Combinatorial Online Prediction via Metarounding
نویسندگان
چکیده
We consider online prediction problems of combinatorial concepts. Examples of such concepts include s-t paths, permutations, truth assignments, set covers, and so on. The goal of the online prediction algorithm is to compete with the best fixed combinatorial concept in hindsight. A generic approach to this problem is to design an online prediction algorithm using the corresponding offline (approximation) algorithm as an oracle. The current state-of-the art method, however, is not efficient enough. In this paper we propose a more efficient online prediction algorithm when the offline approximation algorithm has a guarantee of the integrality gap.
منابع مشابه
Kuhn meets Rosenblatt: Combinatorial Algorithms for Online Structured Prediction
Online algorithms have been successful at a variety of prediction tasks. In structured prediction settings, the model produced by an online learner is fed as input to some combinatorial algorithm for producing structured outputs. This combinatorial algorithm is predominantly considered a black box, which severely limits the control available to the learner. In this paper, we break open this bla...
متن کاملHierarchies of Relaxations for Online Prediction Problems with Evolving Constraints
We study online prediction where regret of the algorithm is measured against a benchmark defined via evolving constraints. This framework captures online prediction on graphs, as well as other prediction problems with combinatorial structure. A key aspect here is that finding the optimal benchmark predictor (even in hindsight, given all the data) might be computationally hard due to the combina...
متن کاملEfficient Online Multiclass Prediction on Graphs via Surrogate Losses
We develop computationally efficient algorithms for online multi-class prediction. Our construction is based on carefully-chosen data-dependent surrogate loss functions, and the new methods enjoy strong mistake bound guarantees. To illustrate the technique, we study the combinatorial problem of node classification and develop a prediction strategy that is linear-time per round. In contrast, the...
متن کاملOnline Prediction under Submodular Constraints
We consider an online prediction problem of combinatorial concepts where each combinatorial concept is represented as a vertex of a polyhedron described by a submodular function (base polyhedron). In general, there are exponentially many vertices in the base polyhedron. We propose polynomial time algorithms with regret bounds. In particular, for cardinality-based submodular functions, we give O...
متن کاملPrediction by random-walk perturbation
We propose a version of the follow-the-perturbed-leader online prediction algorithm in which the cumulative losses are perturbed by independent symmetric random walks. The forecaster is shown to achieve an expected regret of the optimal order O( √ n logN) where n is the time horizon and N is the number of experts. More importantly, it is shown that the forecaster changes its prediction at most ...
متن کامل