1 citations · 2 across the 5 of their papers we have counts for
5 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…
Inexact Limited Memory Bundle Method
Jenni Lampainen, Kaisa Joki, Napsu Karmitsa +1
Large-scale nonsmooth optimization problems arise in many real-world applications, but obtaining exact function and subgradient values for these problems may be computationally exp…
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…
Clust-Splitter - an Efficient Nonsmooth Optimization-Based Algorithm for Clustering Large Datasets
Jenni Lampainen, Kaisa Joki, Napsu Karmitsa +1
Clustering is a fundamental task in data mining and machine learning, particularly for analyzing large-scale data. In this paper, we introduce Clust-Splitter, an efficient algorith…