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Terje Mildner

dblp:399/1485 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Probabilistic and Bayesian machine learning · 56% Efficient and distributed learning · 28% Trustworthy machine learning · 17%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning › probabilistic inference › approximate inference › variational inference
collapsed variational inference		 0.912025
Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework · ICML 2025
Machine learning › Efficient and distributed learning
federated learning		 0.912025
Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework · ICML 2025
Machine learning › Probabilistic and Bayesian machine learning › probabilistic inference › approximate inference
variational inference		 0.912025
Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework · ICML 2025
Machine learning › Trustworthy machine learning › uncertainty estimation
uncertainty calibration		 0.312025
Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework · ICML 2025
Machine learning › Trustworthy machine learning
uncertainty estimation		 0.312025
Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework · ICML 2025

Methods — techniques the papers use, named apart from their topics

variational inference · 0.9bayesian inference · 0.9
YearPublicationVenuePosition
2025 Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework
abstract
We introduce FedGVI, a probabilistic Federated Learning (FL) framework that is robust to both prior and likelihood misspecification. FedGVI addresses limitations in both frequentist and Bayesian FL by providing unbiased predictions under model misspecification, with calibrated uncertainty quantification. Our approach generalises previous FL approaches, specifically Partitioned Variational Inference (Ashman et al., 2022), by allowing robust and conjugate updates, decreasing computational complexity at the clients. We offer theoretical analysis in terms of fixed-point convergence, optimality of the cavity distribution, and provable robustness to likelihood misspecification. Further, we empirically demonstrate the effectiveness of FedGVI in terms of improved robustness and predictive performance on multiple synthetic and real world classification data sets.
Terje Mildner, Oliver Hamelijnck, Paris Giampouras, Theodoros Damoulas
 ICML1