7 papers · 1 filter
BrainSurgery: Reproducible and Reliable Declarative Weight Manipulations for Model Editing and Upcycling
Gianluca Barmina, Annemette Broch Pirchert, Andrea Blasi Núñez +2
As deep learning models scale, managing, inspecting, and modifying large checkpoints has become increasingly challenging. Researchers often need to alter model weights for layer re…
Disjoint Generation of Synthetic Data
Anton Danholt Lautrup, Muhammad Rajabinasab, Tobias Hyrup +2
We propose a new framework for generating tabular synthetic datasets via disjoint generative models. In this paradigm, a dataset is partitioned into disjoint subsets that are suppl…
Randomized PCA Forest for Unsupervised Outlier Detection
Muhammad Rajabinasab, Farhad Pakdaman, Moncef Gabbouj +2
We propose a novel unsupervised outlier detection method based on Randomized Principal Component Analysis (PCA). Motivated by the performance of Randomized PCA (RPCA) Forest in app…
FlexMoRE: A Flexible Mixture of Rank-heterogeneous Experts for Efficient Federatedly-trained Large Language Models
Annemette Brok Pirchert, Jacob Nielsen, Mogens Henrik From +2
Recent advances in mixture-of-experts architectures have shown that individual experts models can be trained federatedly, i.e., in isolation from other experts by using a common ba…
Achieving Hilbert-Schmidt Independence Under Rényi Differential Privacy for Fair and Private Data Generation
Tobias Hyrup, Emmanouil Panagiotou, Arjun Roy +3
As privacy regulations such as the GDPR and HIPAA and responsibility frameworks for artificial intelligence such as the AI Act gain traction, the ethical and responsible use of rea…
Sharing is CAIRing: Characterizing Principles and Assessing Properties of Universal Privacy Evaluation for Synthetic Tabular Data
Tobias Hyrup, Anton Danholt Lautrup, Arthur Zimek +1
Data sharing is a necessity for innovative progress in many domains, especially in healthcare. However, the ability to share data is hindered by regulations protecting the privacy…