Rosasco, Lorenzo

77 publications

TMLR 2025 Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig

NeurIPS 2025 Computational Efficiency Under Covariate Shift in Kernel Ridge Regression Andrea Della Vecchia, Arnaud Mavakala Watusadisi, Ernesto De Vito, Lorenzo Rosasco

JMLR 2025 Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling Antoine Chatalic, Nicolas Schreuder, Ernesto De Vito, Lorenzo Rosasco

NeurIPS 2025 The $\varphi$ Curve: The Shape of Generalization Through the Lens of Norm-Based Capacity Control Yichen Wang, Yudong Chen, Lorenzo Rosasco, Fanghui Liu

ICLR 2025 Towards a Learning Theory of Representation Alignment Francesco Insulla, Shuo Huang, Lorenzo Rosasco

JMLR 2024 The Nyström Method for Convex Loss Functions Andrea Della Vecchia, Ernesto De Vito, Jaouad Mourtada, Lorenzo Rosasco

NeurIPS 2023 An Optimal Structured Zeroth-Order Algorithm for Non-Smooth Optimization Marco Rando, Cesare Molinari, Lorenzo Rosasco, Silvia Villa

NeurIPS 2023 Assumption Violations in Causal Discovery and the Robustness of Score Matching Francesco Montagna, Atalanti Mastakouri, Elias Eulig, Nicoletta Noceti, Lorenzo Rosasco, Dominik Janzing, Bryon Aragam, Francesco Locatello

CLeaR 2023 Causal Discovery with Score Matching on Additive Models with Arbitrary Noise Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello

COLT 2023 Conference on Learning Theory 2023: Preface Gergely Neu, Lorenzo Rosasco

NeurIPS 2023 Estimating Koopman Operators with Sketching to Provably Learn Large Scale Dynamical Systems Giacomo Meanti, Antoine Chatalic, Vladimir Kostic, Pietro Novelli, Massimiliano Pontil, Lorenzo Rosasco

CoRL 2023 Heteroscedastic Gaussian Processes and Random Features: Scalable Motion Primitives with Guarantees Edoardo Caldarelli, Antoine Chatalic, Adrià Colomé, Lorenzo Rosasco, Carme Torras

CLeaR 2023 Scalable Causal Discovery with Score Matching Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello

AISTATS 2022 Ada-BKB: Scalable Gaussian Process Optimization on Continuous Domains by Adaptive Discretization Marco Rando, Luigi Carratino, Silvia Villa, Lorenzo Rosasco

AISTATS 2022 Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression Giacomo Meanti, Luigi Carratino, Ernesto De Vito, Lorenzo Rosasco

AISTATS 2022 Mean Nyström Embeddings for Adaptive Compressive Learning Antoine Chatalic, Luigi Carratino, Ernesto De Vito, Lorenzo Rosasco

NeurIPS 2022 Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces Vladimir Kostic, Pietro Novelli, Andreas Maurer, Carlo Ciliberto, Lorenzo Rosasco, Massimiliano Pontil

ICML 2022 Multiclass Learning with Margin: Exponential Rates with No Bias-Variance Trade-Off Stefano Vigogna, Giacomo Meanti, Ernesto De Vito, Lorenzo Rosasco

ICML 2022 Nyström Kernel Mean Embeddings Antoine Chatalic, Nicolas Schreuder, Lorenzo Rosasco, Alessandro Rudi

NeurIPSW 2022 Scalable Causal Discovery with Score Matching Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello

ICML 2022 Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco

AISTATS 2021 Asymptotics of Ridge(less) Regression Under General Source Condition Dominic Richards, Jaouad Mourtada, Lorenzo Rosasco

AISTATS 2021 Iterative Regularization for Convex Regularizers Cesare Molinari, Mathurin Massias, Lorenzo Rosasco, Silvia Villa

AISTATS 2021 Regularized ERM on Random Subspaces Andrea Della Vecchia, Jaouad Mourtada, Ernesto De Vito, Lorenzo Rosasco

NeurIPS 2021 ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions Luigi Carratino, Stefano Vigogna, Daniele Calandriello, Lorenzo Rosasco

