← Authors

Lorenzo Rosasco

63 papers · 2004–2025 · 9 conferences · across top CS/AI conferences

Achievements

๐ŸŒ Conference Polyglot (9) ๐Ÿงญ Keyword Pioneer ๐Ÿฃ Hot Topic Early Bird ๐Ÿ Cross-Pollinator (13) ๐ŸŒˆ Renaissance Researcher (5) ๐ŸŒ‰ Interdisciplinary Bridge ๐Ÿƒ Academic Marathon (21) ๐Ÿ—บ๏ธ Taxonomy Completionist (20) ๐Ÿ  Conference Loyalist (27) ๐ŸŒŸ Keyword Trendsetter Combo (3) ๐Ÿ”ฌ Deep Specialist (28) ๐Ÿ† Keyword Champion ๐ŸŒฑ Topic Pioneer ๐Ÿค Dynamic Duo (14) ๐Ÿ“ˆ Trend Setter ๐Ÿ”ฅ Unstoppable (11) ๐Ÿš€ Conference Pioneer โšก Prolific Year (7) ๐Ÿ’Ž Century Club (63) ๐Ÿ—ƒ๏ธ Keyword Collector (97)

Conferences

NIPS (27) AISTATS (11) JMLR (10) ICML (7) COLT (3) CLEAR (2) CORL (1) CVPR (1) ICLR (1)

Top co-authors

Alessandro Rudi (14) Luigi Carratino (11) Ernesto De Vito (9) Carlo Ciliberto (8) Silvia Villa (8) Daniele Calandriello (6) Antoine Chatalic (5) Alessandro Verri (4) Junhong Lin (4) Giacomo Meanti (4)

Research topics

Statistics (1)

Keywords

kernel methods (16) reproducing kernel hilbert space (7) nystrรถm method (6) stochastic gradient descent (6) gaussian process (5) nonparametric learning (5) convex optimization (5) early stopping (4) bayesian optimization (4) structured prediction (4) iterative regularization (4) support vector machine (4) kernel ridge regression (4) manifold learning (4) random feature (4) score matching (3) regularization parameter (3) causal discovery (3) ridge regression (3) large-scale learning (3)

Papers

Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling JMLR 2025
Towards a learning theory of representation alignment ICLR 2025
The Nystrรถm 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
Nystrรถm Kernel Mean Embeddings ICML 2022
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times ICML 2022
Mean Nystrรถm 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 Nystrรถm 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: Nystrรถm 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