Learning, Regularization and Ill-Posed Inverse Problems. - Lorenzo Rosasco; Andrea Caponnetto; Ernesto De Vito; Francesca Odone; Umberto De Giovannini (NIPS, 2004)

Some Properties of Regularized Kernel Methods - Ernesto De Vito; Lorenzo Rosasco; Andrea Caponnetto; Michele Piana; Alessandro Verri (J. Mach. Learn. Res., 2004)

Are Loss Functions All the Same?. - Lorenzo Rosasco; Ernesto De Vito; Andrea Caponnetto; Michele Piana; Alessandro Verri (Neural Comput., 2004)

Support vector algorithms as regularization networks. - Andrea Caponnetto; Lorenzo Rosasco; Francesca Odone; Alessandro Verri (ESANN, 2005)

Model Selection for Regularized Least-Squares Algorithm in Learning Theory. - Ernesto De Vito; Andrea Caponnetto; Lorenzo Rosasco (Found. Comput. Math., 2005)

Learning from Examples as an Inverse Problem. - Ernesto De Vito; Lorenzo Rosasco; Andrea Caponnetto; Umberto De Giovannini; Francesca Odone (J. Mach. Learn. Res., 2005)

Regularization approaches in learning theory. - Lorenzo Rosasco (2006)

Dimensionality reduction and generalization. - Sofia Mosci; Lorenzo Rosasco; Alessandro Verri (ICML, 2007)

On regularization algorithms in learning theory. - Frank Bauer; Sergei V. Pereverzev; Lorenzo Rosasco (J. Complex., 2007)

A method for robust variable selection with significance assessment. - Annalisa Barla; Sofia Mosci; Lorenzo Rosasco; Alessandro Verri (ESANN, 2008)

Spectral Algorithms for Supervised Learning. - L. Lo Gerfo; Lorenzo Rosasco; Francesca Odone; Ernesto De Vito; Alessandro Verri (Neural Comput., 2008)

A Note on Learning with Integral Operators. - Lorenzo Rosasco; Mikhail Belkin; Ernesto De Vito (COLT, 2009)

Towards a Theoretical Framework for Learning Multi-modal Patterns for Embodied Agents. - Nicoletta Noceti; Barbara Caputo; Claudio Castellini; Luca Baldassarre; Annalisa Barla; Lorenzo Rosasco; Francesca Odone; Giulio Sandini (ICIAP, 2009)

On Invariance in Hierarchical Models. - Jake V. Bouvrie; Lorenzo Rosasco; Tomaso A. Poggio (NIPS, 2009)

Elastic-net regularization in learning theory. - Christine De Mol; Ernesto De Vito; Lorenzo Rosasco (J. Complex., 2009)

A Primal-Dual Algorithm for Group Sparse Regularization with Overlapping Groups. - Sofia Mosci; Silvia Villa; Alessandro Verri; Lorenzo Rosasco (NIPS, 2010)

Spectral Regularization for Support Estimation. - Ernesto De Vito; Lorenzo Rosasco; Alessandro Toigo (NIPS, 2010)

Vector Field Learning via Spectral Filtering. - Luca Baldassarre; Lorenzo Rosasco; Annalisa Barla; Alessandro Verri (ECML/PKDD (1), 2010)

Solving Structured Sparsity Regularization with Proximal Methods. - Sofia Mosci; Lorenzo Rosasco; Matteo Santoro; Alessandro Verri; Silvia Villa (ECML/PKDD (2), 2010)

Mathematics of the Neural Response. - Steve Smale; Lorenzo Rosasco; Jake V. Bouvrie; Andrea Caponnetto; Tomaso A. Poggio (Found. Comput. Math., 2010)

Adaptive Kernel Methods Using the Balancing Principle. - Ernesto De Vito; Sergei V. Pereverzyev; Lorenzo Rosasco (Found. Comput. Math., 2010)

On Learning with Integral Operators. - Lorenzo Rosasco; Mikhail Belkin; Ernesto De Vito (J. Mach. Learn. Res., 2010)

