Alessandro Rudi

ORCID

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 104

Top Venues

Publications

Adrien Vacher, Boris Muzellec, Francis R. Bach, François-Xavier Vialard, Alessandro Rudi
Optimal Estimation of Smooth Transport Maps with Kernel SoS. SIAM J. Math. Data Sci. 6 (2) (2024)
Pierre-Cyril Aubin-Frankowski, Yohann De Castro, Axel Parmentier, Alessandro Rudi
Generalization Bounds of Surrogate Policies for Combinatorial Optimization Problems. CoRR (2024)
Théophile Cantelobre, Carlo Ciliberto, Benjamin Guedj, Alessandro Rudi
Closed-form Filtering for Non-linear Systems. CoRR (2024)
Pierre Boudart, Alessandro Rudi, Pierre Gaillard
Structured Prediction in Online Learning. CoRR (2024)
Ulysse Marteau-Ferey, Francis R. Bach, Alessandro Rudi
Second Order Conditions to Decompose Smooth Functions as Sums of Squares. SIAM J. Optim. 34 (1) (2024)
Gaspard Beugnot, Julien Mairal, Alessandro Rudi
GloptiNets: Scalable Non-Convex Optimization with Certificates. CoRR (2023)
Francis R. Bach, Alessandro Rudi
Exponential Convergence of Sum-of-Squares Hierarchies for Trigonometric Polynomials. SIAM J. Optim. 33 (3) (2023)
Pierre-Cyril Aubin-Frankowski, Alessandro Rudi
Approximation of optimization problems with constraints through kernel Sum-Of-Squares. CoRR (2023)
Anant Raj, Umut Simsekli, Alessandro Rudi
Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models. CoRR (2023)
Riccardo Bonalli, Alessandro Rudi
Non-Parametric Learning of Stochastic Differential Equations with Fast Rates of Convergence. CoRR (2023)
Anant Raj, Umut Simsekli, Alessandro Rudi
Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models. NeurIPS (2023)
Gaspard Beugnot, Julien Mairal, Alessandro Rudi
GloptiNets: Scalable Non-Convex Optimization with Certificates. NeurIPS (2023)
Blake E. Woodworth, Francis R. Bach, Alessandro Rudi
Non-Convex Optimization with Certificates and Fast Rates Through Kernel Sums of Squares. CoRR (2022)
Théophile Cantelobre, Carlo Ciliberto, Benjamin Guedj, Alessandro Rudi
Measuring dissimilarity with diffeomorphism invariance. CoRR (2022)
Luc Brogat-Motte, Alessandro Rudi, Céline Brouard, Juho Rousu, Florence d'Alché-Buc
Vector-Valued Least-Squares Regression under Output Regularity Assumptions. CoRR (2022)
Blake E. Woodworth, Francis R. Bach, Alessandro Rudi
Non-Convex Optimization with Certificates and Fast Rates Through Kernel Sums of Squares. COLT (2022)
Théophile Cantelobre, Carlo Ciliberto, Benjamin Guedj, Alessandro Rudi
Measuring dissimilarity with diffeomorphism invariance. ICML (2022)
Vivien Cabannes, Francis R. Bach, Vianney Perchet, Alessandro Rudi
Active Labeling: Streaming Stochastic Gradients. CoRR (2022)
Ulysse Marteau-Ferey, Francis R. Bach, Alessandro Rudi
Sampling from Arbitrary Functions via PSD Models. AISTATS (2022)
Antoine Chatalic, Nicolas Schreuder, Lorenzo Rosasco, Alessandro Rudi
Nyström Kernel Mean Embeddings. ICML (2022)
Gaspard Beugnot, Julien Mairal, Alessandro Rudi
On the Benefits of Large Learning Rates for Kernel Methods. COLT (2022)
Luc Brogat-Motte, Alessandro Rudi, Céline Brouard, Juho Rousu, Florence d'Alché-Buc
Vector-Valued Least-Squares Regression under Output Regularity Assumptions. J. Mach. Learn. Res. 23 (2022)
Vivien Cabannes, Francis R. Bach, Vianney Perchet, Alessandro Rudi
Active Labeling: Streaming Stochastic Gradients. NeurIPS (2022)
Antoine Chatalic, Nicolas Schreuder, Alessandro Rudi, Lorenzo Rosasco
