{"type":"ScholarlyArticle","title":"Count-Based Frequency Estimation with Bounded Memory","authors":[{"name":"Marc G. Bellemare","slug":"marc-g-bellemare","given":"Marc G.","family":"Bellemare","affiliation":""}],"abstract":"Count-based estimators are a fundamental building block of a number of powerful sequential prediction algorithms, including Context Tree Weighting and Prediction by Partial Matching. Keeping exact counts, however, typically results in a high memory overhead. In particular, when dealing with large alphabets the memory requirements of count-based estimators often become prohibitive. In this paper we propose three novel ideas for approximating count-based estimators using bounded memory. Our first contribution, of independent interest, is an extension of reservoir sampling for sampling distinct symbols from a stream of unknown length, which we call K-distinct reservoir sampling. We combine this sampling scheme with a state-of-the-art count-based estimator for memoryless sources, the Sparse Adaptive Dirichlet (SAD) estimator. The resulting algorithm, the Budget SAD, naturally guarantees a limit on its memory usage. We finally demonstrate the broader use of K-distinct reservoir sampling in nonparametric estimation by using it to restrict the branching factor of the Context Tree Weighting algorithm. We demonstrate the usefulness of our algorithms with empirical results on two sequential, large-alphabet prediction problems.","doi":null,"url":"https://ijcai.org/proceedings/2015/470","pdf":"https://ijcai.org/proceedings/2015/0470.pdf","markdown":"https://ijcai.org/proceedings/2015/470.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/470","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Special Track on Machine Learning","track":"Special Track on Machine Learning","number":470,"pages":{"first":3337,"last":3343},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}