Fred Roosta - RankMe

48 papers A* 8A 6C 1Journal 33

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Counterfactual Explanations on Robust Perceptual Geodesics. CoRR Eslam Zaher, Maciej Trzaskowski, Quan Nguyen, Fred Roosta
2025	A*	`conf`	Determinant Estimation under Memory Constraints and Neural Scaling Laws. ICML Siavash Ameli, Chris van der Heide, Liam Hodgkinson, Fred Roosta, Michael W. Mahoney
2025	J	`jnl`	Determinant Estimation under Memory Constraints and Neural Scaling Laws. CoRR Siavash Ameli, Chris van der Heide, Liam Hodgkinson, Fred Roosta, Michael W. Mahoney
2025	J	`jnl`	Faithful-Newton Framework: Bridging Inner and Outer Solvers for Enhanced Optimization. CoRR Alexander Lim, Fred Roosta
2025	J	`jnl`	First-ish Order Methods: Hessian-aware Scalings of Gradient Descent. CoRR Oscar Smee, Fred Roosta, Stephen J. Wright
2025	A*	`conf`	Importance Sampling for Nonlinear Models. ICML Prakash Palanivelu Rajmohan, Fred Roosta
2025	J	`jnl`	Importance Sampling for Nonlinear Models. CoRR Prakash Palanivelu Rajmohan, Fred Roosta
2025	J	`jnl`	Latent Refinement via Flow Matching for Training-free Linear Inverse Problem Solving. CoRR Hossein Askari, Yadan Luo, Hongfu Sun, Fred Roosta
2025	J	`jnl`	Obtaining Pseudoinverse Solutions with MINRES. SIAM J. Matrix Anal. Appl. Yang Liu, Andre Milzarek, Fred Roosta
2025	A	`conf`	Training-free Medical Image Inverses via Bi-level Guided Diffusion Models. WACV Hossein Askari, Fred Roosta, Hongfu Sun
2025	J	`jnl`	Uncertainty Quantification with the Empirical Neural Tangent Kernel. CoRR Joseph Wilson, Chris van der Heide, Liam Hodgkinson, Fred Roosta
2024	J	`jnl`	Bi-level Guided Diffusion Models for Zero-Shot Medical Imaging Inverse Problems. CoRR Hossein Askari, Fred Roosta, Hongfu Sun
2024	J	`jnl`	Conjugate Direction Methods Under Inconsistent Systems. CoRR Alexander Lim, Yang Liu, Fred Roosta
2024	A*	`conf`	Inexact Newton-type Methods for Optimisation with Nonnegativity Constraints. ICML Oscar Smee, Fred Roosta
2024	A*	`conf`	Manifold Integrated Gradients: Riemannian Geometry for Feature Attribution. ICML Eslam Zaher, Maciej Trzaskowski, Quan Nguyen, Fred Roosta
2024	J	`jnl`	Manifold Integrated Gradients: Riemannian Geometry for Feature Attribution. CoRR Eslam Zaher, Maciej Trzaskowski, Quan Nguyen, Fred Roosta
2024	J	`jnl`	Non-Uniform Smoothness for Gradient Descent. Trans. Mach. Learn. Res. Albert S. Berahas, Lindon Roberts, Fred Roosta
2024	J	`jnl`	SALSA: Sequential Approximate Leverage-Score Algorithm with Application in Analyzing Big Time Series Data. CoRR Ali Eshragh, Luke Yerbury, Asef Nazari, Fred Roosta, Michael W. Mahoney
2023	J	`jnl`	A PAC-Bayesian Perspective on the Interpolating Information Criterion. CoRR Liam Hodgkinson, Christopher van der Heide, Robert Salomone, Fred Roosta, Michael W. Mahoney
2023	J	`jnl`	Complexity Guarantees for Nonconvex Newton-MR Under Inexact Hessian Information. CoRR Alexander Lim, Fred Roosta
2023	A*	`conf`	Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes. ICML Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney
2023	J	`jnl`	Non-Uniform Smoothness for Gradient Descent. CoRR Albert S. Berahas, Lindon Roberts, Fred Roosta
2023	J	`jnl`	Obtaining Pseudo-inverse Solutions With MINRES. CoRR Yang Liu, Andre Milzarek, Fred Roosta
2023	J	`jnl`	The Interpolating Information Criterion for Overparameterized Models. CoRR Liam Hodgkinson, Christopher van der Heide, Robert Salomone, Fred Roosta, Michael W. Mahoney