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

Online Learning, Stability, and Stochastic Gradient Descent - Tomaso A. Poggio; Stephen Voinea; Lorenzo Rosasco (CoRR, 2011)

Mathematical and Computational Foundations of Learning Theory (Dagstuhl Seminar 11291). - Matthias Hein 001; Gábor Lugosi; Lorenzo Rosasco; Steve Smale (Dagstuhl Reports, 2011)

Learning Manifolds with K-Means and K-Flats. - Guillermo D. Cañas; Tomaso A. Poggio; Lorenzo Rosasco (NIPS, 2012)

Learning Probability Measures with respect to Optimal Transport Metrics. - Guillermo D. Cañas; Lorenzo Rosasco (NIPS, 2012)

Multiclass Learning with Simplex Coding. - Youssef Mroueh; Tomaso A. Poggio; Lorenzo Rosasco; Jean-Jacques E. Slotine (NIPS, 2012)

Kernels for Vector-Valued Functions: A Review. - Mauricio A. Álvarez; Lorenzo Rosasco; Neil D. Lawrence (Found. Trends Mach. Learn., 2012)

Multi-output learning via spectral filtering. - Luca Baldassarre; Lorenzo Rosasco; Annalisa Barla; Alessandro Verri (Mach. Learn., 2012)

On Learnability, Complexity and Stability. - Silvia Villa; Lorenzo Rosasco; Tomaso A. Poggio (Empirical Inference, 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 (CVPR Workshops, 2013)

On the impact of learning hierarchical representations for visual recognition in robotics. - Carlo Ciliberto; Sean Ryan Fanello; Matteo Santoro; Lorenzo Natale; Giorgio Metta; Lorenzo Rosasco (IROS, 2013)

On the Sample Complexity of Subspace Learning. - Alessandro Rudi; Guillermo D. Cañas; Lorenzo Rosasco (NIPS, 2013)

Unsupervised Learning of Invariant Representations in Hierarchical Architectures. - Fabio Anselmi; Joel Z. Leibo; Lorenzo Rosasco; Jim Mutch; Andrea Tacchetti; Tomaso A. Poggio (CoRR, 2013)

Quantization and Greed are Good: One bit Phase Retrieval, Robustness and Greedy Refinements. - Youssef Mroueh; Lorenzo Rosasco (CoRR, 2013)

q-ary Compressive Sensing - Youssef Mroueh; Lorenzo Rosasco (CoRR, 2013)

Nonparametric sparsity and regularization. - Lorenzo Rosasco; Silvia Villa; Sofia Mosci; Matteo Santoro; Alessandro Verri (J. Mach. Learn. Res., 2013)

GURLS: a least squares library for supervised learning. - Andrea Tacchetti; Pavan Kumar Mallapragada; Matteo Santoro; Lorenzo Rosasco (J. Mach. Learn. Res., 2013)

A deep representation for invariance and music classification. - Chiyuan Zhang; Georgios Evangelopoulos; Stephen Voinea; Lorenzo Rosasco; Tomaso A. Poggio (ICASSP, 2014)

Word-level invariant representations from acoustic waveforms. - Stephen Voinea; Chiyuan Zhang; Georgios Evangelopoulos; Lorenzo Rosasco; Tomaso A. Poggio (INTERSPEECH, 2014)

Phone classification by a hierarchy of invariant representation layers. - Chiyuan Zhang; Stephen Voinea; Georgios Evangelopoulos; Lorenzo Rosasco; Tomaso A. Poggio (INTERSPEECH, 2014)

Exploiting global force torque measurements for local compliance estimation in tactile arrays. - Carlo Ciliberto; Luca Fiorio; Marco Maggiali; Lorenzo Natale; Lorenzo Rosasco; Giorgio Metta; Giulio Sandini; Francesco Nori (IROS, 2014)

On efficiency and low sample complexity in phase retrieval. - Youssef Mroueh; Lorenzo Rosasco (ISIT, 2014)

Proximal methods for the latent group lasso penalty. - Silvia Villa; Lorenzo Rosasco; Sofia Mosci; Alessandro Verri (Comput. Optim. Appl., 2014)