Nyström Kernel Mean Embeddings. CoRR (2022)
Eloïse Berthier, Justin Carpentier, Alessandro Rudi, Francis R. Bach
Infinite-Dimensional Sums-of-Squares for Optimal Control. CDC (2022)
Alex Nowak, Alessandro Rudi, Francis R. Bach
On the Consistency of Max-Margin Losses. AISTATS (2022)
Gaspard Beugnot, Julien Mairal, Alessandro Rudi
On the Benefits of Large Learning Rates for Kernel Methods. CoRR (2022)
Gaspard Beugnot, Julien Mairal, Alessandro Rudi
Beyond Tikhonov: faster learning with self-concordant losses, via iterative regularization. NeurIPS (2021)
Vivien Cabannes, Alessandro Rudi, Francis R. Bach
Fast rates in structured prediction. CoRR (2021)
Vivien Cabannes, Loucas Pillaud-Vivien, Francis R. Bach, Alessandro Rudi
Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning. NeurIPS (2021)
Vivien Cabannes, Francis R. Bach, Alessandro Rudi
Disambiguation of weak supervision with exponential convergence rates. CoRR (2021)
Vivien A. Cabannes, Francis R. Bach, Alessandro Rudi
Disambiguation of Weak Supervision leading to Exponential Convergence rates. ICML (2021)
Oleksandr Zadorozhnyi, Pierre Gaillard, Sébastien Gerchinovitz, Alessandro Rudi
Online nonparametric regression with Sobolev kernels. CoRR (2021)
Ulysse Marteau-Ferey, Francis R. Bach, Alessandro Rudi
Sampling from Arbitrary Functions via PSD Models. CoRR (2021)
Boris Muzellec, Adrien Vacher, Francis R. Bach, François-Xavier Vialard, Alessandro Rudi
Near-optimal estimation of smooth transport maps with kernel sums-of-squares. CoRR (2021)
Vivien A. Cabannes, Francis R. Bach, Alessandro Rudi
Fast Rates for Structured Prediction. COLT (2021)
Boris Muzellec, Francis R. Bach, Alessandro Rudi
A Note on Optimizing Distributions using Kernel Mean Embeddings. CoRR (2021)
Alessandro Rudi, Carlo Ciliberto
PSD Representations for Effective Probability Models. NeurIPS (2021)
Rémi Jézéquel, Pierre Gaillard, Alessandro Rudi
Mixability made efficient: Fast online multiclass logistic regression. CoRR (2021)
Rémi Jézéquel, Pierre Gaillard, Alessandro Rudi
Mixability made efficient: Fast online multiclass logistic regression. NeurIPS (2021)
Gaspard Beugnot, Julien Mairal, Alessandro Rudi
Beyond Tikhonov: Faster Learning with Self-Concordant Losses via Iterative Regularization. CoRR (2021)
Alessandro Rudi, Carlo Ciliberto
PSD Representations for Effective Probability Models. CoRR (2021)
Boris Muzellec, Francis R. Bach, Alessandro Rudi
Learning PSD-valued functions using kernel sums-of-squares. CoRR (2021)
Adrien Vacher, Boris Muzellec, Alessandro Rudi, Francis R. Bach, François-Xavier Vialard
A Dimension-free Computational Upper-bound for Smooth Optimal Transport Estimation. COLT (2021)
Alex Nowak-Vila, Alessandro Rudi, Francis R. Bach
Max-Margin is Dead, Long Live Max-Margin! CoRR (2021)
Rémi Jézéquel, Pierre Gaillard, Alessandro Rudi
Efficient improper learning for online logistic regression. CoRR (2020)
Carlo Ciliberto, Lorenzo Rosasco, Alessandro Rudi
A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings. CoRR (2020)
Nicholas Sterge, Bharath Sriperumbudur, Lorenzo Rosasco, Alessandro Rudi
Gain with no Pain: Efficiency of Kernel-PCA by Nyström Sampling. AISTATS (2020)
Ulysse Marteau-Ferey, Francis R. Bach, Alessandro Rudi
Non-parametric Models for Non-negative Functions. CoRR (2020)
Alex Nowak, Francis R. Bach, Alessandro Rudi
