Roosta, Fred

24 publications

ICML 2025 Determinant Estimation Under Memory Constraints and Neural Scaling Laws Siavash Ameli, Chris Van Der Heide, Liam Hodgkinson, Fred Roosta, Michael W. Mahoney

ICML 2025 Importance Sampling for Nonlinear Models Prakash Palanivelu Rajmohan, Fred Roosta

NeurIPS 2025 Latent Refinement via Flow Matching for Training-Free Linear Inverse Problem Solving Hossein Askari, Yadan Luo, Hongfu Sun, Fred Roosta

WACV 2025 Training-Free Medical Image Inverses via Bi-Level Guided Diffusion Models Hossein Askari, Fred Roosta, Hongfu Sun

NeurIPS 2025 Uncertainty Quantification with the Empirical Neural Tangent Kernel Joseph Wilson, Chris van der Heide, Liam Hodgkinson, Fred Roosta

ICML 2024 Inexact Newton-Type Methods for Optimisation with Nonnegativity Constraints Oscar Smee, Fred Roosta

ICML 2024 Manifold Integrated Gradients: Riemannian Geometry for Feature Attribution Eslam Zaher, Maciej Trzaskowski, Quan Nguyen, Fred Roosta

TMLR 2024 Non-Uniform Smoothness for Gradient Descent Albert S. Berahas, Lindon Roberts, Fred Roosta

NeurIPSW 2023 A PAC-Bayesian Perspective on the Interpolating Information Criterion Liam Hodgkinson, Chris van der Heide, Robert Salomone, Fred Roosta, Michael Mahoney

ICML 2023 Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes Liam Hodgkinson, Chris Van Der Heide, Fred Roosta, Michael W. Mahoney

JMLR 2022 LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data Ali Eshragh, Fred Roosta, Asef Nazari, Michael W. Mahoney

AISTATS 2021 Shadow Manifold Hamiltonian Monte Carlo Chris Heide, Fred Roosta, Liam Hodgkinson, Dirk Kroese

AAAI 2021 Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks Russell Tsuchida, Tim Pearce, Christopher van der Heide, Fred Roosta, Marcus Gallagher

JMLR 2021 Implicit Langevin Algorithms for Sampling from Log-Concave Densities Liam Hodgkinson, Robert Salomone, Fred Roosta

JMLR 2021 Limit Theorems for Out-of-Sample Extensions of the Adjacency and Laplacian Spectral Embeddings Keith D. Levin, Fred Roosta, Minh Tang, Michael W. Mahoney, Carey E. Priebe

UAI 2021 Non-PSD Matrix Sketching with Applications to Regression and Optimization Zhili Feng, Fred Roosta, David P. Woodruff

UAI 2021 Stochastic Continuous Normalizing Flows: Training SDEs as ODEs Liam Hodgkinson, Chris Heide, Fred Roosta, Michael W. Mahoney

ICML 2020 DINO: Distributed Newton-Type Optimization Method Rixon Crane, Fred Roosta

NeurIPS 2019 DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization Rixon Crane, Fred Roosta

NeurIPS 2018 GIANT: Globally Improved Approximate Newton Method for Distributed Optimization Shusen Wang, Fred Roosta, Peng Xu, Michael W. Mahoney

ICML 2018 Invariance of Weight Distributions in Rectified MLPs Russell Tsuchida, Fred Roosta, Marcus Gallagher

ICML 2018 Out-of-Sample Extension of Graph Adjacency Spectral Embedding Keith Levin, Fred Roosta, Michael Mahoney, Carey Priebe

NeurIPS 2017 Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction Kristofer Bouchard, Alejandro Bujan, Fred Roosta, Shashanka Ubaru, Mr. Prabhat, Antoine Snijders, Jian-Hua Mao, Edward Chang, Michael W. Mahoney, Sharmodeep Bhattacharya

NeurIPS 2016 Sub-Sampled Newton Methods with Non-Uniform Sampling Peng Xu, Jiyan Yang, Fred Roosta, Christopher Ré, Michael W. Mahoney