Linear Solvability in the Viewing Graph. - Alessandro Rudi; Matia Pizzoli; Fiora Pirri (ACCV (3), 2010)

A general method for the point of regard estimation in 3D space. - Fiora Pirri; Matia Pizzoli; Alessandro Rudi (CVPR, 2011)

Adaptive Optimization for Cross Validation. - Alessandro Rudi; Gabriele Chiusano; Alessandro Verri (ESANN, 2012)

On the Sample Complexity of Subspace Learning. - Alessandro Rudi; Guillermo D. Cañas; Lorenzo Rosasco (NIPS, 2013)

Geometrical and computational aspects of Spectral Support Estimation for novelty detection. - Alessandro Rudi; Francesca Odone; Ernesto De Vito (Pattern Recognit. Lett., 2014)

Less is More: Nyström Computational Regularization. - Alessandro Rudi; Raffaello Camoriano; Lorenzo Rosasco (NIPS, 2015)

NYTRO: When Subsampling Meets Early Stopping. - Raffaello Camoriano; Tomás Angles; Alessandro Rudi; Lorenzo Rosasco (AISTATS, 2016)

A Consistent Regularization Approach for Structured Prediction. - Carlo Ciliberto; Lorenzo Rosasco; Alessandro Rudi (NIPS, 2016)

Generalization Properties of Learning with Random Features. - Alessandro Rudi; Raffaello Camoriano; Lorenzo Rosasco (CoRR, 2016)

Consistent Multitask Learning with Nonlinear Output Relations. - Carlo Ciliberto; Alessandro Rudi; Lorenzo Rosasco; Massimiliano Pontil (NIPS, 2017)

FALKON: An Optimal Large Scale Kernel Method. - Alessandro Rudi; Luigi Carratino; Lorenzo Rosasco (NIPS, 2017)

Generalization Properties of Learning with Random Features. - Alessandro Rudi; Lorenzo Rosasco (NIPS, 2017)

Regularized Kernel Algorithms for Support Estimation. - Alessandro Rudi; Ernesto De Vito; Alessandro Verri; Francesca Odone (Frontiers Appl. Math. Stat., 2017)

Exponential Convergence of Testing Error for Stochastic Gradient Methods. - Loucas Pillaud-Vivien; Alessandro Rudi; Francis R. Bach (COLT, 2018)

Learning with SGD and Random Features. - Luigi Carratino; Alessandro Rudi; Lorenzo Rosasco (NeurIPS, 2018)

Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance. - Giulia Luise; Alessandro Rudi; Massimiliano Pontil; Carlo Ciliberto (NeurIPS, 2018)

Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes. - Loucas Pillaud-Vivien; Alessandro Rudi; Francis R. Bach (NeurIPS, 2018)

On Fast Leverage Score Sampling and Optimal Learning. - Alessandro Rudi; Daniele Calandriello; Luigi Carratino; Lorenzo Rosasco (NeurIPS, 2018)

Manifold Structured Prediction. - Alessandro Rudi; Carlo Ciliberto; Gian Maria Marconi; Lorenzo Rosasco (NeurIPS, 2018)

Approximating the Quadratic Transportation Metric in Near-Linear Time. - Jason M. Altschuler; Francis R. Bach; Alessandro Rudi; Jonathan Weed (CoRR, 2018)

Sharp Analysis of Learning with Discrete Losses. - Alex Nowak-Vila; Francis R. Bach; Alessandro Rudi (AISTATS, 2019)

Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance. - Ulysse Marteau-Ferey; Dmitrii Ostrovskii; Francis R. Bach; Alessandro Rudi (COLT, 2019)

Affine Invariant Covariance Estimation for Heavy-Tailed Distributions. - Dmitrii M. Ostrovskii; Alessandro Rudi (COLT, 2019)

Massively scalable Sinkhorn distances via the Nyström method. - Jason M. Altschuler; Francis R. Bach; Alessandro Rudi; Jonathan Niles-Weed (NeurIPS, 2019)

Localized Structured Prediction. - Carlo Ciliberto; Francis R. Bach; Alessandro Rudi (NeurIPS, 2019)

Efficient online learning with kernels for adversarial large scale problems. - Rémi Jézéquel; Pierre Gaillard; Alessandro Rudi (NeurIPS, 2019)

Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses. - Ulysse Marteau-Ferey; Francis R. Bach; Alessandro Rudi (NeurIPS, 2019)

A General Theory for Structured Prediction with Smooth Convex Surrogates. - Alex Nowak-Vila; Francis R. Bach; Alessandro Rudi (CoRR, 2019)

Gain with no Pain: Efficient Kernel-PCA by Nyström Sampling. - Nicholas Sterge; Bharath K. Sriperumbudur; Lorenzo Rosasco; Alessandro Rudi (CoRR, 2019)

