69 citations · 1.1k across the 120 of their papers we have counts for
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Differentially Private Bayesian Inference for Generalized Linear Models
Tejas Kulkarni, Joonas Jälkö, Antti Koskela +2
Generalized linear models (GLMs) such as logistic regression are among the most widely used arms in data analyst's repertoire and often used on sensitive datasets. A large body of…
Rethinking pooling in graph neural networks
Diego Mesquita, Amauri H. Souza, Samuel Kaski
Graph pooling is a central component of a myriad of graph neural network (GNN) architectures. As an inheritance from traditional CNNs, most approaches formulate graph pooling as a…
Bayesian Inference for Optimal Transport with Stochastic Cost
Anton Mallasto, Markus Heinonen, Samuel Kaski
In machine learning and computer vision, optimal transport has had significant success in learning generative models and defining metric distances between structured and stochastic…
Privacy-preserving Data Sharing on Vertically Partitioned Data
Razane Tajeddine, Joonas Jälkö, Samuel Kaski +1
In this work, we introduce a differentially private method for generating synthetic data from vertically partitioned data, \emph{i.e.}, where data of the same individuals is distri…
Teaching to Learn: Sequential Teaching of Agents with Inner States
Mustafa Mert Celikok, Pierre-Alexandre Murena, Samuel Kaski
In sequential machine teaching, a teacher's objective is to provide the optimal sequence of inputs to sequential learners in order to guide them towards the best model. In this pap…
Likelihood-Free Inference with Deep Gaussian Processes
Alexander Aushev, Henri Pesonen, Markus Heinonen +2
In recent years, surrogate models have been successfully used in likelihood-free inference to decrease the number of simulator evaluations. The current state-of-the-art performance…
A High-Performance Implementation of Bayesian Matrix Factorization with Limited Communication
Tom Vander Aa, Xiangju Qin, Paul Blomstedt +3
Matrix factorization is a very common machine learning technique in recommender systems. Bayesian Matrix Factorization (BMF) algorithms would be attractive because of their ability…