5 papers
Improving Clinical Dataset Condensation with Mode Connectivity-based Trajectory Surrogates
Pafue Christy Nganjimi, Andrew Soltan, Danielle Belgrave +3
Dataset condensation (DC) enables the creation of compact, privacy-preserving synthetic datasets that can match the utility of real patient records, supporting democratised access…
Geometric Uncertainty for Detecting and Correcting Hallucinations in LLMs
Edward Phillips, Sean Wu, Soheila Molaei +3
Large language models demonstrate impressive results across diverse tasks but are still known to hallucinate, generating linguistically plausible but incorrect answers to questions…
Aggregation on Learnable Manifolds for Asynchronous Federated Optimization
Archie Licudi, Anshul Thakur, Soheila Molaei +2
Asynchronous federated learning (FL) with heterogeneous clients faces two key issues: curvature-induced loss barriers encountered by standard linear parameter interpolation techniq…
Efficient Task Grouping Through Samplewise Optimisation Landscape Analysis
Anshul Thakur, Yichen Huang, Soheila Molaei +2
Shared training approaches, such as multi-task learning (MTL) and gradient-based meta-learning, are widely used in various machine learning applications, but they often suffer from…
FOCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics
Pramit Saha, Felix Wagner, Divyanshu Mishra +5
Effective training of large Vision-Language Models (VLMs) on resource-constrained client devices in Federated Learning (FL) requires the usage of parameter-efficient fine-tuning (P…