44 citations · 153 across the 39 of their papers we have counts for
6 papers · 1 filter
Equilibrium Learning in Unbounded Stochastic Games with Gaussian Transitions
Jonathan Anthony Mavroforas, Fred Roosta
Learning equilibria from data is difficult when several decision makers interact indefinitely and the system state has no prescribed bounds. We examine discounted general-sum stoch…
Why Heavy-Tailed Weight Spectra Predict Model Quality
Joseph Wilson, Chris van der Heide, Liam Hodgkinson +3
A solid understanding of the predictive success of deep neural networks (DNNs) remains elusive. For example, while many data-dependent metrics exist that seek to predict model qual…
Primal-Dual Inexact Newton-MR for Nonconvex Optimization with Equality Constraints
Oscar Smee, Fred Roosta
Optimization problems with nonlinear equality constraints arise throughout science, engineering, and increasingly in machine learning. Prominent methods for solving such problems i…
Is the Last Layer Sufficient for Uncertainty Quantification?
Joseph Wilson, Chris van der Heide, Liam Hodgkinson +1
Epistemic uncertainty quantification (UQ) for deep neural networks (DNNs) is a requirement for safe adoption of AI in mission-critical settings. Several leading methods for UQ line…
The Geometric Wall: Manifold Structure Predicts Layerwise Sparse Autoencoder Scaling Laws
Eslam Zaher, Maciej Trzaskowski, Quan Nguyen +1
Sparse autoencoders (SAEs) operationalise the linear representation hypothesis: they reconstruct model activations as sparse linear combinations of interpretable dictionary atoms,…
Counterfactual Explanations on Robust Perceptual Geodesics
Eslam Zaher, Maciej Trzaskowski, Quan Nguyen +1
Latent-space optimization methods for counterfactual explanations - framed as minimal semantic perturbations that change model predictions - inherit the ambiguity of Wachter et al.…