Lorenzo Rosasco

Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling JMLR 2025 Towards a learning theory of representation alignment ICLR 2025 The Nyström method for convex loss functions JMLR 2024 Estimating Koopman operators with sketching to provably learn large scale dynamical systems NIPS 2023 Heteroscedastic Gaussian Processes and Random Features: Scalable Motion Primitives with Guarantees CORL 2023 Conference on Learning Theory 2023: Preface COLT 2023 Scalable Causal Discovery with Score Matching CLEAR 2023 Causal Discovery with Score Matching on Additive Models with Arbitrary Noise CLEAR 2023 An Optimal Structured Zeroth-order Algorithm for Non-smooth Optimization NIPS 2023 Assumption violations in causal discovery and the robustness of score matching NIPS 2023 Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces NIPS 2022 Multiclass learning with margin: exponential rates with no bias-variance trade-off ICML 2022 Nyström Kernel Mean Embeddings ICML 2022 Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times ICML 2022 Mean Nyström Embeddings for Adaptive Compressive Learning AISTATS 2022 Ada-BKB: Scalable Gaussian Process Optimization on Continuous Domains by Adaptive Discretization AISTATS 2022 Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression AISTATS 2022 Iterative regularization for convex regularizers AISTATS 2021 Asymptotics of Ridge(less) Regression under General Source Condition AISTATS 2021 Regularized ERM on random subspaces AISTATS 2021 ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions NIPS 2021 Near-linear time Gaussian process optimization with adaptive batching and resparsification ICML 2020 Kernel Methods Through the Roof: Handling Billions of Points Efficiently NIPS 2020 Hyperbolic Manifold Regression AISTATS 2020 Gain with no Pain: Efficiency of Kernel-PCA by Nyström Sampling AISTATS 2020 Decentralised Learning with Random Features and Distributed Gradient Descent ICML 2020 A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings JMLR 2020 Beating SGD Saturation with Tail-Averaging and Minibatching NIPS 2019 Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret COLT 2019 Implicit Regularization of Accelerated Methods in Hilbert Spaces NIPS 2019 Learning with SGD and Random Features NIPS 2018 Solving lp-norm regularization with tensor kernels AISTATS 2018 On Fast Leverage Score Sampling and Optimal Learning NIPS 2018 Statistical and Computational Trade-Offs in Kernel K-Means NIPS 2018 Iterate Averaging as Regularization for Stochastic Gradient Descent COLT 2018 Manifold Structured Prediction NIPS 2018 Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification NIPS 2018 FALKON: An Optimal Large Scale Kernel Method NIPS 2017 Optimal Rates for Multi-pass Stochastic Gradient Methods JMLR 2017 Consistent Multitask Learning with Nonlinear Output Relations NIPS 2017 Generalization Properties of Learning with Random Features NIPS 2017 Optimal Learning for Multi-pass Stochastic Gradient Methods NIPS 2016 Generalization Properties and Implicit Regularization for Multiple Passes SGM ICML 2016 A Consistent Regularization Approach for Structured Prediction NIPS 2016 NYTRO: When Subsampling Meets Early Stopping AISTATS 2016 Iterative Regularization for Learning with Convex Loss Functions JMLR 2016 Less is More: Nyström Computational Regularization NIPS 2015 Learning Multiple Visual Tasks While Discovering Their Structure CVPR 2015 Learning with Incremental Iterative Regularization NIPS 2015 Convex Learning of Multiple Tasks and their Structure ICML 2015 GURLS: A Least Squares Library for Supervised Learning JMLR 2013 Nonparametric Sparsity and Regularization JMLR 2013 On the Sample Complexity of Subspace Learning NIPS 2013 Multiclass Learning with Simplex Coding NIPS 2012 Learning Probability Measures with respect to Optimal Transport Metrics NIPS 2012 Learning Manifolds with K-Means and K-Flats NIPS 2012 Spectral Regularization for Support Estimation NIPS 2010 On Learning with Integral Operators JMLR 2010 A Regularization Approach to Nonlinear Variable Selection AISTATS 2010 A Primal-Dual Algorithm for Group Sparse Regularization with Overlapping Groups NIPS 2010 On Invariance in Hierarchical Models NIPS 2009 Learning from Examples as an Inverse Problem JMLR 2005 Some Properties of Regularized Kernel Methods JMLR 2004