Lorenzo Rosasco

Orcid: 0000-0003-3098-383X

Affiliations:
  • MIT, Cambridge, MA, USA


According to our database1, Lorenzo Rosasco authored at least 182 papers between 2004 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Stochastic zeroth order descent with structured directions.
Comput. Optim. Appl., December, 2024

Iterative regularization for low complexity regularizers.
Numerische Mathematik, April, 2024

A structured prediction approach for robot imitation learning.
Int. J. Robotics Res., February, 2024

RESPRECT: Speeding-up Multi-Fingered Grasping With Residual Reinforcement Learning.
IEEE Robotics Autom. Lett., 2024

Top-tuning: A study on transfer learning for an efficient alternative to fine tuning for image classification with fast kernel methods.
Image Vis. Comput., 2024

Trust And Balance: Few Trusted Samples Pseudo-Labeling and Temperature Scaled Loss for Effective Source-Free Unsupervised Domain Adaptation.
CoRR, 2024

A New Formulation for Zeroth-Order Optimization of Adversarial EXEmples in Malware Detection.
CoRR, 2024

Q-Learning to navigate turbulence without a map.
CoRR, 2024

Neural reproducing kernel Banach spaces and representer theorems for deep networks.
CoRR, 2024

Linear quadratic control of nonlinear systems with Koopman operator learning and the Nyström method.
CoRR, 2024

Key Design Choices in Source-Free Unsupervised Domain Adaptation: An In-depth Empirical Analysis.
CoRR, 2024

Sim2Real Bilevel Adaptation for Object Surface Classification using Vision-Based Tactile Sensors.
Proceedings of the IEEE International Conference on Robotics and Automation, 2024

Automatic Gain Tuning for Humanoid Robots Walking Architectures Using Gradient-Free Optimization Techniques.
Proceedings of the 23rd IEEE-RAS International Conference on Humanoid Robots, 2024

2023
Fast kernel methods for data quality monitoring as a goodness-of-fit test.
Mach. Learn. Sci. Technol., September, 2023

Zeroth-order optimization with orthogonal random directions.
Math. Program., May, 2023

Convergence of the forward-backward algorithm: beyond the worst-case with the help of geometry.
Math. Program., March, 2023

Learning Rhythmic Trajectories with Geometric Constraints for Laser-Based Skincare Procedures.
CoRR, 2023

Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling.
CoRR, 2023

Shortcuts for causal discovery of nonlinear models by score matching.
CoRR, 2023

Key Design Choices for Double-Transfer in Source-Free Unsupervised Domain Adaptation.
CoRR, 2023

From inexact optimization to learning via gradient concentration.
Comput. Optim. Appl., 2023

An Optimal Structured Zeroth-order Algorithm for Non-smooth Optimization.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Assumption violations in causal discovery and the robustness of score matching.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Estimating Koopman operators with sketching to provably learn large scale dynamical systems.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A Grasp Pose is All You Need: Learning Multi-Fingered Grasping with Deep Reinforcement Learning from Vision and Touch.
IROS, 2023

Regularization Properties of Dual Subgradient Flow.
Proceedings of the European Control Conference, 2023

Heteroscedastic Gaussian Processes and Random Features: Scalable Motion Primitives with Guarantees.
Proceedings of the Conference on Robot Learning, 2023

Scalable Causal Discovery with Score Matching.
Proceedings of the Conference on Causal Learning and Reasoning, 2023

Causal Discovery with Score Matching on Additive Models with Arbitrary Noise.
Proceedings of the Conference on Causal Learning and Reasoning, 2023

2022
Learn Fast, Segment Well: Fast Object Segmentation Learning on the iCub Robot.
IEEE Trans. Robotics, 2022

Fast approximation of orthogonal matrices and application to PCA.
Signal Process., 2022

ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots.
IEEE Robotics Autom. Lett., 2022

Iterative regularization in classification via hinge loss diagonal descent.
CoRR, 2022

Fine-tuning or top-tuning? Transfer learning with pretrained features and fast kernel methods.
CoRR, 2022

Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs.
CoRR, 2022

Learning new physics efficiently with nonparametric methods.
CoRR, 2022

Physics Informed Shallow Machine Learning for Wind Speed Prediction.
CoRR, 2022

An elementary analysis of ridge regression with random design.
CoRR, 2022

From Handheld to Unconstrained Object Detection: a Weakly-supervised On-line Learning Approach.
Proceedings of the 31st IEEE International Conference on Robot and Human Interactive Communication, 2022

Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Efficient Unsupervised Learning for Plankton Images.
Proceedings of the 26th International Conference on Pattern Recognition, 2022

Multiclass learning with margin: exponential rates with no bias-variance trade-off.
Proceedings of the International Conference on Machine Learning, 2022

Nyström Kernel Mean Embeddings.
Proceedings of the International Conference on Machine Learning, 2022

Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times.
Proceedings of the International Conference on Machine Learning, 2022

Ada-BKB: Scalable Gaussian Process Optimization on Continuous Domains by Adaptive Discretization.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Mean Nyström Embeddings for Adaptive Compressive Learning.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Constructing Fast Approximate Eigenspaces With Application to the Fast Graph Fourier Transforms.
IEEE Trans. Signal Process., 2021

Accelerated Iterative Regularization via Dual Diagonal Descent.
SIAM J. Optim., 2021

Structured Prediction for CRiSP Inverse Kinematics Learning With Misspecified Robot Models.
IEEE Robotics Autom. Lett., 2021

On the Emergence of Whole-Body Strategies From Humanoid Robot Push-Recovery Learning.
IEEE Robotics Autom. Lett., 2021

Understanding neural networks with reproducing kernel Banach spaces.
CoRR, 2021

Ada-BKB: Scalable Gaussian Process Optimization on Continuous Domain by Adaptive Discretization.
CoRR, 2021

ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Fast Object Segmentation Learning with Kernel-based Methods for Robotics.
Proceedings of the IEEE International Conference on Robotics and Automation, 2021

Regularized ERM on random subspaces.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Asymptotics of Ridge(less) Regression under General Source Condition.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Iterative regularization for convex regularizers.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings.
J. Mach. Learn. Res., 2020

Faster Kriging: Facing High-Dimensional Simulators.
Oper. Res., 2020

Learning to Avoid Obstacles With Minimal Intervention Control.
Frontiers Robotics AI, 2020

Data-efficient Weakly-supervised Learning for On-line Object Detection under Domain Shift in Robotics.
CoRR, 2020

Fast Region Proposal Learning for Object Detection for Robotics.
CoRR, 2020

For interpolating kernel machines, the minimum norm ERM solution is the most stable.
CoRR, 2020

Implicit regularization for convex regularizers.
CoRR, 2020

On-line object detection: a robotics challenge.
Auton. Robots, 2020

Kernel Methods Through the Roof: Handling Billions of Points Efficiently.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Decentralised Learning with Random Features and Distributed Gradient Descent.
Proceedings of the 37th International Conference on Machine Learning, 2020

Near-linear time Gaussian process optimization with adaptive batching and resparsification.
Proceedings of the 37th International Conference on Machine Learning, 2020

Gain with no Pain: Efficiency of Kernel-PCA by Nyström Sampling.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Hyperbolic Manifold Regression.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Are we done with object recognition? The iCub robot's perspective.
Robotics Auton. Syst., 2019

Symmetry-adapted representation learning.
Pattern Recognit., 2019

Gain with no Pain: Efficient Kernel-PCA by Nyström Sampling.
CoRR, 2019

Multi-Scale Vector Quantization with Reconstruction Trees.
CoRR, 2019

Reproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion spaces.
CoRR, 2019

Theory III: Dynamics and Generalization in Deep Networks.
CoRR, 2019

A computational model for grid maps in neural populations.
CoRR, 2019

Implicit Regularization of Accelerated Methods in Hilbert Spaces.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Beating SGD Saturation with Tail-Averaging and Minibatching.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Learning to Sequence Multiple Tasks with Competing Constraints.
Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2019

Genuine Personality Recognition from Highly Constrained Face Images.
Proceedings of the Image Analysis and Processing - ICIAP 2019, 2019

A Weakly Supervised Strategy for Learning Object Detection on a Humanoid Robot.
Proceedings of the 19th IEEE-RAS International Conference on Humanoid Robots, 2019

Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret.
Proceedings of the Conference on Learning Theory, 2019

2018
Iterative Regularization via Dual Diagonal Descent.
J. Math. Imaging Vis., 2018

Generalization properties of doubly stochastic learning algorithms.
J. Complex., 2018

