Projekt per år
Sammanfattning
We present a new algorithmic approach for the task of finding a chordal Markov network structure that maximizes a given scoring function. The algorithm is based on branch and bound and integrates dynamic programming for both domain pruning and for obtaining strong bounds for search-space pruning. Empirically, we show that the approach dominates in terms of running times a recent integer programming approach (and thereby also a recent constraint optimization approach) for the problem. Furthermore, our algorithm scales at times further with respect to the number of variables than a state-of-the-art dynamic programming algorithm for the problem, with the potential of reaching 20 variables and at the same time circumventing the tight exponential lower bounds on memory consumption of the pure dynamic programming approach.
Originalspråk | engelska |
---|---|
Titel på värdpublikation | Advances in Neural Information Processing Systems 30 (NIPS 2017) |
Redaktörer | I. Guyon |
Antal sidor | 11 |
Förlag | Neural Information Processing Systems Foundation |
Utgivningsdatum | 2017 |
Status | Publicerad - 2017 |
MoE-publikationstyp | A4 Artikel i en konferenspublikation |
Evenemang | Annual Conference on Neural Information Processing Systems - Long Beach, Förenta Staterna (USA) Varaktighet: 4 dec. 2017 → 9 dec. 2017 Konferensnummer: 31 http://nips.cc/Conferences/2017 |
Publikationsserier
Namn | Advances in neural information processing systems |
---|---|
Förlag | Neural Information Processing Systems (NIPS) |
Volym | 30 |
ISSN (tryckt) | 1049-5258 |
Vetenskapsgrenar
- 113 Data- och informationsvetenskap
Projekt
- 2 Slutfört
-
Harnessing Constraint Reasoning for Structure Discovery
Järvisalo, M. (Projektledare)
01/01/2015 → 31/12/2018
Projekt: Helsingfors Universitetets treåriga forskningsprojekt
-
Polynomisen hierarkian päätösproseduurit, Boolean optimointi, ja mallien laskenta
Järvisalo, M. (Projektledare)
01/09/2014 → 31/08/2019
Projekt: Forskningsprojekt