Alessandro Rudi

papers

246

total citations

papers (11)

Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning

NEURIPS 2021arXiv

citations

Mixability made efficient: Fast online multiclass logistic regression

NEURIPS 2021arXiv

citations

Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models

NEURIPS 2023arXiv

citations

Dynamic Regret Reduces to Kernelized Static Regret

NEURIPS 2025arXiv

citations

Active Labeling: Streaming Stochastic Gradients

NEURIPS 2022arXiv

citations

Beyond Tikhonov: faster learning with self-concordant losses, via iterative regularization

NEURIPS 2021arXiv

citations

GloptiNets: Scalable Non-Convex Optimization with Certificates

NEURIPS 2023arXiv

citations

Safely Learning Controlled Stochastic Dynamics

NEURIPS 2025arXiv

citations

Alessandro Rudi

papers (11)

Kernel Methods Through the Roof: Handling Billions of Points Efficiently

Non-parametric Models for Non-negative Functions

PSD Representations for Effective Probability Models

Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning

Mixability made efficient: Fast online multiclass logistic regression

Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models

Dynamic Regret Reduces to Kernelized Static Regret

Active Labeling: Streaming Stochastic Gradients

Beyond Tikhonov: faster learning with self-concordant losses, via iterative regularization

GloptiNets: Scalable Non-Convex Optimization with Certificates

Safely Learning Controlled Stochastic Dynamics

papers (11)

Kernel Methods Through the Roof: Handling Billions of Points Efficiently

Non-parametric Models for Non-negative Functions

PSD Representations for Effective Probability Models

Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning

Mixability made efficient: Fast online multiclass logistic regression

Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models

Dynamic Regret Reduces to Kernelized Static Regret

Active Labeling: Streaming Stochastic Gradients

Beyond Tikhonov: faster learning with self-concordant losses, via iterative regularization

GloptiNets: Scalable Non-Convex Optimization with Certificates

Safely Learning Controlled Stochastic Dynamics