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)
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
The binary exponentiated gradient algorithm for learning linear functions
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Artificial Intelligence - Special issue on relevance
Efficient learning with virtual threshold gates
Information and Computation
The robustness of the p-norm algorithms
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Linear hinge loss and average margin
Proceedings of the 1998 conference on Advances in neural information processing systems II
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Competitive routing of virtual circuits with unknown duration
Journal of Computer and System Sciences
Predicting nearly as well as the best pruning of a decision tree through dynamic programming scheme
Theoretical Computer Science
Predicting nearly as well as the best pruning of a planar decision graph
Theoretical Computer Science
Direct and indirect algorithms for on-line learning of disjunctions
Theoretical Computer Science
Dynamic routing on networks with fixed-size buffers
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Developments from a June 1996 seminar on Online algorithms: the state of the art
Rational Kernels: Theory and Algorithms
The Journal of Machine Learning Research
Moment Kernels for Regular Distributions
Machine Learning
Efficient algorithms for online decision problems
Journal of Computer and System Sciences - Special issue: Learning theory 2003
Online kernel PCA with entropic matrix updates
Proceedings of the 24th international conference on Machine learning
Online linear optimization and adaptive routing
Journal of Computer and System Sciences
Efficiency versus convergence of Boolean kernels for on-line learning algorithms
Journal of Artificial Intelligence Research
Learning Permutations with Exponential Weights
The Journal of Machine Learning Research
Learning permutations with exponential weights
COLT'07 Proceedings of the 20th annual conference on Learning theory
Detecting Management Fraud in Public Companies
Management Science
Online algorithms for the newsvendor problem with and without censored demands
FAW'10 Proceedings of the 4th international conference on Frontiers in algorithmics
Algorithms for adversarial bandit problems with multiple plays
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
Combining initial segments of lists
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
Online allocation with risk information
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
On following the perturbed leader in the bandit setting
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
The shortest path problem under partial monitoring
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Tracking the best of many experts
COLT'05 Proceedings of the 18th annual conference on Learning Theory
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
Journal of Computer and System Sciences
Combining initial segments of lists
Theoretical Computer Science
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Kernels are typically applied to linear algorithms whose weight vector is a linear combination of the feature vectors of the examples. On-line versions of these algorithms are sometimes called "additive updates" because they add a multiple of the last feature vector to the current weight vector.In this paper we have found a way to use special convolution kernels to efficiently implement "multiplicative" updates. The kernels are defined by a directed graph. Each edge contributes an input. The inputs along a path form a product feature and all such products build the feature vector associated with the inputs.We also have a set of probabilities on the edges so that the outflow from each vertex is one. We then discuss multiplicative updates on these graphs where the prediction is essentially a kernel computation and the update contributes a factor to each edge. After adding the factors to the edges, the total outflow out of each vertex is not one any more. However some clever algorithms re-normalize the weights on the paths so that the total outflow out of each vertex is one again. Finally, we show that if the digraph is built from a regular expressions, then this can be used for speeding up the kernel and re-normalization computations.We reformulate a large number of multiplicative update algorithms using path kernels and characterize the applicability of our method. The examples include efficient algorithms for learning disjunctions and a recent algorithm that predicts as well as the best pruning of a series parallel digraphs.