Consistent Structured Prediction with Max-Min Margin Markov Networks. ICML (2020)
Vivien Cabannes, Alessandro Rudi, Francis R. Bach
Structured Prediction with Partial Labelling through the Infimum Loss. CoRR (2020)
Alessandro Rudi, Ulysse Marteau-Ferey, Francis R. Bach
Finding Global Minima via Kernel Approximations. CoRR (2020)
Vivien Cabannes, Alessandro Rudi, Francis R. Bach
Structured Prediction with Partial Labelling through the Infimum Loss. ICML (2020)
Carlo Ciliberto, Andrea Rocchetto, Alessandro Rudi, Leonard Wossnig
Fast quantum learning with statistical guarantees. CoRR (2020)
Alessandro Rudi, Leonard Wossnig, Carlo Ciliberto, Andrea Rocchetto, Massimiliano Pontil, Simone Severini
Approximating Hamiltonian dynamics with the Nyström method. Quantum 4 (2020)
Ulysse Marteau-Ferey, Francis R. Bach, Alessandro Rudi
Non-parametric Models for Non-negative Functions. NeurIPS (2020)
Thomas Eboli, Alex Nowak-Vila, Jian Sun, Francis R. Bach, Jean Ponce, Alessandro Rudi
Structured and Localized Image Restoration. CoRR (2020)
Xuefei Lu, Alessandro Rudi, Emanuele Borgonovo, Lorenzo Rosasco
Faster Kriging: Facing High-Dimensional Simulators. Oper. Res. 68 (1) (2020)
Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro Rudi
Kernel methods through the roof: handling billions of points efficiently. CoRR (2020)
Alex Nowak-Vila, Francis R. Bach, Alessandro Rudi
Consistent Structured Prediction with Max-Min Margin Markov Networks. CoRR (2020)
Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro Rudi
Kernel Methods Through the Roof: Handling Billions of Points Efficiently. NeurIPS (2020)
Luc Brogat-Motte, Alessandro Rudi, Céline Brouard, Juho Rousu, Florence d'Alché-Buc
Learning Output Embeddings in Structured Prediction. CoRR (2020)
Nicolò Pagliana, Alessandro Rudi, Ernesto De Vito, Lorenzo Rosasco
Interpolation and Learning with Scale Dependent Kernels. CoRR (2020)
Carlo Ciliberto, Lorenzo Rosasco, Alessandro Rudi
A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings. J. Mach. Learn. Res. 21 (2020)
Rémi Jézéquel, Pierre Gaillard, Alessandro Rudi
Efficient improper learning for online logistic regression. COLT (2020)
Dmitrii M. Ostrovskii, Alessandro Rudi
Affine Invariant Covariance Estimation for Heavy-Tailed Distributions. COLT (2019)
Ulysse Marteau-Ferey, Dmitrii Ostrovskii, Francis R. Bach, Alessandro Rudi
Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance. CoRR (2019)
Jason Altschuler, Francis R. Bach, Alessandro Rudi, Jonathan Niles-Weed
Massively scalable Sinkhorn distances via the Nyström method. NeurIPS (2019)
Ulysse Marteau-Ferey, Francis R. Bach, Alessandro Rudi
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses. NeurIPS (2019)
Rémi Jézéquel, Pierre Gaillard, Alessandro Rudi
Efficient online learning with kernels for adversarial large scale problems. NeurIPS (2019)
Alex Nowak-Vila, Francis R. Bach, Alessandro Rudi
A General Theory for Structured Prediction with Smooth Convex Surrogates. CoRR (2019)
Ulysse Marteau-Ferey, Dmitrii Ostrovskii, Francis R. Bach, Alessandro Rudi
Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance. COLT (2019)
Carlo Ciliberto, Francis R. Bach, Alessandro Rudi
Localized Structured Prediction. NeurIPS (2019)
Rémi Jézéquel, Pierre Gaillard, Alessandro Rudi
Efficient online learning with kernels for adversarial large scale problems. CoRR (2019)
Ulysse Marteau-Ferey, Francis R. Bach, Alessandro Rudi
