15 papers
Understanding and Improving Shampoo and SOAP via Kullback-Leibler Minimization
Wu Lin, Scott C. Lowe, Felix Dangel +3
Shampoo and its efficient variant, SOAP, employ structured second-moment estimations and have shown strong performance for training neural networks (NNs). In practice, however, Sha…
Exploiting weight-space symmetries for approximating curvature
Artem Artemev, Rui Xia, Benjamin M. Boyd +4
Many machine learning techniques rely on approximating a loss function's curvature, but this is notoriously hard to do at the scale of modern deep networks. Surprisingly, no previo…
Reparametrizing Shampoo and SOAP for Subspace Basis Updates and BFloat16 Storage
Alan Milligan, Zikun Xu, Simon Lacoste-Julien +2
Shampoo-based methods, such as KL-Shampoo and SOAP, have demonstrated strong performance in training neural networks and rely on QR decomposition. Because existing QR implementatio…
Dataless Weight Disentanglement in Task Arithmetic via Kronecker-Factored Approximate Curvature
Angelo Porrello, Pietro Buzzega, Felix Dangel +4
Task Arithmetic yields a modular, scalable way to adapt foundation models. Combining multiple task vectors, however, can lead to cross-task interference, causing representation dri…
Generalizing the Geometry of Model Merging Through Frechet Averages
Marvin F. da Silva, Mohammed Adnan, Felix Dangel +1
Model merging aims to combine multiple models into one without additional training. Naïve parameter-space averaging can be fragile under architectural symmetries, as their geometr…
Efficient Bilevel Optimization with KFAC-Based Hypergradients
Disen Liao, Felix Dangel, Yaoliang Yu
Bilevel optimization (BO) is widely applicable to many machine learning problems. Scaling BO, however, requires repeatedly computing hypergradients, which involves solving inverse…