69 citations · 1.1k across the 120 of their papers we have counts for
43 papers · 1 filter
Cooperative Bayesian Optimization for Imperfect Agents
Ali Khoshvishkaie, Petrus Mikkola, Pierre-Alexandre Murena +1
We introduce a cooperative Bayesian optimization problem for optimizing black-box functions of two variables where two agents choose together at which points to query the function…
Expert-Aided Causal Discovery of Ancestral Graphs
Tiago da Silva, Bruna Bazaluk, Eliezer de Souza da Silva +6
Causal discovery (CD) is an important component of many scientific applications, yet most techniques produce unreliable point estimates that often contradict expert knowledge. To m…
Collaborative Learning From Distributed Data With Differentially Private Synthetic Twin Data
Lukas Prediger, Joonas Jälkö, Antti Honkela +1
Consider a setting where multiple parties holding sensitive data aim to collaboratively learn population level statistics, but pooling the sensitive data sets is not possible. We p…
Learning Relevant Contextual Variables Within Bayesian Optimization
Julien Martinelli, Ayush Bharti, Armi Tiihonen +5
Contextual Bayesian Optimization (CBO) efficiently optimizes black-box functions with respect to design variables, while simultaneously integrating contextual information regarding…
TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series
Alexander Nikitin, Letizia Iannucci, Samuel Kaski
Temporally indexed data are essential in a wide range of fields and of interest to machine learning researchers. Time series data, however, are often scarce or highly sensitive, wh…
Online simulator-based experimental design for cognitive model selection
Alexander Aushev, Aini Putkonen, Gregoire Clarte +4
The problem of model selection with a limited number of experimental trials has received considerable attention in cognitive science, where the role of experiments is to discrimina…
Differentiable User Models
Alex Hämäläinen, Mustafa Mert Çelikok, Samuel Kaski
Probabilistic user modeling is essential for building machine learning systems in the ubiquitous cases with humans in the loop. However, modern advanced user models, often designed…
Modular Flows: Differential Molecular Generation
Yogesh Verma, Samuel Kaski, Markus Heinonen +1
Generating new molecules is fundamental to advancing critical applications such as drug discovery and material synthesis. Flows can generate molecules effectively by inverting the…
Multi-Fidelity Bayesian Optimization with Unreliable Information Sources
Petrus Mikkola, Julien Martinelli, Louis Filstroff +1
Bayesian optimization (BO) is a powerful framework for optimizing black-box, expensive-to-evaluate functions. Over the past decade, many algorithms have been proposed to integrate…
Provably expressive temporal graph networks
Amauri H. Souza, Diego Mesquita, Samuel Kaski +1
Temporal graph networks (TGNs) have gained prominence as models for embedding dynamic interactions, but little is known about their theoretical underpinnings. We establish fundamen…
Bayesian Optimization Augmented with Actively Elicited Expert Knowledge
Daolang Huang, Louis Filstroff, Petrus Mikkola +2
Bayesian optimization (BO) is a well-established method to optimize black-box functions whose direct evaluations are costly. In this paper, we tackle the problem of incorporating e…
Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations
Alexander Nikitin, Samuel Kaski
Predictive maintenance (PdM) is the task of scheduling maintenance operations based on a statistical analysis of the system's condition. We propose a human-in-the-loop PdM approach…
Non-separable Spatio-temporal Graph Kernels via SPDEs
Alexander Nikitin, ST John, Arno Solin +1
Gaussian processes (GPs) provide a principled and direct approach for inference and learning on graphs. However, the lack of justified graph kernels for spatio-temporal modelling h…
Likelihood-Free Inference in State-Space Models with Unknown Dynamics
Alexander Aushev, Thong Tran, Henri Pesonen +2
Likelihood-free inference (LFI) has been successfully applied to state-space models, where the likelihood of observations is not available but synthetic observations generated by a…
Behaviour-conditioned policies for cooperative reinforcement learning tasks
Antti Keurulainen, Isak Westerlund, Ariel Kwiatkowski +2
The cooperation among AI systems, and between AI systems and humans is becoming increasingly important. In various real-world tasks, an agent needs to cooperate with unknown partne…
Stochastic Cluster Embedding
Zhirong Yang, Yuwei Chen, Denis Sedov +2
Neighbor Embedding (NE) aims to preserve pairwise similarities between data items and has been shown to yield an effective principle for data visualization. However, even the best…
Variational multiple shooting for Bayesian ODEs with Gaussian processes
Pashupati Hegde, Çağatay Yıldız, Harri Lähdesmäki +2
Recent machine learning advances have proposed black-box estimation of unknown continuous-time system dynamics directly from data. However, earlier works are based on approximative…
D3p -- A Python Package for Differentially-Private Probabilistic Programming
Lukas Prediger, Niki Loppi, Samuel Kaski +1
We present d3p, a software package designed to help fielding runtime efficient widely-applicable Bayesian inference under differential privacy guarantees. d3p achieves general appl…
Decision Rule Elicitation for Domain Adaptation
Alexander Nikitin, Samuel Kaski
Human-in-the-loop machine learning is widely used in artificial intelligence (AI) to elicit labels for data points from experts or to provide feedback on how close the predicted re…
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…