Fred Roosta
22 papers · 2016–2025 · 8 venues · across top CS/AI venues
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Machine Learning
(21)
Optimization & Theory Β· Machine Learning
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Mathematics & Optimization
(13)
Optimization
(11)
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(7)
Core Methods
(6)
Artificial Intelligence
(5)
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Keywords
communication efficiency
(3)
newton method
(3)
distributed optimization
(3)
importance sampling
(2)
dimensionality reduction
(2)
markov chain monte carlo
(2)
newton-type method
(2)
gaussian process
(2)
neural tangent kernel
(2)
convex optimization
(2)
out-of-sample extension
(2)
stochastic differential equation
(2)
random dot product graph
(2)
graph analysis
(1)
uncertainty quantification
(1)
stochastic gradient descent
(1)
density estimation
(1)
feature extraction
(1)
double descent
(1)
feature attribution
(1)
Papers
Determinant Estimation under Memory Constraints and Neural Scaling Laws
ICML 2025
Importance Sampling for Nonlinear Models
ICML 2025
Training-Free Medical Image Inverses via Bi-Level Guided Diffusion Models
WACV 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
Implicit Langevin Algorithms for Sampling From Log-concave Densities
JMLR 2021
Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings
JMLR 2021
Non-PSD matrix sketching with applications to regression and optimization
UAI 2021
Shadow Manifold Hamiltonian Monte Carlo
AISTATS 2021
Stochastic continuous normalizing flows: training SDEs as ODEs
UAI 2021
DINO: Distributed Newton-Type Optimization Method
ICML 2020
DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization
NeurIPS 2019
Exchangeability and Kernel Invariance in Trained MLPs
IJCAI 2019
FLAG nβ FLARE: Fast Linearly-Coupled Adaptive Gradient Methods
AISTATS 2018
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization
NeurIPS 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
NeurIPS 2017
Sub-sampled Newton Methods with Non-uniform Sampling
NeurIPS 2016