Learning An Invariant Speech Representation. - Georgios Evangelopoulos; Stephen Voinea; Chiyuan Zhang; Lorenzo Rosasco; Tomaso A. Poggio (CoRR, 2014)

Regularization by Early Stopping for Online Learning Algorithms. - Lorenzo Rosasco; Andrea Tacchetti; Silvia Villa (CoRR, 2014)

Learning multiple visual tasks while discovering their structure. - Carlo Ciliberto; Lorenzo Rosasco; Silvia Villa (CVPR, 2015)

Convex Learning of Multiple Tasks and their Structure. - Carlo Ciliberto; Youssef Mroueh; Tomaso A. Poggio; Lorenzo Rosasco (ICML, 2015)

Teaching iCub to recognize objects using deep Convolutional Neural Networks. - Giulia Pasquale; Carlo Ciliberto; Francesca Odone; Lorenzo Rosasco; Lorenzo Natale (MLIS@ICML, 2015)

Discovering discrete subword units with binarized autoencoders and hidden-Markov-model encoders. - Leonardo Badino; Alessio Mereta; Lorenzo Rosasco (INTERSPEECH, 2015)

Discriminative template learning in group-convolutional networks for invariant speech representations. - Chiyuan Zhang; Stephen Voinea; Georgios Evangelopoulos; Lorenzo Rosasco; Tomaso A. Poggio (INTERSPEECH, 2015)

Learning with Incremental Iterative Regularization. - Lorenzo Rosasco; Silvia Villa (NIPS, 2015)

Less is More: Nyström Computational Regularization. - Alessandro Rudi; Raffaello Camoriano; Lorenzo Rosasco (NIPS, 2015)

Characterizing the Input-Output Function of the Olfactory-Limbic Pathway in the Guinea Pig. - Gian Luca Breschi; Carlo Ciliberto; Thierry Nieus; Lorenzo Rosasco; Stefano Taverna; Michela Chiappalone; Valentina Pasquale (Comput. Intell. Neurosci., 2015)

On Invariance and Selectivity in Representation Learning. - Fabio Anselmi; Lorenzo Rosasco; Tomaso A. Poggio (CoRR, 2015)

Deep Convolutional Networks are Hierarchical Kernel Machines. - Fabio Anselmi; Lorenzo Rosasco; Cheston Tan; Tomaso A. Poggio (CoRR, 2015)

Real-world Object Recognition with Off-the-shelf Deep Conv Nets: How Many Objects can iCub Learn? - Giulia Pasquale; Carlo Ciliberto; Francesca Odone; Lorenzo Rosasco; Lorenzo Natale (CoRR, 2015)

Mathematical and Computational Foundations of Learning Theory (Dagstuhl Seminar 15361). - Matthias Hein 001; Gábor Lugosi; Lorenzo Rosasco (Dagstuhl Reports, 2015)

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

NYTRO: When Subsampling Meets Early Stopping. - Raffaello Camoriano; Tomás Angles; Alessandro Rudi; Lorenzo Rosasco (AISTATS, 2016)

Combining sensory modalities and exploratory procedures to improve haptic object recognition in robotics. - Bertrand Higy; Carlo Ciliberto; Lorenzo Rosasco; Lorenzo Natale (Humanoids, 2016)

Active perception: Building objects' models using tactile exploration. - Nawid Jamali; Carlo Ciliberto; Lorenzo Rosasco; Lorenzo Natale (Humanoids, 2016)

Generalization Properties and Implicit Regularization for Multiple Passes SGM. - Junhong Lin; Raffaello Camoriano; Lorenzo Rosasco (ICML, 2016)

Incremental semiparametric inverse dynamics learning. - Raffaello Camoriano; Silvio Traversaro; Lorenzo Rosasco; Giorgio Metta; Francesco Nori (ICRA, 2016)

Object identification from few examples by improving the invariance of a Deep Convolutional Neural Network. - Giulia Pasquale; Carlo Ciliberto; Lorenzo Rosasco; Lorenzo Natale (IROS, 2016)

