Institution

# Helsinki Institute for Information Technology

Facility•Espoo, Finland•

About: Helsinki Institute for Information Technology is a(n) facility organization based out in Espoo, Finland. It is known for research contribution in the topic(s): Population & Bayesian network. The organization has 630 authors who have published 1962 publication(s) receiving 63426 citation(s).

##### Papers published on a yearly basis

##### Papers

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27 Aug 2007

TL;DR: The Data-Oriented Network Architecture (DONA) is proposed, which involves a clean-slate redesign of Internet naming and name resolution to adapt to changes in Internet usage.

Abstract: The Internet has evolved greatly from its original incarnation. For instance, the vast majority of current Internet usage is data retrieval and service access, whereas the architecture was designed around host-to-host applications such as telnet and ftp. Moreover, the original Internet was a purely transparent carrier of packets, but now the various network stakeholders use middleboxes to improve security and accelerate applications. To adapt to these changes, we propose the Data-Oriented Network Architecture (DONA), which involves a clean-slate redesign of Internet naming and name resolution.

1,585 citations

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[...]

TL;DR: In this paper, leave-one-out cross-validation (LOO) and the widely applicable information criterion (WAIC) are used to estimate pointwise out-of-sample prediction accuracy from a fitted Bayesian model using the log-likelihood evaluated at the posterior simulations of the parameter values.

Abstract: Leave-one-out cross-validation (LOO) and the widely applicable information criterion (WAIC) are methods for estimating pointwise out-of-sample prediction accuracy from a fitted Bayesian model using the log-likelihood evaluated at the posterior simulations of the parameter values. LOO and WAIC have various advantages over simpler estimates of predictive error such as AIC and DIC but are less used in practice because they involve additional computational steps. Here we lay out fast and stable computations for LOO and WAIC that can be performed using existing simulation draws. We introduce an efficient computation of LOO using Pareto-smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. Although WAIC is asymptotically equal to LOO, we demonstrate that PSIS-LOO is more robust in the finite case with weak priors or influential observations. As a byproduct of our calculations, we also obtain approximate standard errors for estimated predictive errors and for comparison of predictive errors between two models. We implement the computations in an R package called loo and demonstrate using models fit with the Bayesian inference package Stan.

1,533 citations

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[...]

TL;DR: In this article, leave-one-out cross-validation (LOO) and the widely applicable information criterion (WAIC) are used to estimate pointwise out-of-sample prediction accuracy from a fitted Bayesian model using the log-likelihood evaluated at the posterior simulations of the parameter values.

Abstract: Leave-one-out cross-validation (LOO) and the widely applicable information criterion (WAIC) are methods for estimating pointwise out-of-sample prediction accuracy from a fitted Bayesian model using the log-likelihood evaluated at the posterior simulations of the parameter values. LOO and WAIC have various advantages over simpler estimates of predictive error such as AIC and DIC but are less used in practice because they involve additional computational steps. Here we lay out fast and stable computations for LOO and WAIC that can be performed using existing simulation draws. We introduce an efficient computation of LOO using Pareto-smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. Although WAIC is asymptotically equal to LOO, we demonstrate that PSIS-LOO is more robust in the finite case with weak priors or influential observations. As a byproduct of our calculations, we also obtain approximate standard errors for estimated predictive errors and for comparing of predictive errors between two models. We implement the computations in an R package called 'loo' and demonstrate using models fit with the Bayesian inference package Stan.

1,349 citations

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TL;DR: New algorithms that reduce the database activity considerably by picking a Random sample, to find using this sample all association rules that probably hold in the whole database, and then to verify the results with the rest of the database.

Abstract: Discovery of association rules .is an important database mining problem. Current algorithms for finding association rules require several passes over the analyzed database, and obviously the role of I/O overhead is very significant for very large databases. We present new algorithms that reduce the database activity considerably. The idea is to pick a Random sample, to find using this sample all association rules that probably hold in the whole database, and then to verify the results with the rest of the database. The algorithms thus produce exact association rules, not approximations based on a sample. The approach is, however, probabilistic, and in those rare cases where our sampling method does not produce all association rules, the missing rules can be found in a second pass. Our experiments show that the proposed algorithms can find association rules very efficiently in only one database

1,230 citations

##### Authors

Showing all 630 results

Name | H-index | Papers | Citations |
---|---|---|---|

Dimitri P. Bertsekas | 94 | 332 | 85939 |

Olli Kallioniemi | 90 | 353 | 42021 |

Heikki Mannila | 72 | 295 | 26500 |

Jukka Corander | 66 | 411 | 17220 |

Jaakko Kangasjärvi | 62 | 146 | 17096 |

Aapo Hyvärinen | 61 | 301 | 44146 |

Samuel Kaski | 58 | 522 | 14180 |

Nadarajah Asokan | 58 | 327 | 11947 |

Aristides Gionis | 58 | 292 | 19300 |

Hannu Toivonen | 56 | 192 | 19316 |

Nicola Zamboni | 53 | 128 | 11397 |

Jorma Rissanen | 52 | 151 | 22720 |

Tero Aittokallio | 52 | 271 | 8689 |

Juha Veijola | 52 | 261 | 19588 |

Juho Hamari | 51 | 176 | 16631 |