Fred Roosta

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 35

Top Topics

Gaussian Processes

Top Venues

J. Mach. Learn. Res.

Publications

Hossein Askari, Fred Roosta, Hongfu Sun
Bi-level Guided Diffusion Models for Zero-Shot Medical Imaging Inverse Problems. CoRR (2024)
Ali Eshragh, Luke Yerbury, Asef Nazari, Fred Roosta, Michael W. Mahoney
SALSA: Sequential Approximate Leverage-Score Algorithm with Application in Analyzing Big Time Series Data. CoRR (2024)
Alexander Lim, Yang Liu, Fred Roosta
Conjugate Direction Methods Under Inconsistent Systems. CoRR (2024)
Eslam Zaher, Maciej Trzaskowski, Quan Nguyen, Fred Roosta
Manifold Integrated Gradients: Riemannian Geometry for Feature Attribution. CoRR (2024)
Albert S. Berahas, Lindon Roberts, Fred Roosta
Non-Uniform Smoothness for Gradient Descent. Trans. Mach. Learn. Res. 2024 (2024)
Liam Hodgkinson, Christopher van der Heide, Robert Salomone, Fred Roosta, Michael W. Mahoney
The Interpolating Information Criterion for Overparameterized Models. CoRR (2023)
Liam Hodgkinson, Christopher van der Heide, Robert Salomone, Fred Roosta, Michael W. Mahoney
A PAC-Bayesian Perspective on the Interpolating Information Criterion. CoRR (2023)
Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes. ICML (2023)
Alexander Lim, Fred Roosta
Complexity Guarantees for Nonconvex Newton-MR Under Inexact Hessian Information. CoRR (2023)
Yang Liu, Andre Milzarek, Fred Roosta
Obtaining Pseudo-inverse Solutions With MINRES. CoRR (2023)
Albert S. Berahas, Lindon Roberts, Fred Roosta
Non-Uniform Smoothness for Gradient Descent. CoRR (2023)
Dung Nguyen, Yan Zhao, Yifan Zhang, Anh Ngoc-Lan Huynh, Fred Roosta, Graeme L. Hammer, Scott C. Chapman, Andries B. Potgieter
Crop Type Prediction Utilising a Long Short-Term Memory with a Self-Attention for Winter Crops in Australia. IGARSS (2022)
Yang Liu, Fred Roosta
MINRES: From Negative Curvature Detection to Monotonicity Properties. SIAM J. Optim. 32 (4) (2022)
Fred Roosta, Yang Liu, Peng Xu, Michael W. Mahoney
Newton-MR: Inexact Newton Method with minimum residual sub-problem solver. EURO J. Comput. Optim. 10 (2022)
Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes. CoRR (2022)
Ali Eshragh, Fred Roosta, Asef Nazari, Michael W. Mahoney
LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data. J. Mach. Learn. Res. 23 (2022)
Liam Hodgkinson, Robert Salomone, Fred Roosta
Implicit Langevin Algorithms for Sampling From Log-concave Densities. J. Mach. Learn. Res. 22 (2021)
Christopher van der Heide, Fred Roosta, Liam Hodgkinson, Dirk Kroese
Shadow Manifold Hamiltonian Monte Carlo. AISTATS (2021)
Zhewei Yao, Peng Xu, Fred Roosta, Michael W. Mahoney
Inexact Nonconvex Newton-Type Methods. INFORMS J. Optim. 3 (2) (2021)
Keith D. Levin, Fred Roosta, Minh Tang, Michael W. Mahoney, Carey E. Priebe
Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings. J. Mach. Learn. Res. 22 (2021)
Zhili Feng, Fred Roosta, David P. Woodruff
Non-PSD matrix sketching with applications to regression and optimization. UAI (2021)
Yang Liu, Fred Roosta
Convergence of Newton-MR under Inexact Hessian Information. SIAM J. Optim. 31 (1) (2021)
Russell Tsuchida, Tim Pearce, Christopher van der Heide, Fred Roosta, Marcus Gallagher
Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks. AAAI (2021)
Zhili Feng, Fred Roosta, David P. Woodruff
Non-PSD Matrix Sketching with Applications to Regression and Optimization. CoRR (2021)
Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney
Stochastic continuous normalizing flows: training SDEs as ODEs. UAI (2021)
Vektor Dewanto, George Dunn, Ali Eshragh, Marcus Gallagher, Fred Roosta
Average-reward model-free reinforcement learning: a systematic review and literature mapping. CoRR (2020)
Russell Tsuchida, Tim Pearce, Christopher van der Heide, Fred Roosta, Marcus Gallagher
Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks. CoRR (2020)
Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney
Stochastic Normalizing Flows. CoRR (2020)
Chih-Hao Fang, Sudhir B. Kylasa, Fred Roosta, Michael W. Mahoney, Ananth Grama
Newton-ADMM: a distributed GPU-accelerated optimizer for multiclass classification problems. SC (2020)
Peng Xu, Fred Roosta, Michael W. Mahoney
Second-order Optimization for Non-convex Machine Learning: an Empirical Study. SDM (2020)
Rixon Crane, Fred Roosta
DINO: Distributed Newton-Type Optimization Method. ICML (2020)
Peng Xu, Fred Roosta, Michael W. Mahoney
Newton-type methods for non-convex optimization under inexact Hessian information. Math. Program. 184 (1) (2020)
Rixon Crane, Fred Roosta
DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization. NeurIPS (2019)
Keith D. Levin, Fred Roosta, Minh Tang, Michael W. Mahoney, Carey E. Priebe
Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings. CoRR (2019)
Russell Tsuchida, Fred Roosta, Marcus Gallagher
Richer priors for infinitely wide multi-layer perceptrons. CoRR (2019)