A Consistent Regularization Approach for Structured Prediction. - Carlo Ciliberto; Lorenzo Rosasco; Alessandro Rudi (NIPS, 2016)

Optimal Learning for Multi-pass Stochastic Gradient Methods. - Junhong Lin; Lorenzo Rosasco (NIPS, 2016)

Incremental Object Recognition in Robotics with Extension to New Classes in Constant Time. - Raffaello Camoriano; Giulia Pasquale; Carlo Ciliberto; Lorenzo Natale; Lorenzo Rosasco; Giorgio Metta (CoRR, 2016)

Generalization Properties of Learning with Random Features. - Alessandro Rudi; Raffaello Camoriano; Lorenzo Rosasco (CoRR, 2016)

Enabling Depth-Driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives. - Giulia Pasquale; Tanis Mar; Carlo Ciliberto; Lorenzo Rosasco; Lorenzo Natale (Frontiers Robotics AI, 2016)

Iterative Regularization for Learning with Convex Loss Functions. - Junhong Lin; Lorenzo Rosasco; Ding-Xuan Zhou (J. Mach. Learn. Res., 2016)

Stochastic Forward-Backward Splitting for Monotone Inclusions. - Lorenzo Rosasco; Silvia Villa; Bang Công Vu (J. Optim. Theory Appl., 2016)

Unsupervised learning of invariant representations. - Fabio Anselmi; Joel Z. Leibo; Lorenzo Rosasco; Jim Mutch; Andrea Tacchetti; Tomaso A. Poggio (Theor. Comput. Sci., 2016)

Interactive data collection for deep learning object detectors on humanoid robots. - Elisa Maiettini; Giulia Pasquale; Lorenzo Rosasco; Lorenzo Natale (Humanoids, 2017)

Incremental robot learning of new objects with fixed update time. - Raffaello Camoriano; Giulia Pasquale; Carlo Ciliberto; Lorenzo Natale; Lorenzo Rosasco; Giorgio Metta (ICRA, 2017)

Consistent Multitask Learning with Nonlinear Output Relations. - Carlo Ciliberto; Alessandro Rudi; Lorenzo Rosasco; Massimiliano Pontil (NIPS, 2017)

FALKON: An Optimal Large Scale Kernel Method. - Alessandro Rudi; Luigi Carratino; Lorenzo Rosasco (NIPS, 2017)

Generalization Properties of Learning with Random Features. - Alessandro Rudi; Lorenzo Rosasco (NIPS, 2017)

Generalization Properties of Doubly Online Learning Algorithms. - Junhong Lin; Lorenzo Rosasco (CoRR, 2017)

Don't relax: early stopping for convex regularization. - Simon Matet; Lorenzo Rosasco; Silvia Villa; Bang Long Vu (CoRR, 2017)

Optimal Rates for Learning with Nyström Stochastic Gradient Methods. - Junhong Lin; Lorenzo Rosasco (CoRR, 2017)

Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review. - Tomaso A. Poggio; Hrushikesh N. Mhaskar; Lorenzo Rosasco; Brando Miranda; Qianli Liao (Int. J. Autom. Comput., 2017)

Optimal Rates for Multi-pass Stochastic Gradient Methods. - Junhong Lin; Lorenzo Rosasco (J. Mach. Learn. Res., 2017)

Solving lp-norm regularization with tensor kernels. - Saverio Salzo; Lorenzo Rosasco; Johan A. K. Suykens (AISTATS, 2018)

Iterate Averaging as Regularization for Stochastic Gradient Descent. - Gergely Neu; Lorenzo Rosasco (COLT, 2018)

Sparse Multiple Kernel Learning: Support Identification via Mirror Stratifiability. - Guillaume Garrigos; Lorenzo Rosasco; Silvia Villa (EUSIPCO, 2018)

Constrained DMPs for Feasible Skill Learning on Humanoid Robots. - Anqing Duan; Raffaello Camoriano; Diego Ferigo; Daniele Calandriello; Lorenzo Rosasco; Daniele Pucci (Humanoids, 2018)

