Alessandro Rudi

Enjoying Non-linearity in Multinomial Logistic Bandits: A Minimax-Optimal Algorithm ALT 2026 Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models NeurIPS 2023 GloptiNets: Scalable Non-Convex Optimization with Certificates NeurIPS 2023 Active Labeling: Streaming Stochastic Gradients NeurIPS 2022 Measuring dissimilarity with diffeomorphism invariance ICML 2022 Non-Convex Optimization with Certificates and Fast Rates Through Kernel Sums of Squares COLT 2022 Nyström Kernel Mean Embeddings ICML 2022 On the Benefits of Large Learning Rates for Kernel Methods COLT 2022 On the Consistency of Max-Margin Losses AISTATS 2022 Sampling from Arbitrary Functions via PSD Models AISTATS 2022 Vector-Valued Least-Squares Regression under Output Regularity Assumptions JMLR 2022 A Dimension-free Computational Upper-bound for Smooth Optimal Transport Estimation COLT 2021 Beyond Tikhonov: faster learning with self-concordant losses, via iterative regularization NeurIPS 2021 Disambiguation of Weak Supervision leading to Exponential Convergence rates ICML 2021 Fast Rates for Structured Prediction COLT 2021 Mixability made efficient: Fast online multiclass logistic regression NeurIPS 2021 Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning NeurIPS 2021 PSD Representations for Effective Probability Models NeurIPS 2021 A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings JMLR 2020 Consistent Structured Prediction with Max-Min Margin Markov Networks ICML 2020 Efficient improper learning for online logistic regression COLT 2020 Gain with no Pain: Efficiency of Kernel-PCA by Nyström Sampling AISTATS 2020 Kernel Methods Through the Roof: Handling Billions of Points Efficiently NeurIPS 2020 Non-parametric Models for Non-negative Functions NeurIPS 2020 Statistical Estimation of the Poincaré constant and Application to Sampling Multimodal Distributions AISTATS 2020 Structured Prediction with Partial Labelling through the Infimum Loss ICML 2020 Affine Invariant Covariance Estimation for Heavy-Tailed Distributions COLT 2019 Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance COLT 2019 Efficient online learning with kernels for adversarial large scale problems NeurIPS 2019 Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses NeurIPS 2019 Localized Structured Prediction NeurIPS 2019 Massively scalable Sinkhorn distances via the Nyström method NeurIPS 2019 Sharp Analysis of Learning with Discrete Losses AISTATS 2019 Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance NeurIPS 2018 Exponential Convergence of Testing Error for Stochastic Gradient Methods COLT 2018 Learning with SGD and Random Features NeurIPS 2018 Manifold Structured Prediction NeurIPS 2018 On Fast Leverage Score Sampling and Optimal Learning NeurIPS 2018 Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes NeurIPS 2018 Consistent Multitask Learning with Nonlinear Output Relations NeurIPS 2017 FALKON: An Optimal Large Scale Kernel Method NeurIPS 2017 Generalization Properties of Learning with Random Features NeurIPS 2017 A Consistent Regularization Approach for Structured Prediction NeurIPS 2016 NYTRO: When Subsampling Meets Early Stopping AISTATS 2016 Less is More: Nyström Computational Regularization NeurIPS 2015 On the Sample Complexity of Subspace Learning NeurIPS 2013