JMLR 2020 A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings Carlo Ciliberto, Lorenzo Rosasco, Alessandro Rudi

ICML 2020 Decentralised Learning with Random Features and Distributed Gradient Descent Dominic Richards, Patrick Rebeschini, Lorenzo Rosasco

AISTATS 2020 Gain with No Pain: Efficiency of Kernel-PCA by Nyström Sampling Nicholas Sterge, Bharath Sriperumbudur, Lorenzo Rosasco, Alessandro Rudi

AISTATS 2020 Hyperbolic Manifold Regression Gian Marconi, Carlo Ciliberto, Lorenzo Rosasco

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

ICML 2020 Near-Linear Time Gaussian Process Optimization with Adaptive Batching and Resparsification Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco

NeurIPS 2019 Beating SGD Saturation with Tail-Averaging and Minibatching Nicole Muecke, Gergely Neu, Lorenzo Rosasco

COLT 2019 Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco

NeurIPS 2019 Implicit Regularization of Accelerated Methods in Hilbert Spaces Nicolò Pagliana, Lorenzo Rosasco

NeurIPS 2018 Dirichlet-Based Gaussian Processes for Large-Scale Calibrated Classification Dimitrios Milios, Raffaello Camoriano, Pietro Michiardi, Lorenzo Rosasco, Maurizio Filippone

COLT 2018 Iterate Averaging as Regularization for Stochastic Gradient Descent Gergely Neu, Lorenzo Rosasco

NeurIPS 2018 Learning with SGD and Random Features Luigi Carratino, Alessandro Rudi, Lorenzo Rosasco

NeurIPS 2018 Manifold Structured Prediction Alessandro Rudi, Carlo Ciliberto, GianMaria Marconi, Lorenzo Rosasco

NeurIPS 2018 On Fast Leverage Score Sampling and Optimal Learning Alessandro Rudi, Daniele Calandriello, Luigi Carratino, Lorenzo Rosasco

AISTATS 2018 Solving Lp-Norm Regularization with Tensor Kernels Saverio Salzo, Lorenzo Rosasco, Johan A. K. Suykens

NeurIPS 2018 Statistical and Computational Trade-Offs in Kernel K-Means Daniele Calandriello, Lorenzo Rosasco

NeurIPS 2017 Consistent Multitask Learning with Nonlinear Output Relations Carlo Ciliberto, Alessandro Rudi, Lorenzo Rosasco, Massimiliano Pontil

NeurIPS 2017 FALKON: An Optimal Large Scale Kernel Method Alessandro Rudi, Luigi Carratino, Lorenzo Rosasco

NeurIPS 2017 Generalization Properties of Learning with Random Features Alessandro Rudi, Lorenzo Rosasco

JMLR 2017 Optimal Rates for Multi-Pass Stochastic Gradient Methods Junhong Lin, Lorenzo Rosasco

NeurIPS 2016 A Consistent Regularization Approach for Structured Prediction Carlo Ciliberto, Lorenzo Rosasco, Alessandro Rudi

ICML 2016 Generalization Properties and Implicit Regularization for Multiple Passes SGM Junhong Lin, Raffaello Camoriano, Lorenzo Rosasco

AAAI 2016 Holographic Embeddings of Knowledge Graphs Maximilian Nickel, Lorenzo Rosasco, Tomaso A. Poggio

JMLR 2016 Iterative Regularization for Learning with Convex Loss Functions Junhong Lin, Lorenzo Rosasco, Ding-Xuan Zhou

AISTATS 2016 NYTRO: When Subsampling Meets Early Stopping Raffaello Camoriano, Tomás Angles, Alessandro Rudi, Lorenzo Rosasco

NeurIPS 2016 Optimal Learning for Multi-Pass Stochastic Gradient Methods Junhong Lin, Lorenzo Rosasco

ICML 2015 Convex Learning of Multiple Tasks and Their Structure Carlo Ciliberto, Youssef Mroueh, Tomaso Poggio, Lorenzo Rosasco

CVPR 2015 Learning Multiple Visual Tasks While Discovering Their Structure Carlo Ciliberto, Lorenzo Rosasco, Silvia Villa