Speeding-Up Object Detection Training for Robotics with FALKON. - Elisa Maiettini; Giulia Pasquale; Lorenzo Rosasco; Lorenzo Natale (IROS, 2018)

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

Learning with SGD and Random Features. - Luigi Carratino; Alessandro Rudi; Lorenzo Rosasco (NeurIPS, 2018)

Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification. - Dimitrios Milios; Raffaello Camoriano; Pietro Michiardi; Lorenzo Rosasco; Maurizio Filippone (NeurIPS, 2018)

On Fast Leverage Score Sampling and Optimal Learning. - Alessandro Rudi; Daniele Calandriello; Luigi Carratino; Lorenzo Rosasco (NeurIPS, 2018)

Manifold Structured Prediction. - Alessandro Rudi; Carlo Ciliberto; Gian Maria Marconi; Lorenzo Rosasco (NeurIPS, 2018)

Modified Fejér sequences and applications. - Junhong Lin; Lorenzo Rosasco; Silvia Villa; Ding-Xuan Zhou (Comput. Optim. Appl., 2018)

Theory of Deep Learning III: explaining the non-overfitting puzzle. - Tomaso A. Poggio; Kenji Kawaguchi; Qianli Liao; Brando Miranda; Lorenzo Rosasco; Xavier Boix; Jack Hidary; Hrushikesh N. Mhaskar (CoRR, 2018)

Generalization properties of doubly stochastic learning algorithms. - Junhong Lin; Lorenzo Rosasco (J. Complex., 2018)

Iterative Regularization via Dual Diagonal Descent. - Guillaume Garrigos; Lorenzo Rosasco; Silvia Villa (J. Math. Imaging Vis., 2018)

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

A Weakly Supervised Strategy for Learning Object Detection on a Humanoid Robot. - Elisa Maiettini; Giulia Pasquale; Vadim Tikhanoff; Lorenzo Rosasco; Lorenzo Natale (Humanoids, 2019)

Genuine Personality Recognition from Highly Constrained Face Images. - Fabio Anselmi; Nicoletta Noceti; Lorenzo Rosasco; Robert Ward (ICIAP (1), 2019)

Learning to Sequence Multiple Tasks with Competing Constraints. - Anqing Duan; Raffaello Camoriano; Diego Ferigo; Yanlong Huang; Daniele Calandriello; Lorenzo Rosasco; Daniele Pucci (IROS, 2019)

Beating SGD Saturation with Tail-Averaging and Minibatching. - Nicole Mücke; Gergely Neu; Lorenzo Rosasco (NeurIPS, 2019)

Implicit Regularization of Accelerated Methods in Hilbert Spaces. - Nicolò Pagliana; Lorenzo Rosasco (NeurIPS, 2019)

A computational model for grid maps in neural populations. - Fabio Anselmi; Benedetta Franceschiello; Micah M. Murray; Lorenzo Rosasco (CoRR, 2019)

Theory III: Dynamics and Generalization in Deep Networks. - Andrzej Banburski; Qianli Liao; Brando Miranda; Lorenzo Rosasco; Bob Liang; Jack Hidary; Tomaso A. Poggio (CoRR, 2019)

Reproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion spaces. - Ernesto De Vito; Nicole Mücke; Lorenzo Rosasco (CoRR, 2019)

Multi-Scale Vector Quantization with Reconstruction Trees. - Enrico Cecini; Ernesto De Vito; Lorenzo Rosasco (CoRR, 2019)

Gain with no Pain: Efficient Kernel-PCA by Nyström Sampling. - Nicholas Sterge; Bharath K. Sriperumbudur; Lorenzo Rosasco; Alessandro Rudi (CoRR, 2019)

Symmetry-adapted representation learning. - Fabio Anselmi; Georgios Evangelopoulos; Lorenzo Rosasco; Tomaso A. Poggio (Pattern Recognit., 2019)

Are we done with object recognition? The iCub robot's perspective. - Giulia Pasquale; Carlo Ciliberto; Francesca Odone; Lorenzo Rosasco; Lorenzo Natale (Robotics Auton. Syst., 2019)

