no code implementations • 20 Mar 2024 • Ileana Montoya Perez, Parisa Movahedi, Valtteri Nieminen, Antti Airola, Tapio Pahikkala
Objectives: The aim of this study is to evaluate the Mann-Whitney U test on DP-synthetic biomedical data in terms of Type I and Type II errors, in order to establish whether statistical hypothesis testing performed on privacy preserving synthetic data is likely to lead to loss of test's validity or decreased power.
no code implementations • 22 Mar 2021 • Tapio Pahikkala, Parisa Movahedi, Ileana Montoya, Havu Miikonen, Stephan Foldes, Antti Airola, Laszlo Major
We show that the maximal number of classification problems with fixed class proportion, for which a learning algorithm can achieve zero LPOCV error, equals the maximal number of code words in a constant weight code (CWC), with certain technical properties.
no code implementations • 21 Dec 2020 • Markus Viljanen, Tapio Pahikkala
The goal of recommender systems is to help users find useful items from a large catalog of items by producing a list of item recommendations for every user.
no code implementations • 11 Sep 2020 • Markus Viljanen, Jukka Vahlo, Aki Koponen, Tapio Pahikkala
In this paper, we use a survey data set of game likes to present content based interaction models that generalize into new games, new players, and both new games and players simultaneously.
1 code implementation • 2 Sep 2020 • Markus Viljanen, Antti Airola, Tapio Pahikkala
Pairwise learning corresponds to the supervised learning setting where the goal is to make predictions for pairs of objects.
no code implementations • 28 May 2020 • Jonne Pohjankukka, Tapio Pahikkala, Paavo Nevalainen, Jukka Heikkonen
To overcome this problem we propose a modified version of the CV method called spatial k-fold cross validation (SKCV), which provides a useful estimate for model prediction performance without optimistic bias due to SAC.
no code implementations • 4 May 2020 • Sandor Szedmak, Anna Cichonska, Heli Julkunen, Tapio Pahikkala, Juho Rousu
For learning the models, we present an efficient gradient-based algorithm that can be implemented in linear time in the sample size, order, rank of the tensor and the dimension of the input.
no code implementations • 5 Mar 2018 • Michiel Stock, Tapio Pahikkala, Antti Airola, Bernard De Baets, Willem Waegeman
Problems of that kind are often referred to as pairwise learning, dyadic prediction or network inference problems.
no code implementations • 29 Jan 2018 • Ileana Montoya Perez, Antti Airola, Peter J. Boström, Ivan Jambor, Tapio Pahikkala
This method extends LPO by creating a tournament from pair comparisons to produce a ranking for the data.
no code implementations • 4 Jan 2017 • Markus Viljanen, Antti Airola, Jukka Heikkonen, Tapio Pahikkala
Throughout this paper, we illustrate the application of these methods to real world game development problems on the Hipster Sheep mobile game.
no code implementations • 14 Jun 2016 • Michiel Stock, Tapio Pahikkala, Antti Airola, Bernard De Baets, Willem Waegeman
In this work we analyze kernel-based methods for pairwise learning, with a particular focus on a recently-suggested two-step method.
no code implementations • 7 Jan 2016 • Antti Airola, Tapio Pahikkala
Kronecker product kernel provides the standard approach in the kernel methods literature for learning from graph data, where edges are labeled and both start and end vertices have their own feature representations.
no code implementations • 19 Jun 2015 • Tapio Pahikkala, Markus Viljanen, Antti Airola, Willem Waegeman
We consider the problem of learning regression functions from pairwise data when there exists prior knowledge that the relation to be learned is symmetric or anti-symmetric.
no code implementations • 17 May 2014 • Michiel Stock, Thomas Fober, Eyke Hüllermeier, Serghei Glinca, Gerhard Klebe, Tapio Pahikkala, Antti Airola, Bernard De Baets, Willem Waegeman
For a given query, the search operation results in a ranking of the enzymes in the database, from very similar to dissimilar enzymes, while information about the biological function of annotated database enzymes is ignored.
1 code implementation • 17 May 2014 • Tapio Pahikkala, Michiel Stock, Antti Airola, Tero Aittokallio, Bernard De Baets, Willem Waegeman
Dyadic prediction methods operate on pairs of objects (dyads), aiming to infer labels for out-of-sample dyads.
no code implementations • 21 Sep 2012 • Tapio Pahikkala, Antti Airola, Michiel Stock, Bernard De Baets, Willem Waegeman
In domains like bioinformatics, information retrieval and social network analysis, one can find learning tasks where the goal consists of inferring a ranking of objects, conditioned on a particular target object.