Statistical Estimation of the Poincaré constant and Application to Sampling Multimodal Distributions. - Loucas Pillaud-Vivien; Francis R. Bach; Tony Lelièvre; Alessandro Rudi; Gabriel Stoltz (AISTATS, 2020)

Gain with no Pain: Efficiency of Kernel-PCA by Nyström Sampling. - Nicholas Sterge; Bharath K. Sriperumbudur; Lorenzo Rosasco; Alessandro Rudi (AISTATS, 2020)

Efficient improper learning for online logistic regression. - Rémi Jézéquel; Pierre Gaillard; Alessandro Rudi (COLT, 2020)

Structured Prediction with Partial Labelling through the Infimum Loss. - Vivien Cabannes; Alessandro Rudi; Francis R. Bach (ICML, 2020)

Consistent Structured Prediction with Max-Min Margin Markov Networks. - Alex Nowak; Francis R. Bach; Alessandro Rudi (ICML, 2020)

Non-parametric Models for Non-negative Functions. - Ulysse Marteau-Ferey; Francis R. Bach; Alessandro Rudi (NeurIPS, 2020)

Kernel Methods Through the Roof: Handling Billions of Points Efficiently. - Giacomo Meanti; Luigi Carratino; Lorenzo Rosasco; Alessandro Rudi (NeurIPS, 2020)

Fast quantum learning with statistical guarantees. - Carlo Ciliberto; Andrea Rocchetto; Alessandro Rudi; Leonard Wossnig (CoRR, 2020)

Structured and Localized Image Restoration. - Thomas Eboli; Alex Nowak-Vila; Jian Sun 009; Francis R. Bach; Jean Ponce; Alessandro Rudi (CoRR, 2020)

Consistent Structured Prediction with Max-Min Margin Markov Networks. - Alex Nowak-Vila; Francis R. Bach; Alessandro Rudi (CoRR, 2020)

Learning Output Embeddings in Structured Prediction. - Luc Brogat-Motte; Alessandro Rudi; Céline Brouard; Juho Rousu; Florence d'Alché-Buc (CoRR, 2020)

Faster Kriging: Facing High-Dimensional Simulators. - Xuefei Lu; Alessandro Rudi; Emanuele Borgonovo; Lorenzo Rosasco (Oper. Res., 2020)

A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings. - Carlo Ciliberto; Lorenzo Rosasco; Alessandro Rudi (J. Mach. Learn. Res., 2020)

Approximating Hamiltonian dynamics with the Nyström method. - Alessandro Rudi; Leonard Wossnig; Carlo Ciliberto; Andrea Rocchetto; Massimiliano Pontil; Simone Severini (Quantum, 2020)

Fast Rates for Structured Prediction. - Vivien A. Cabannes; Francis R. Bach; Alessandro Rudi (COLT, 2021)

A Dimension-free Computational Upper-bound for Smooth Optimal Transport Estimation. - Adrien Vacher; Boris Muzellec; Alessandro Rudi; Francis R. Bach; François-Xavier Vialard (COLT, 2021)

Disambiguation of Weak Supervision leading to Exponential Convergence rates. - Vivien A. Cabannes; Francis R. Bach; Alessandro Rudi (ICML, 2021)

Beyond Tikhonov: faster learning with self-concordant losses, via iterative regularization. - Gaspard Beugnot; Julien Mairal; Alessandro Rudi (NeurIPS, 2021)

Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning. - Vivien Cabannes; Loucas Pillaud-Vivien; Francis R. Bach; Alessandro Rudi (NeurIPS, 2021)

Mixability made efficient: Fast online multiclass logistic regression. - Rémi Jézéquel; Pierre Gaillard; Alessandro Rudi (NeurIPS, 2021)

PSD Representations for Effective Probability Models. - Alessandro Rudi; Carlo Ciliberto (NeurIPS, 2021)

Disambiguation of weak supervision with exponential convergence rates. - Vivien Cabannes; Francis R. Bach; Alessandro Rudi (CoRR, 2021)

Online nonparametric regression with Sobolev kernels. - Oleksandr Zadorozhnyi; Pierre Gaillard; Sébastien Gerchinovitz; Alessandro Rudi (CoRR, 2021)

Max-Margin is Dead, Long Live Max-Margin! - Alex Nowak-Vila; Alessandro Rudi; Francis R. Bach (CoRR, 2021)

A Note on Optimizing Distributions using Kernel Mean Embeddings. - Boris Muzellec; Francis R. Bach; Alessandro Rudi (CoRR, 2021)

Learning PSD-valued functions using kernel sums-of-squares. - Boris Muzellec; Francis R. Bach; Alessandro Rudi (CoRR, 2021)