NeurIPS 2015 Learning with Incremental Iterative Regularization Lorenzo Rosasco, Silvia Villa

NeurIPS 2015 Less Is More: Nyström Computational Regularization Alessandro Rudi, Raffaello Camoriano, Lorenzo Rosasco

MLOSS 2013 GURLS: A Least Squares Library for Supervised Learning Andrea Tacchetti, Pavan K. Mallapragada, Matteo Santoro, Lorenzo Rosasco

JMLR 2013 Nonparametric Sparsity and Regularization Lorenzo Rosasco, Silvia Villa, Sofia Mosci, Matteo Santoro, Alessandro Verri

NeurIPS 2013 On the Sample Complexity of Subspace Learning Alessandro Rudi, Guillermo D Canas, Lorenzo Rosasco

CVPRW 2013 iCub World: Friendly Robots Help Building Good Vision Data-Sets Sean Ryan Fanello, Carlo Ciliberto, Matteo Santoro, Lorenzo Natale, Giorgio Metta, Lorenzo Rosasco, Francesca Odone

FnTML 2012 Kernels for Vector-Valued Functions: A Review Mauricio A. Álvarez, Lorenzo Rosasco, Neil D. Lawrence

NeurIPS 2012 Learning Manifolds with K-Means and K-Flats Guillermo Canas, Tomaso Poggio, Lorenzo Rosasco

NeurIPS 2012 Learning Probability Measures with Respect to Optimal Transport Metrics Guillermo Canas, Lorenzo Rosasco

MLJ 2012 Multi-Output Learning via Spectral Filtering Luca Baldassarre, Lorenzo Rosasco, Annalisa Barla, Alessandro Verri

NeurIPS 2012 Multiclass Learning with Simplex Coding Youssef Mroueh, Tomaso Poggio, Lorenzo Rosasco, Jean-jeacques Slotine

NeurIPS 2010 A Primal-Dual Algorithm for Group Sparse Regularization with Overlapping Groups Sofia Mosci, Silvia Villa, Alessandro Verri, Lorenzo Rosasco

AISTATS 2010 A Regularization Approach to Nonlinear Variable Selection Lorenzo Rosasco, Matteo Santoro, Sofia Mosci, Alessandro Verri, Silvia Villa

JMLR 2010 On Learning with Integral Operators Lorenzo Rosasco, Mikhail Belkin, Ernesto De Vito

ECML-PKDD 2010 Solving Structured Sparsity Regularization with Proximal Methods Sofia Mosci, Lorenzo Rosasco, Matteo Santoro, Alessandro Verri, Silvia Villa

NeurIPS 2010 Spectral Regularization for Support Estimation Ernesto D. Vito, Lorenzo Rosasco, Alessandro Toigo

ECML-PKDD 2010 Vector Field Learning via Spectral Filtering Luca Baldassarre, Lorenzo Rosasco, Annalisa Barla, Alessandro Verri

COLT 2009 A Note on Learning with Integral Operators Lorenzo Rosasco, Mikhail Belkin, Ernesto De Vito

NeurIPS 2009 On Invariance in Hierarchical Models Jake Bouvrie, Lorenzo Rosasco, Tomaso Poggio

ICML 2007 Dimensionality Reduction and Generalization Sofia Mosci, Lorenzo Rosasco, Alessandro Verri

JMLR 2005 Learning from Examples as an Inverse Problem Ernesto De Vito, Lorenzo Rosasco, Andrea Caponnetto, Umberto De Giovannini, Francesca Odone

NeCo 2004 Are Loss Functions All the Same? Lorenzo Rosasco, Ernesto De Vito, Andrea Caponnetto, Michele Piana, Alessandro Verri

NeurIPS 2004 Learning, Regularization and Ill-Posed Inverse Problems Lorenzo Rosasco, Andrea Caponnetto, Ernesto D. Vito, Francesca Odone, Umberto D. Giovannini

JMLR 2004 Some Properties of Regularized Kernel Methods Ernesto De Vito, Lorenzo Rosasco, Andrea Caponnetto, Michele Piana, Alessandro Verri