Theory of Deep Learning III: explaining the non-overfitting puzzle.
CoRR, 2018

Modified Fejér sequences and applications.
Comput. Optim. Appl., 2018

Manifold Structured Prediction.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

On Fast Leverage Score Sampling and Optimal Learning.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Learning with SGD and Random Features.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Statistical and Computational Trade-Offs in Kernel K-Means.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Speeding-Up Object Detection Training for Robotics with FALKON.
Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2018

Constrained DMPs for Feasible Skill Learning on Humanoid Robots.
Proceedings of the 18th IEEE-RAS International Conference on Humanoid Robots, 2018

Sparse Multiple Kernel Learning: Support Identification via Mirror Stratifiability.
Proceedings of the 26th European Signal Processing Conference, 2018

Iterate Averaging as Regularization for Stochastic Gradient Descent.
Proceedings of the Conference On Learning Theory, 2018

Solving lp-norm regularization with tensor kernels.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
Optimal Rates for Multi-pass Stochastic Gradient Methods.
J. Mach. Learn. Res., 2017

Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review.
Int. J. Autom. Comput., 2017

Optimal Rates for Learning with Nyström Stochastic Gradient Methods.
CoRR, 2017

Don't relax: early stopping for convex regularization.
CoRR, 2017

Generalization Properties of Doubly Online Learning Algorithms.
CoRR, 2017

Generalization Properties of Learning with Random Features.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

FALKON: An Optimal Large Scale Kernel Method.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Consistent Multitask Learning with Nonlinear Output Relations.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Incremental robot learning of new objects with fixed update time.
Proceedings of the 2017 IEEE International Conference on Robotics and Automation, 2017

Interactive data collection for deep learning object detectors on humanoid robots.
Proceedings of the 17th IEEE-RAS International Conference on Humanoid Robotics, 2017

2016
Unsupervised learning of invariant representations.
Theor. Comput. Sci., 2016

Stochastic Forward-Backward Splitting for Monotone Inclusions.
J. Optim. Theory Appl., 2016

Iterative Regularization for Learning with Convex Loss Functions.
J. Mach. Learn. Res., 2016

Enabling Depth-Driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives.
Frontiers Robotics AI, 2016

Generalization Properties of Learning with Random Features.
CoRR, 2016

Incremental Object Recognition in Robotics with Extension to New Classes in Constant Time.
CoRR, 2016

Optimal Learning for Multi-pass Stochastic Gradient Methods.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

A Consistent Regularization Approach for Structured Prediction.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Object identification from few examples by improving the invariance of a Deep Convolutional Neural Network.
Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016

Incremental semiparametric inverse dynamics learning.
Proceedings of the 2016 IEEE International Conference on Robotics and Automation, 2016

Generalization Properties and Implicit Regularization for Multiple Passes SGM.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Active perception: Building objects' models using tactile exploration.
Proceedings of the 16th IEEE-RAS International Conference on Humanoid Robots, 2016

Combining sensory modalities and exploratory procedures to improve haptic object recognition in robotics.
Proceedings of the 16th IEEE-RAS International Conference on Humanoid Robots, 2016

NYTRO: When Subsampling Meets Early Stopping.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Holographic Embeddings of Knowledge Graphs.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015
Mathematical and Computational Foundations of Learning Theory (Dagstuhl Seminar 15361).
Dagstuhl Reports, 2015

Real-world Object Recognition with Off-the-shelf Deep Conv Nets: How Many Objects can iCub Learn?
CoRR, 2015

Deep Convolutional Networks are Hierarchical Kernel Machines.
CoRR, 2015

On Invariance and Selectivity in Representation Learning.
CoRR, 2015

Characterizing the Input-Output Function of the Olfactory-Limbic Pathway in the Guinea Pig.
Comput. Intell. Neurosci., 2015

Less is More: Nyström Computational Regularization.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Learning with Incremental Iterative Regularization.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Discriminative template learning in group-convolutional networks for invariant speech representations.
Proceedings of the 16th Annual Conference of the International Speech Communication Association, 2015

Discovering discrete subword units with binarized autoencoders and hidden-Markov-model encoders.
Proceedings of the 16th Annual Conference of the International Speech Communication Association, 2015