Hyperbolic Manifold Regression. - Gian Maria Marconi; Carlo Ciliberto; Lorenzo Rosasco (AISTATS, 2020)

Gain with no Pain: Efficiency of Kernel-PCA by Nyström Sampling. - Nicholas Sterge; Bharath K. Sriperumbudur; Lorenzo Rosasco; Alessandro Rudi (AISTATS, 2020)

Near-linear time Gaussian process optimization with adaptive batching and resparsification. - Daniele Calandriello; Luigi Carratino; Alessandro Lazaric; Michal Valko; Lorenzo Rosasco (ICML, 2020)

Decentralised Learning with Random Features and Distributed Gradient Descent. - Dominic Richards; Patrick Rebeschini; Lorenzo Rosasco (ICML, 2020)

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

On-line object detection: a robotics challenge. - Elisa Maiettini; Giulia Pasquale; Lorenzo Rosasco; Lorenzo Natale (Auton. Robots, 2020)

Implicit regularization for convex regularizers. - Cesare Molinari; Mathurin Massias; Lorenzo Rosasco; Silvia Villa (CoRR, 2020)

For interpolating kernel machines, the minimum norm ERM solution is the most stable. - Akshay Rangamani; Lorenzo Rosasco; Tomaso A. Poggio (CoRR, 2020)

Fast Region Proposal Learning for Object Detection for Robotics. - Federico Ceola; Elisa Maiettini; Giulia Pasquale; Lorenzo Rosasco; Lorenzo Natale (CoRR, 2020)

Data-efficient Weakly-supervised Learning for On-line Object Detection under Domain Shift in Robotics. - Elisa Maiettini; Raffaello Camoriano; Giulia Pasquale; Vadim Tikhanoff; Lorenzo Rosasco; Lorenzo Natale (CoRR, 2020)

Learning to Avoid Obstacles With Minimal Intervention Control. - Anqing Duan; Raffaello Camoriano; Diego Ferigo; Yanlong Huang; Daniele Calandriello; Lorenzo Rosasco; Daniele Pucci (Frontiers Robotics AI, 2020)

Faster Kriging: Facing High-Dimensional Simulators. - Xuefei Lu; Alessandro Rudi; Emanuele Borgonovo; Lorenzo Rosasco (Oper. Res., 2020)

A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings. - Carlo Ciliberto; Lorenzo Rosasco; Alessandro Rudi (J. Mach. Learn. Res., 2020)

Iterative regularization for convex regularizers. - Cesare Molinari; Mathurin Massias; Lorenzo Rosasco; Silvia Villa (AISTATS, 2021)

Asymptotics of Ridge(less) Regression under General Source Condition. - Dominic Richards; Jaouad Mourtada; Lorenzo Rosasco (AISTATS, 2021)

Regularized ERM on random subspaces. - Andrea Della Vecchia; Jaouad Mourtada; Ernesto De Vito; Lorenzo Rosasco (AISTATS, 2021)

Fast Object Segmentation Learning with Kernel-based Methods for Robotics. - Federico Ceola; Elisa Maiettini; Giulia Pasquale; Lorenzo Rosasco; Lorenzo Natale (ICRA, 2021)

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

Ada-BKB: Scalable Gaussian Process Optimization on Continuous Domain by Adaptive Discretization. - Marco Rando; Luigi Carratino; Silvia Villa; Lorenzo Rosasco (CoRR, 2021)

Understanding neural networks with reproducing kernel Banach spaces. - Francesca Bartolucci; Ernesto De Vito; Lorenzo Rosasco; Stefano Vigogna (CoRR, 2021)

On the Emergence of Whole-Body Strategies From Humanoid Robot Push-Recovery Learning. - Diego Ferigo; Raffaello Camoriano; Paolo Maria Viceconte; Daniele Calandriello; Silvio Traversaro; Lorenzo Rosasco; Daniele Pucci (IEEE Robotics Autom. Lett., 2021)

Structured Prediction for CRiSP Inverse Kinematics Learning With Misspecified Robot Models. - Gian Maria Marconi; Raffaello Camoriano; Lorenzo Rosasco; Carlo Ciliberto (IEEE Robotics Autom. Lett., 2021)

