Fred (Farbod) Roosta

Orcid: 0000-0002-6920-7072

Affiliations:
  • University of Queensland, School of Mathematics and Physics, Brisbane, Australia
  • UC Berkeley, International Computer Science Institute, CA, USA


According to our database1, Fred (Farbod) Roosta authored at least 53 papers between 2013 and 2024.

Collaborative distances:

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Non-Uniform Smoothness for Gradient Descent.
Trans. Mach. Learn. Res., 2024

Bi-level Guided Diffusion Models for Zero-Shot Medical Imaging Inverse Problems.
CoRR, 2024

Conjugate Direction Methods Under Inconsistent Systems.
CoRR, 2024

SALSA: Sequential Approximate Leverage-Score Algorithm with Application in Analyzing Big Time Series Data.
CoRR, 2024

Manifold Integrated Gradients: Riemannian Geometry for Feature Attribution.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Inexact Newton-type Methods for Optimisation with Nonnegativity Constraints.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
A PAC-Bayesian Perspective on the Interpolating Information Criterion.
CoRR, 2023

Obtaining Pseudo-inverse Solutions With MINRES.
CoRR, 2023

Complexity Guarantees for Nonconvex Newton-MR Under Inexact Hessian Information.
CoRR, 2023

The Interpolating Information Criterion for Overparameterized Models.
CoRR, 2023

Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes.
Proceedings of the International Conference on Machine Learning, 2023

2022
MINRES: From Negative Curvature Detection to Monotonicity Properties.
SIAM J. Optim., December, 2022

LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data.
J. Mach. Learn. Res., 2022

Newton-MR: Inexact Newton Method with minimum residual sub-problem solver.
EURO J. Comput. Optim., 2022

Crop Type Prediction Utilising a Long Short-Term Memory with a Self-Attention for Winter Crops in Australia.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2022

2021
Inexact Nonconvex Newton-Type Methods.
INFORMS J. Optim., January, 2021

Convergence of Newton-MR under Inexact Hessian Information.
SIAM J. Optim., 2021

Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings.
J. Mach. Learn. Res., 2021

Implicit Langevin Algorithms for Sampling From Log-concave Densities.
J. Mach. Learn. Res., 2021

Stochastic continuous normalizing flows: training SDEs as ODEs.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Non-PSD matrix sketching with applications to regression and optimization.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Shadow Manifold Hamiltonian Monte Carlo.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Newton-type methods for non-convex optimization under inexact Hessian information.
Math. Program., 2020

Average-reward model-free reinforcement learning: a systematic review and literature mapping.
CoRR, 2020

Stochastic Normalizing Flows.
CoRR, 2020

Second-order Optimization for Non-convex Machine Learning: an Empirical Study.
Proceedings of the 2020 SIAM International Conference on Data Mining, 2020

Newton-ADMM: a distributed GPU-accelerated optimizer for multiclass classification problems.
Proceedings of the International Conference for High Performance Computing, 2020

DINO: Distributed Newton-Type Optimization Method.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Sub-sampled Newton methods.
Math. Program., 2019

Variational perspective on local graph clustering.
Math. Program., 2019

Richer priors for infinitely wide multi-layer perceptrons.
CoRR, 2019

GPU Accelerated Sub-Sampled Newton's Method for Convex Classification Problems.
Proceedings of the 2019 SIAM International Conference on Data Mining, 2019

DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Exchangeability and Kernel Invariance in Trained MLPs.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

2018
Newton-MR: Newton's Method Without Smoothness or Convexity.
CoRR, 2018

Distributed Second-order Convex Optimization.
CoRR, 2018

GPU Accelerated Sub-Sampled Newton\textsf{'}s Method.
CoRR, 2018

GIANT: Globally Improved Approximate Newton Method for Distributed Optimization.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Invariance of Weight Distributions in Rectified MLPs.
Proceedings of the 35th International Conference on Machine Learning, 2018

Out-of-sample extension of graph adjacency spectral embedding.
Proceedings of the 35th International Conference on Machine Learning, 2018

FLAG n' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016
Parallel Local Graph Clustering.
Proc. VLDB Endow., 2016

Sub-Sampled Newton Methods II: Local Convergence Rates.
CoRR, 2016

Sub-Sampled Newton Methods I: Globally Convergent Algorithms.
CoRR, 2016

FLAG: Fast Linearly-Coupled Adaptive Gradient Method.
CoRR, 2016

Sub-sampled Newton Methods with Non-uniform Sampling.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015
Assessing Stochastic Algorithms for Large Scale Nonlinear Least Squares Problems Using Extremal Probabilities of Linear Combinations of Gamma Random Variables.
SIAM/ASA J. Uncertain. Quantification, 2015

Improved Bounds on Sample Size for Implicit Matrix Trace Estimators.
Found. Comput. Math., 2015

2014
Stochastic Algorithms for Inverse Problems Involving PDEs and many Measurements.
SIAM J. Sci. Comput., 2014

Algorithms that satisfy a stopping criterion, probably.
CoRR, 2014

2013
Data completion and stochastic algorithms for PDE inversion problems with many measurements.
CoRR, 2013


  Loading...