Near-optimal estimation of smooth transport maps with kernel sums-of-squares. - Boris Muzellec; Adrien Vacher; Francis R. Bach; François-Xavier Vialard; Alessandro Rudi (CoRR, 2021)

Sampling from Arbitrary Functions via PSD Models. - Ulysse Marteau-Ferey; Francis R. Bach; Alessandro Rudi (AISTATS, 2022)

On the Consistency of Max-Margin Losses. - Alex Nowak; Alessandro Rudi; Francis R. Bach (AISTATS, 2022)

Infinite-Dimensional Sums-of-Squares for Optimal Control. - Eloïse Berthier; Justin Carpentier; Alessandro Rudi; Francis R. Bach (CDC, 2022)

On the Benefits of Large Learning Rates for Kernel Methods. - Gaspard Beugnot; Julien Mairal; Alessandro Rudi (COLT, 2022)

Non-Convex Optimization with Certificates and Fast Rates Through Kernel Sums of Squares. - Blake E. Woodworth; Francis R. Bach; Alessandro Rudi (COLT, 2022)

Measuring dissimilarity with diffeomorphism invariance. - Théophile Cantelobre; Carlo Ciliberto; Benjamin Guedj; Alessandro Rudi (ICML, 2022)

Nyström Kernel Mean Embeddings. - Antoine Chatalic; Nicolas Schreuder; Lorenzo Rosasco; Alessandro Rudi (ICML, 2022)

Active Labeling: Streaming Stochastic Gradients. - Vivien Cabannes; Francis R. Bach; Vianney Perchet; Alessandro Rudi (NeurIPS, 2022)

Vector-Valued Least-Squares Regression under Output Regularity Assumptions. - Luc Brogat-Motte; Alessandro Rudi; Céline Brouard; Juho Rousu; Florence d'Alché-Buc (J. Mach. Learn. Res., 2022)

GloptiNets: Scalable Non-Convex Optimization with Certificates. - Gaspard Beugnot; Julien Mairal; Alessandro Rudi (NeurIPS, 2023)

Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models. - Anant Raj; Umut Simsekli; Alessandro Rudi (NeurIPS, 2023)

Approximation of optimization problems with constraints through kernel Sum-Of-Squares. - Pierre-Cyril Aubin-Frankowski; Alessandro Rudi (CoRR, 2023)

Non-Parametric Learning of Stochastic Differential Equations with Fast Rates of Convergence. - Riccardo Bonalli; Alessandro Rudi (CoRR, 2023)

Exponential Convergence of Sum-of-Squares Hierarchies for Trigonometric Polynomials. - Francis R. Bach; Alessandro Rudi (SIAM J. Optim., 2023)

Closed-form Filtering for Non-linear Systems. - Théophile Cantelobre; Carlo Ciliberto; Benjamin Guedj; Alessandro Rudi (CoRR, 2024)

Structured Prediction in Online Learning. - Pierre Boudart; Alessandro Rudi; Pierre Gaillard (CoRR, 2024)

Generalization Bounds of Surrogate Policies for Combinatorial Optimization Problems. - Pierre-Cyril Aubin-Frankowski; Yohann De Castro; Axel Parmentier; Alessandro Rudi (CoRR, 2024)

Learning Controlled Stochastic Differential Equations. - Luc Brogat-Motte; Riccardo Bonalli; Alessandro Rudi (CoRR, 2024)

Optimal Estimation of Smooth Transport Maps with Kernel SoS. - Adrien Vacher; Boris Muzellec; Francis R. Bach; François-Xavier Vialard; Alessandro Rudi (SIAM J. Math. Data Sci., 2024)

Second Order Conditions to Decompose Smooth Functions as Sums of Squares. - Ulysse Marteau-Ferey; Francis R. Bach; Alessandro Rudi (SIAM J. Optim., 2024)

Solving Moment and Polynomial Optimization Problems on Sobolev Spaces. - Didier Henrion; Alessandro Rudi (SIAM J. Optim., 2025)

Finding global minima via kernel approximations. - Alessandro Rudi; Ulysse Marteau-Ferey; Francis R. Bach (Math. Program., 2025)

Safely Learning Controlled Stochastic Dynamics. - Luc Brogat-Motte; Alessandro Rudi; Riccardo Bonalli (CoRR, 2025)

Enjoying Non-linearity in Multinomial Logistic Bandits. - Pierre Boudart; Pierre Gaillard; Alessandro Rudi (CoRR, 2025)

Dynamic Regret Reduces to Kernelized Static Regret. - Andrew Jacobsen; Alessandro Rudi; Francesco Orabona; Nicolò Cesa-Bianchi (CoRR, 2025)