Teaching iCub to recognize objects using deep Convolutional Neural Networks.
Proceedings of the 4th Workshop on Machine Learning for Interactive Systems, 2015

Convex Learning of Multiple Tasks and their Structure.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Learning multiple visual tasks while discovering their structure.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015

2014
Regularization by Early Stopping for Online Learning Algorithms.
CoRR, 2014

Learning An Invariant Speech Representation.
CoRR, 2014

Proximal methods for the latent group lasso penalty.
Comput. Optim. Appl., 2014

On efficiency and low sample complexity in phase retrieval.
Proceedings of the 2014 IEEE International Symposium on Information Theory, Honolulu, HI, USA, June 29, 2014

Exploiting global force torque measurements for local compliance estimation in tactile arrays.
Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014

Phone classification by a hierarchy of invariant representation layers.
Proceedings of the 15th Annual Conference of the International Speech Communication Association, 2014

Word-level invariant representations from acoustic waveforms.
Proceedings of the 15th Annual Conference of the International Speech Communication Association, 2014

A deep representation for invariance and music classification.
Proceedings of the IEEE International Conference on Acoustics, 2014

2013
GURLS: a least squares library for supervised learning.
J. Mach. Learn. Res., 2013

Nonparametric sparsity and regularization.
J. Mach. Learn. Res., 2013

q-ary Compressive Sensing
CoRR, 2013

Quantization and Greed are Good: One bit Phase Retrieval, Robustness and Greedy Refinements.
CoRR, 2013

Unsupervised Learning of Invariant Representations in Hierarchical Architectures.
CoRR, 2013

On the Sample Complexity of Subspace Learning.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

On the impact of learning hierarchical representations for visual recognition in robotics.
Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013

iCub World: Friendly Robots Help Building Good Vision Data-Sets.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013

On Learnability, Complexity and Stability.
Proceedings of the Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik, 2013

2012
Multi-output learning via spectral filtering.
Mach. Learn., 2012

Kernels for Vector-Valued Functions: A Review.
Found. Trends Mach. Learn., 2012

Multiclass Learning with Simplex Coding.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Learning Probability Measures with respect to Optimal Transport Metrics.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Learning Manifolds with K-Means and K-Flats.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011
Mathematical and Computational Foundations of Learning Theory (Dagstuhl Seminar 11291).
Dagstuhl Reports, 2011

Online Learning, Stability, and Stochastic Gradient Descent
CoRR, 2011

2010
A Regularization Approach to Nonlinear Variable Selection.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

On Learning with Integral Operators.
J. Mach. Learn. Res., 2010

Adaptive Kernel Methods Using the Balancing Principle.
Found. Comput. Math., 2010

Mathematics of the Neural Response.
Found. Comput. Math., 2010

Solving Structured Sparsity Regularization with Proximal Methods.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

Vector Field Learning via Spectral Filtering.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

Spectral Regularization for Support Estimation.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

A Primal-Dual Algorithm for Group Sparse Regularization with Overlapping Groups.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

2009
Elastic-net regularization in learning theory.
J. Complex., 2009

On Invariance in Hierarchical Models.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Towards a Theoretical Framework for Learning Multi-modal Patterns for Embodied Agents.
Proceedings of the Image Analysis and Processing, 2009

A Note on Learning with Integral Operators.
Proceedings of the COLT 2009, 2009

2008
Spectral Algorithms for Supervised Learning.
Neural Comput., 2008

A method for robust variable selection with significance assessment.
Proceedings of the 16th European Symposium on Artificial Neural Networks, 2008

2007
On regularization algorithms in learning theory.
J. Complex., 2007

Dimensionality reduction and generalization.
Proceedings of the Machine Learning, 2007

2006
Regularization approaches in learning theory.
PhD thesis, 2006

2005
Learning from Examples as an Inverse Problem.
J. Mach. Learn. Res., 2005

Model Selection for Regularized Least-Squares Algorithm in Learning Theory.
Found. Comput. Math., 2005

Support vector algorithms as regularization networks.
Proceedings of the 13th European Symposium on Artificial Neural Networks, 2005

2004
Are Loss Functions All the Same?.
Neural Comput., 2004

Some Properties of Regularized Kernel Methods
J. Mach. Learn. Res., 2004

Learning, Regularization and Ill-Posed Inverse Problems.
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004


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