← Authors

Fred Roosta

22 papers · 2016–2025 · 8 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (12) πŸ—ΊοΈ Taxonomy Completionist (38) 🌈 Renaissance Researcher (7) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (8) πŸƒ Academic Marathon (9) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸ† Keyword Champion (2) πŸ—ƒοΈ Keyword Collector (98) πŸ’Ž Century Club (22) πŸ”₯ Unstoppable (10) ⚑ Prolific Year (6)

Conferences

ICML (8) NIPS (4) JMLR (3) AISTATS (2) UAI (2) AAAI (1) IJCAI (1) WACV (1)

Top co-authors

Michael W. Mahoney (8) Liam Hodgkinson (5) Chris van der Heide (5) Marcus Gallagher (3) Russell Tsuchida (3) Rixon Crane (2) Michael Mahoney (2) Peng Xu (2) Hongfu Sun (1) Eslam Zaher (1)

Keywords

distributed optimization (3) communication efficiency (3) newton method (3) gaussian process (2) dimensionality reduction (2) random dot product graph (2) stochastic differential equation (2) markov chain monte carlo (2) out-of-sample extension (2) convex optimization (2) newton-type method (2) uncertainty quantification (1) stochastic gradient descent (1) graph analysis (1) density estimation (1) feature extraction (1) double descent (1) neural network analysis (1) non-convex optimization (1) neural tangent kernel (1)

Papers

Determinant Estimation under Memory Constraints and Neural Scaling Laws ICML 2025
Training-Free Medical Image Inverses via Bi-Level Guided Diffusion Models WACV 2025
Importance Sampling for Nonlinear Models ICML 2025
Inexact Newton-type Methods for Optimisation with Nonnegativity Constraints ICML 2024
Manifold Integrated Gradients: Riemannian Geometry for Feature Attribution ICML 2024
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes ICML 2023
LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data JMLR 2022
Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks AAAI 2021
Non-PSD matrix sketching with applications to regression and optimization UAI 2021
Stochastic continuous normalizing flows: training SDEs as ODEs UAI 2021
Implicit Langevin Algorithms for Sampling From Log-concave Densities JMLR 2021
Shadow Manifold Hamiltonian Monte Carlo AISTATS 2021
Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings JMLR 2021
DINO: Distributed Newton-Type Optimization Method ICML 2020
DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization NIPS 2019
Exchangeability and Kernel Invariance in Trained MLPs IJCAI 2019
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization NIPS 2018
FLAG n’ FLARE: Fast Linearly-Coupled Adaptive Gradient Methods AISTATS 2018
Invariance of Weight Distributions in Rectified MLPs ICML 2018
Out-of-sample extension of graph adjacency spectral embedding ICML 2018
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction NIPS 2017
Sub-sampled Newton Methods with Non-uniform Sampling NIPS 2016