6 papers
MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning
Javier Lopez-Piqueres, Pranav Deshpande, Archan Ray +3
We present MetaTT, a Tensor Train (TT) adapter framework for fine-tuning of pre-trained transformers. MetaTT enables flexible and parameter-efficient model adaptation by using a si…
CRUMB: Efficient Prior Fitted Network Inference via Distributionally Matched Context Batching
Jamie Heredge, Mattia J. Villani, Pranav Deshpande +2
Prior-fitted networks (PFNs) are a promising class of tabular foundation models that perform in-context learning, whereby the entire labelled training set is supplied as context, a…
Entropy Distribution as a Fingerprint for Hallucinations in Generative Models
Mattia J. Villani, Pranav Deshpande, Akshay Seshadri +2
Large Language Models (LLMs) often generate factually incorrect outputs, commonly termed hallucinations, that undermine trust and limit deployment in high-stakes settings. Existing…
A Unified Framework for Provably Efficient Algorithms to Estimate Shapley Values
Tyler Chen, Akshay Seshadri, Mattia J. Villani +7
Shapley values have emerged as a critical tool for explaining which features impact the decisions made by machine learning models. However, computing exact Shapley values is diffic…
Trading-off Accuracy and Communication Cost in Federated Learning
Mattia Jacopo Villani, Emanuele Natale, Frederik Mallmann-Trenn
Leveraging the training-by-pruning paradigm introduced by Zhou et al. and Isik et al. introduced a federated learning protocol that achieves a 34-fold reduction in communication co…
Relating Piecewise Linear Kolmogorov Arnold Networks to ReLU Networks
Nandi Schoots, Mattia Jacopo Villani, Niels uit de Bos
Kolmogorov-Arnold Networks are a new family of neural network architectures which holds promise for overcoming the curse of dimensionality and has interpretability benefits (arXiv:…