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 Pekka Parviainen

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Average citations per article3.46
Citation Count45
Publication count13
Publication years2009-2017
Available for download6
Average downloads per article84.00
Downloads (cumulative)504
Downloads (12 Months)68
Downloads (6 Weeks)5
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13 results found Export Results: bibtexendnoteacmrefcsv

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1
September 2017 International Journal of Approximate Reasoning: Volume 88 Issue C, September 2017
Publisher: Elsevier Science Inc.
Bibliometrics:
Citation Count: 0

Bayesian networks, and especially their structures, are powerful tools for representing conditional independencies and dependencies between random variables. In applications where related variables form a priori known groups, chosen to represent different views to or aspects of the same entities, one may be more interested in modeling dependencies between groups ...
Keywords: Bayesian networks, Conditional independence, Multi-view learning, Structure learning

2
January 2016 The Journal of Machine Learning Research: Volume 17 Issue 1, January 2016
Publisher: JMLR.org
Bibliometrics:
Citation Count: 2
Downloads (6 Weeks): 0,   Downloads (12 Months): 5,   Downloads (Overall): 12

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We present methods based on Metropolis-coupled Markov chain Monte Carlo (MC 3 ) and annealed importance sampling (AIS) for estimating the posterior distribution of Bayesian networks. The methods draw samples from an appropriate distribution of partial orders on the nodes, continued by sampling directed acyclic graphs (DAGs) conditionally on the ...
Keywords: linear extension, annealed importance sampling, Markov chain Monte Carlo, directed acyclic graph, fast zeta transform

3 published by ACM
December 2015 ACM Transactions on Algorithms (TALG) - Special Issue on SODA'12 and Regular Papers: Volume 12 Issue 1, February 2016
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 3,   Downloads (12 Months): 32,   Downloads (Overall): 97

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We investigate fast algorithms for changing between the standard basis and an orthogonal basis of idempotents for Möbius algebras of finite lattices. We show that every lattice with v elements, n of which are nonzero and join-irreducible (or, by a dual result, nonzero and meet-irreducible), has arithmetic circuits of size ...
Keywords: Arithmetic circuit, Möbius inversion, fast multiplication, lattice, zeta transform, Möbius transform, semigroup algebra

4
December 2015 NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1
Publisher: MIT Press
Bibliometrics:
Citation Count: 0

Both learning and inference tasks on Bayesian networks are NP-hard in general. Bounded tree-width Bayesian networks have recently received a lot of attention as a way to circumvent this complexity issue; however, while inference on bounded tree-width networks is tractable, the learning problem remains NP-hard even for tree-width 2. In ...

5
July 2015 MUD'15: Proceedings of the 2nd International Conference on Mining Urban Data - Volume 1392
Publisher: CEUR-WS.org
Bibliometrics:
Citation Count: 0

This pilot study investigates how considering accessibility could help to model prices of residential real estate more accurately. We introduce two novelties from the price modeling point of view (1) defining accessibility as travel time by public transport, in addition to geographic distance, and (2) considering dynamic points of interest ...

6
May 2013 The Journal of Machine Learning Research: Volume 14 Issue 1, January 2013
Publisher: JMLR.org
Bibliometrics:
Citation Count: 4
Downloads (6 Weeks): 1,   Downloads (12 Months): 7,   Downloads (Overall): 60

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We consider the problem of finding a directed acyclic graph (DAG) that optimizes a decomposable Bayesian network score. While in a favorable case an optimal DAG can be found in polynomial time, in the worst case the fastest known algorithms rely on dynamic programming across the node subsets, taking time ...
Keywords: exact algorithm, space-time tradeoff, parallelization, partial order, structure learning

7
August 2012 UAI'12: Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence
Publisher: AUAI Press
Bibliometrics:
Citation Count: 2

Learning a Bayesian network structure from data is an NP-hard problem and thus exact algorithms are feasible only for small data sets. Therefore, network structures for larger networks are usually learned with various heuristics. Another approach to scaling up the structure learning is local learning. In local learning, the modeler ...

8
January 2012 SODA '12: Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete algorithms
Publisher: Society for Industrial and Applied Mathematics
Bibliometrics:
Citation Count: 4
Downloads (6 Weeks): 0,   Downloads (12 Months): 7,   Downloads (Overall): 151

Full text available: PDFPDF
We investigate fast algorithms for changing between the standard basis and an orthogonal basis of idempotents for Möbius algebras of finite lattices. We show that every lattice with v elements, n of which are nonzero and join-irreducible (or, by a dual result, nonzero and meet-irreducible), has arithmetic circuits of size ...

9
September 2011 ECML PKDD'11: Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Publisher: Springer-Verlag
Bibliometrics:
Citation Count: 2

Bayesian networks (BNs) are an appealing model for causal and noncausal dependencies among a set of variables. Learning BNs from observational data is challenging due to the nonidentifiability of the network structure and model misspecification in the presence of unobserved (latent) variables. Here, we investigate the prospects of Bayesian learning ...

10
September 2011 ECMLPKDD'11: Proceedings of the 2011th European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Publisher: Springer-Verlag
Bibliometrics:
Citation Count: 0

Bayesian networks (BNs) are an appealing model for causal and non-causal dependencies among a set of variables. Learning BNs from observational data is challenging due to the nonidentifiability of the network structure and model misspecification in the presence of unobserved (latent) variables. Here, we investigate the prospects of Bayesian learning ...

11
July 2011 UAI'11: Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence
Publisher: AUAI Press
Bibliometrics:
Citation Count: 0

We present a new Markov chain Monte Carlo method for estimating posterior probabilities of structural features in Bayesian networks. The method draws samples from the posterior distribution of partial orders on the nodes; for each sampled partial order, the conditional probabilities of interest are computed exactly. We give both analytical ...

12
January 2010 SODA '10: Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete algorithms
Publisher: Society for Industrial and Applied Mathematics
Bibliometrics:
Citation Count: 8
Downloads (6 Weeks): 0,   Downloads (12 Months): 8,   Downloads (Overall): 108

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Many combinatorial problems---such as the traveling salesman, feedback arcset, cutwidth, and treewidth problem---can be formulated as finding a feasible permutation of n elements. Typically, such problems can be solved by dynamic programming in time and space O * (2 n ), by divide and conquer in time O * (4 ...

13
June 2009 UAI '09: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Publisher: AUAI Press
Bibliometrics:
Citation Count: 23
Downloads (6 Weeks): 1,   Downloads (12 Months): 9,   Downloads (Overall): 76

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The fastest known exact algorithms for score-based structure discovery in Bayesian networks on n nodes run in time and space 2 n n O(1) . The usage of these algorithms is limited to networks on at most around 25 nodes mainly due to the space requirement. Here, we study space--time ...



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