44 citations · 153 across the 39 of their papers we have counts for
6 papers · 1 filter
Latent Refinement via Flow Matching for Training-free Linear Inverse Problem Solving
Hossein Askari, Yadan Luo, Hongfu Sun +1
Recent advances in inverse problem solving have increasingly adopted flow priors over diffusion models due to their ability to construct straight probability paths from noise to da…
Faithful-Newton Framework: Bridging Inner and Outer Solvers for Enhanced Optimization
Alexander Lim, Fred Roosta
Newton-type methods enjoy fast local convergence and strong empirical performance, but achieving global guarantees comparable to first-order methods remains challenging. Even for s…
Importance Sampling for Nonlinear Models
Prakash Palanivelu Rajmohan, Fred Roosta
While norm-based and leverage-score-based methods have been extensively studied for identifying "important" data points in linear models, analogous tools for nonlinear models remai…
Determinant Estimation under Memory Constraints and Neural Scaling Laws
Siavash Ameli, Chris van der Heide, Liam Hodgkinson +2
Calculating or accurately estimating log-determinants of large positive definite matrices is of fundamental importance in many machine learning tasks. While its cubic computational…
First-ish Order Methods: Hessian-aware Scalings of Gradient Descent
Oscar Smee, Fred Roosta, Stephen J. Wright
Gradient descent is the primary workhorse for optimizing large-scale problems in machine learning. However, its performance is highly sensitive to the choice of the learning rate.…
Uncertainty Quantification with the Empirical Neural Tangent Kernel
Joseph Wilson, Chris van der Heide, Liam Hodgkinson +1
While neural networks have demonstrated impressive performance across various tasks, accurately quantifying uncertainty in their predictions is essential to ensure their trustworth…