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses. CoRR (2019)
Alex Nowak-Vila, Francis R. Bach, Alessandro Rudi
Sharp Analysis of Learning with Discrete Losses. AISTATS (2019)
Nicholas Sterge, Bharath Sriperumbudur, Lorenzo Rosasco, Alessandro Rudi
Gain with no Pain: Efficient Kernel-PCA by Nyström Sampling. CoRR (2019)
Alessandro Rudi, Carlo Ciliberto, Gian Maria Marconi, Lorenzo Rosasco
Manifold Structured Prediction. CoRR (2018)
Luigi Carratino, Alessandro Rudi, Lorenzo Rosasco
Learning with SGD and Random Features. NeurIPS (2018)
Alessandro Rudi, Carlo Ciliberto, Gian Maria Marconi, Lorenzo Rosasco
Manifold Structured Prediction. NeurIPS (2018)
Loucas Pillaud-Vivien, Alessandro Rudi, Francis R. Bach
Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes. NeurIPS (2018)
Loucas Pillaud-Vivien, Alessandro Rudi, Francis R. Bach
Exponential Convergence of Testing Error for Stochastic Gradient Methods. COLT (2018)
Jason Altschuler, Francis R. Bach, Alessandro Rudi, Jonathan Weed
Massively scalable Sinkhorn distances via the Nyström method. CoRR (2018)
Alex Nowak-Vila, Francis R. Bach, Alessandro Rudi
Sharp Analysis of Learning with Discrete Losses. CoRR (2018)
Giulia Luise, Alessandro Rudi, Massimiliano Pontil, Carlo Ciliberto
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance. NeurIPS (2018)
Alessandro Rudi, Leonard Wossnig, Carlo Ciliberto, Andrea Rocchetto, Massimiliano Pontil, Simone Severini
Approximating Hamiltonian dynamics with the Nyström method. CoRR (2018)
Jason Altschuler, Francis R. Bach, Alessandro Rudi, Jonathan Weed
Approximating the Quadratic Transportation Metric in Near-Linear Time. CoRR (2018)
Loucas Pillaud-Vivien, Alessandro Rudi, Francis R. Bach
Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes. CoRR (2018)
Giulia Luise, Alessandro Rudi, Massimiliano Pontil, Carlo Ciliberto
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance. CoRR (2018)
Luigi Carratino, Alessandro Rudi, Lorenzo Rosasco
Learning with SGD and Random Features. CoRR (2018)
Alessandro Rudi, Daniele Calandriello, Luigi Carratino, Lorenzo Rosasco
On Fast Leverage Score Sampling and Optimal Learning. CoRR (2018)
Carlo Ciliberto, Francis R. Bach, Alessandro Rudi
Localized Structured Prediction. CoRR (2018)
Alessandro Rudi, Daniele Calandriello, Luigi Carratino, Lorenzo Rosasco
On Fast Leverage Score Sampling and Optimal Learning. NeurIPS (2018)
Loucas Pillaud-Vivien, Alessandro Rudi, Francis R. Bach
Exponential convergence of testing error for stochastic gradient methods. CoRR (2017)
Alessandro Rudi, Lorenzo Rosasco
Generalization Properties of Learning with Random Features. NIPS (2017)
Alessandro Rudi, Ernesto De Vito, Alessandro Verri, Francesca Odone
Regularized Kernel Algorithms for Support Estimation. Frontiers Appl. Math. Stat. 3 (2017)
Alessandro Rudi, Luigi Carratino, Lorenzo Rosasco
FALKON: An Optimal Large Scale Kernel Method. NIPS (2017)
Carlo Ciliberto, Alessandro Rudi, Lorenzo Rosasco, Massimiliano Pontil
Consistent Multitask Learning with Nonlinear Output Relations. NIPS (2017)
Alessandro Rudi, Luigi Carratino, Lorenzo Rosasco
FALKON: An Optimal Large Scale Kernel Method. CoRR (2017)
Carlo Ciliberto, Alessandro Rudi, Lorenzo Rosasco, Massimiliano Pontil
Consistent Multitask Learning with Nonlinear Output Relations. CoRR (2017)

Alessandro Rudi.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Alessandro Rudi