2022	C	`conf`	Crop Type Prediction Utilising a Long Short-Term Memory with a Self-Attention for Winter Crops in Australia. IGARSS Dung Nguyen, Yan Zhao, Yifan Zhang, Anh Ngoc-Lan Huynh, Fred Roosta, Graeme L. Hammer, Scott C. Chapman, Andries B. Potgieter
2022	J	`jnl`	LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data. J. Mach. Learn. Res. Ali Eshragh, Fred Roosta, Asef Nazari, Michael W. Mahoney
2022	J	`jnl`	MINRES: From Negative Curvature Detection to Monotonicity Properties. SIAM J. Optim. Yang Liu, Fred Roosta
2022	J	`jnl`	Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes. CoRR Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney
2022	J	`jnl`	Newton-MR: Inexact Newton Method with minimum residual sub-problem solver. EURO J. Comput. Optim. Fred Roosta, Yang Liu, Peng Xu, Michael W. Mahoney
2021	A*	`conf`	Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks. AAAI Russell Tsuchida, Tim Pearce, Christopher van der Heide, Fred Roosta, Marcus Gallagher
2021	J	`jnl`	Convergence of Newton-MR under Inexact Hessian Information. SIAM J. Optim. Yang Liu, Fred Roosta
2021	J	`jnl`	Implicit Langevin Algorithms for Sampling From Log-concave Densities. J. Mach. Learn. Res. Liam Hodgkinson, Robert Salomone, Fred Roosta
2021	J	`jnl`	Inexact Nonconvex Newton-Type Methods. INFORMS J. Optim. Zhewei Yao, Peng Xu, Fred Roosta, Michael W. Mahoney
2021	J	`jnl`	Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings. J. Mach. Learn. Res. Keith D. Levin, Fred Roosta, Minh Tang, Michael W. Mahoney, Carey E. Priebe
2021	J	`jnl`	Non-PSD Matrix Sketching with Applications to Regression and Optimization. CoRR Zhili Feng, Fred Roosta, David P. Woodruff
2021	A	`conf`	Non-PSD matrix sketching with applications to regression and optimization. UAI Zhili Feng, Fred Roosta, David P. Woodruff
2021	A	`conf`	Shadow Manifold Hamiltonian Monte Carlo. AISTATS Christopher van der Heide, Fred Roosta, Liam Hodgkinson, Dirk P. Kroese
2021	A	`conf`	Stochastic continuous normalizing flows: training SDEs as ODEs. UAI Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney
2020	J	`jnl`	Average-reward model-free reinforcement learning: a systematic review and literature mapping. CoRR Vektor Dewanto, George Dunn, Ali Eshragh, Marcus Gallagher, Fred Roosta
2020	J	`jnl`	Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks. CoRR Russell Tsuchida, Tim Pearce, Christopher van der Heide, Fred Roosta, Marcus Gallagher
2020	A*	`conf`	DINO: Distributed Newton-Type Optimization Method. ICML Rixon Crane, Fred Roosta
2020	A	`conf`	Newton-ADMM: a distributed GPU-accelerated optimizer for multiclass classification problems. SC Chih-Hao Fang, Sudhir B. Kylasa, Fred Roosta, Michael W. Mahoney, Ananth Grama
2020	J	`jnl`	Newton-type methods for non-convex optimization under inexact Hessian information. Math. Program. Peng Xu, Fred Roosta, Michael W. Mahoney
2020	A	`conf`	Second-order Optimization for Non-convex Machine Learning: an Empirical Study. SDM Peng Xu, Fred Roosta, Michael W. Mahoney
2020	J	`jnl`	Stochastic Normalizing Flows. CoRR Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney
2019	A*	`conf`	DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization. NeurIPS Rixon Crane, Fred Roosta
2019	J	`jnl`	Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings. CoRR Keith D. Levin, Fred Roosta, Minh Tang, Michael W. Mahoney, Carey E. Priebe
2019	J	`jnl`	Richer priors for infinitely wide multi-layer perceptrons. CoRR Russell Tsuchida, Fred Roosta, Marcus Gallagher