Alessandro Rudi

Orcid: 0000-0002-3879-7794

According to our database1, Alessandro Rudi authored at least 73 papers between 2010 and 2024.

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Bibliography

2024
Second Order Conditions to Decompose Smooth Functions as Sums of Squares.
SIAM J. Optim., March, 2024

Closed-form Filtering for Non-linear Systems.
CoRR, 2024

2023
Exponential Convergence of Sum-of-Squares Hierarchies for Trigonometric Polynomials.
SIAM J. Optim., September, 2023

Non-Parametric Learning of Stochastic Differential Equations with Fast Rates of Convergence.
CoRR, 2023

Approximation of optimization problems with constraints through kernel Sum-Of-Squares.
CoRR, 2023

Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

GloptiNets: Scalable Non-Convex Optimization with Certificates.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
Vector-Valued Least-Squares Regression under Output Regularity Assumptions.
J. Mach. Learn. Res., 2022

Active Labeling: Streaming Stochastic Gradients.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Nyström Kernel Mean Embeddings.
Proceedings of the International Conference on Machine Learning, 2022

Measuring dissimilarity with diffeomorphism invariance.
Proceedings of the International Conference on Machine Learning, 2022

Non-Convex Optimization with Certificates and Fast Rates Through Kernel Sums of Squares.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

On the Benefits of Large Learning Rates for Kernel Methods.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Infinite-Dimensional Sums-of-Squares for Optimal Control.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022

On the Consistency of Max-Margin Losses.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Sampling from Arbitrary Functions via PSD Models.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Near-optimal estimation of smooth transport maps with kernel sums-of-squares.
CoRR, 2021

Learning PSD-valued functions using kernel sums-of-squares.
CoRR, 2021

A Note on Optimizing Distributions using Kernel Mean Embeddings.
CoRR, 2021

Max-Margin is Dead, Long Live Max-Margin!
CoRR, 2021

Online nonparametric regression with Sobolev kernels.
CoRR, 2021

Disambiguation of weak supervision with exponential convergence rates.
CoRR, 2021

PSD Representations for Effective Probability Models.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Mixability made efficient: Fast online multiclass logistic regression.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Beyond Tikhonov: faster learning with self-concordant losses, via iterative regularization.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Disambiguation of Weak Supervision leading to Exponential Convergence rates.
Proceedings of the 38th International Conference on Machine Learning, 2021

A Dimension-free Computational Upper-bound for Smooth Optimal Transport Estimation.
Proceedings of the Conference on Learning Theory, 2021

Fast Rates for Structured Prediction.
Proceedings of the Conference on Learning Theory, 2021

2020
Approximating Hamiltonian dynamics with the Nyström method.
Quantum, 2020

A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings.
J. Mach. Learn. Res., 2020

Faster Kriging: Facing High-Dimensional Simulators.
Oper. Res., 2020

Finding Global Minima via Kernel Approximations.
CoRR, 2020

Learning Output Embeddings in Structured Prediction.
CoRR, 2020

Consistent Structured Prediction with Max-Min Margin Markov Networks.
CoRR, 2020

Structured and Localized Image Restoration.
CoRR, 2020

Fast quantum learning with statistical guarantees.
CoRR, 2020

Kernel Methods Through the Roof: Handling Billions of Points Efficiently.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Non-parametric Models for Non-negative Functions.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Consistent Structured Prediction with Max-Min Margin Markov Networks.
Proceedings of the 37th International Conference on Machine Learning, 2020

Structured Prediction with Partial Labelling through the Infimum Loss.
Proceedings of the 37th International Conference on Machine Learning, 2020

Efficient improper learning for online logistic regression.
Proceedings of the Conference on Learning Theory, 2020

Gain with no Pain: Efficiency of Kernel-PCA by Nyström Sampling.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Statistical Estimation of the Poincaré constant and Application to Sampling Multimodal Distributions.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Gain with no Pain: Efficient Kernel-PCA by Nyström Sampling.
CoRR, 2019

A General Theory for Structured Prediction with Smooth Convex Surrogates.
CoRR, 2019

Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Efficient online learning with kernels for adversarial large scale problems.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Localized Structured Prediction.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Massively scalable Sinkhorn distances via the Nyström method.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Affine Invariant Covariance Estimation for Heavy-Tailed Distributions.
Proceedings of the Conference on Learning Theory, 2019

Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance.
Proceedings of the Conference on Learning Theory, 2019

Sharp Analysis of Learning with Discrete Losses.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
Approximating the Quadratic Transportation Metric in Near-Linear Time.
CoRR, 2018

Manifold Structured Prediction.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

On Fast Leverage Score Sampling and Optimal Learning.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Learning with SGD and Random Features.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Exponential Convergence of Testing Error for Stochastic Gradient Methods.
Proceedings of the Conference On Learning Theory, 2018

2017
Regularized Kernel Algorithms for Support Estimation.
Frontiers Appl. Math. Stat., 2017

Generalization Properties of Learning with Random Features.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

FALKON: An Optimal Large Scale Kernel Method.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Consistent Multitask Learning with Nonlinear Output Relations.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016
Generalization Properties of Learning with Random Features.
CoRR, 2016

A Consistent Regularization Approach for Structured Prediction.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

NYTRO: When Subsampling Meets Early Stopping.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Less is More: Nyström Computational Regularization.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014
Geometrical and computational aspects of Spectral Support Estimation for novelty detection.
Pattern Recognit. Lett., 2014

2013
On the Sample Complexity of Subspace Learning.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2012
Adaptive Optimization for Cross Validation.
Proceedings of the 20th European Symposium on Artificial Neural Networks, 2012

2011
A general method for the point of regard estimation in 3D space.
Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition, 2011

2010
Linear Solvability in the Viewing Graph.
Proceedings of the Computer Vision - ACCV 2010, 2010


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