208 citations · 361 across the 23 of their papers we have counts for
11 papers · 1 filter
Scalable Pairwise Kernel Learning with Stochastic Vec Trick
Napsu Karmitsa, Tapio Pahikkala, Antti Airola
Pairwise learning is a specialized form of supervised learning that focuses on predicting outcomes for pairs of objects. In this work, we introduce SPaiK, a new scalable kernel lea…
Interaction Concordance Index: Performance Evaluation for Interaction Prediction Methods
Tapio Pahikkala, Riikka Numminen, Parisa Movahedi +2
Consider two sets of entities and their members' mutual affinity values, say drug-target affinities (DTA). Drugs and targets are said to interact in their effects on DTAs if drug's…
Does Differentially Private Synthetic Data Lead to Synthetic Discoveries?
Ileana Montoya Perez, Parisa Movahedi, Valtteri Nieminen +2
Background: Synthetic data has been proposed as a solution for sharing anonymized versions of sensitive biomedical datasets. Ideally, synthetic data should preserve the structure a…
A Link between Coding Theory and Cross-Validation with Applications
Tapio Pahikkala, Parisa Movahedi, Ileana Montoya +4
How many different binary classification problems a single learning algorithm can solve on a fixed data with exactly zero or at most a given number of cross-validation errors? Whil…
Generalized vec trick for fast learning of pairwise kernel models
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. Prominent applications include predicting drug-target o…
A Solution for Large Scale Nonlinear Regression with High Rank and Degree at Constant Memory Complexity via Latent Tensor Reconstruction
Sandor Szedmak, Anna Cichonska, Heli Julkunen +2
This paper proposes a novel method for learning highly nonlinear, multivariate functions from examples. Our method takes advantage of the property that continuous functions can be…
Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two-Step Method
Michiel Stock, Tapio Pahikkala, Antti Airola +2
Pairwise learning or dyadic prediction concerns the prediction of properties for pairs of objects. It can be seen as an umbrella covering various machine learning problems such as…
Spectral Analysis of Symmetric and Anti-Symmetric Pairwise Kernels
Tapio Pahikkala, Markus Viljanen, Antti Airola +1
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. Such p…
A two-step learning approach for solving full and almost full cold start problems in dyadic prediction
Tapio Pahikkala, Michiel Stock, Antti Airola +3
Dyadic prediction methods operate on pairs of objects (dyads), aiming to infer labels for out-of-sample dyads. We consider the full and almost full cold start problem in dyadic pre…
Identification of functionally related enzymes by learning-to-rank methods
Michiel Stock, Thomas Fober, Eyke Hüllermeier +6
Enzyme sequences and structures are routinely used in the biological sciences as queries to search for functionally related enzymes in online databases. To this end, one usually de…
Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data
Tapio Pahikkala, Antti Airola, Michiel Stock +2
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, conditione…