computationally expensive method of explicitly representing each permutation as an expert.