208 citations · 361 across the 23 of their papers we have counts for
23 papers
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…
A Comprehensive Guide to Differential Privacy: From Theory to User Expectations
Napsu Karmitsa, Antti Airola, Tapio Pahikkala +1
The increasing availability of personal data has enabled significant advances in fields such as machine learning, healthcare, and cybersecurity. However, this data abundance also r…
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…
New Recommendation Algorithm for Implicit Data Motivated by the Multivariate Normal Distribution
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. Data sets based on impl…
Content Based Player and Game Interaction Model for Game Recommendation in the Cold Start setting
Markus Viljanen, Jukka Vahlo, Aki Koponen +1
Game recommendation is an important application of recommender systems. Recommendations are made possible by data sets of historical player and game interactions, and sometimes the…
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…
Estimating the Prediction Performance of Spatial Models via Spatial k-Fold Cross Validation
Jonne Pohjankukka, Tapio Pahikkala, Paavo Nevalainen +1
In machine learning one often assumes the data are independent when evaluating model performance. However, this rarely holds in practise. Geographic information data sets are an ex…
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…
A Comparative Study of Pairwise Learning Methods based on Kernel Ridge Regression
Michiel Stock, Tapio Pahikkala, Antti Airola +2
Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction or…
Tournament Leave-pair-out Cross-validation for Receiver Operating Characteristic (ROC) Analysis
Ileana Montoya Perez, Antti Airola, Peter J. Boström +2
Receiver operating characteristic (ROC) analysis is widely used for evaluating diagnostic systems. Recent studies have shown that estimating an area under ROC curve (AUC) with stan…
Measuring Player Retention and Monetization using the Mean Cumulative Function
Markus Viljanen, Antti Airola, Anne-Maarit Majanoja +2
Game analytics supports game development by providing direct quantitative feedback about player experience. Player retention and monetization in particular have become central busi…
Playtime Measurement with Survival Analysis
Markus Viljanen, Antti Airola, Jukka Heikkonen +1
Maximizing product use is a central goal of many businesses, which makes retention and monetization two central analytics metrics in games. Player retention may refer to various du…
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…
Fast Kronecker product kernel methods via generalized vec trick
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…
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…
A kernel-based framework for learning graded relations from data
Willem Waegeman, Tapio Pahikkala, Antti Airola +3
Driven by a large number of potential applications in areas like bioinformatics, information retrieval and social network analysis, the problem setting of inferring relations betwe…
Training linear ranking SVMs in linearithmic time using red-black trees
Antti Airola, Tapio Pahikkala, Tapio Salakoski
We introduce an efficient method for training the linear ranking support vector machine. The method combines cutting plane optimization with red-black tree based approach to subgra…
Linear Time Feature Selection for Regularized Least-Squares
Tapio Pahikkala, Antti Airola, Tapio Salakoski
We propose a novel algorithm for greedy forward feature selection for regularized least-squares (RLS) regression and classification, also known as the least-squares support vector…