Accelerated Iterative Regularization via Dual Diagonal Descent. - Luca Calatroni; Guillaume Garrigos; Lorenzo Rosasco; Silvia Villa (SIAM J. Optim., 2021)

Constructing Fast Approximate Eigenspaces With Application to the Fast Graph Fourier Transforms. - Cristian Rusu; Lorenzo Rosasco (IEEE Trans. Signal Process., 2021)

Mean Nyström Embeddings for Adaptive Compressive Learning. - Antoine Chatalic; Luigi Carratino; Ernesto De Vito; Lorenzo Rosasco (AISTATS, 2022)

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

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

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

Nyström Kernel Mean Embeddings. - Antoine Chatalic; Nicolas Schreuder; Lorenzo Rosasco; Alessandro Rudi (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)

Efficient Unsupervised Learning for Plankton Images. - Paolo Didier Alfano; Marco Rando; Marco Letizia; Francesca Odone; Lorenzo Rosasco; Vito Paolo Pastore (ICPR, 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 (NeurIPS, 2022)

From Handheld to Unconstrained Object Detection: a Weakly-supervised On-line Learning Approach. - Elisa Maiettini; Andrea Maracani; Raffaello Camoriano; Giulia Pasquale; Vadim Tikhanoff; Lorenzo Rosasco; Lorenzo Natale (RO-MAN, 2022)

An elementary analysis of ridge regression with random design. - Jaouad Mourtada; Lorenzo Rosasco (CoRR, 2022)

Physics Informed Shallow Machine Learning for Wind Speed Prediction. - Daniele Lagomarsino-Oneto; Giacomo Meanti; Nicolò Pagliana; Alessandro Verri; Andrea Mazzino; Lorenzo Rosasco; Agnese Seminara (CoRR, 2022)

Learning new physics efficiently with nonparametric methods. - Marco Letizia; Gianvito Losapio; Marco Rando; Gaia Grosso; Andrea Wulzer; Maurizio Pierini; Marco Zanetti; Lorenzo Rosasco (CoRR, 2022)

Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. - Emilia Magnani; Nicholas Krämer; Runa Eschenhagen; Lorenzo Rosasco; Philipp Hennig (CoRR, 2022)

Fine-tuning or top-tuning? Transfer learning with pretrained features and fast kernel methods. - Paolo Didier Alfano; Vito Paolo Pastore; Lorenzo Rosasco; Francesca Odone (CoRR, 2022)

Iterative regularization in classification via hinge loss diagonal descent. - Vassilis Apidopoulos; Tomaso A. Poggio; Lorenzo Rosasco; Silvia Villa (CoRR, 2022)

ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots. - Paolo Maria Viceconte; Raffaello Camoriano; Giulio Romualdi; Diego Ferigo; Stefano Dafarra; Silvio Traversaro; Giuseppe Oriolo; Lorenzo Rosasco; Daniele Pucci (IEEE Robotics Autom. Lett., 2022)

Fast approximation of orthogonal matrices and application to PCA. - Cristian Rusu; Lorenzo Rosasco (Signal Process., 2022)

Learn Fast, Segment Well: Fast Object Segmentation Learning on the iCub Robot. - Federico Ceola; Elisa Maiettini; Giulia Pasquale; Giacomo Meanti; Lorenzo Rosasco; Lorenzo Natale (IEEE Trans. Robotics, 2022)

Dataset associated to the "ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots" paper (manuscript DOI: 10.1109/LRA.2022.3141658). - Paolo Maria Viceconte; Raffaello Camoriano; Giulio Romualdi; Diego Ferigo; Stefano Dafarra; Silvio Traversaro; Giuseppe Oriolo; Lorenzo Rosasco; Daniele Pucci (Zenodo, 2022)

Causal Discovery with Score Matching on Additive Models with Arbitrary Noise. - Francesco Montagna; Nicoletta Noceti; Lorenzo Rosasco; Kun Zhang 001; Francesco Locatello (CLeaR, 2023)

Scalable Causal Discovery with Score Matching. - Francesco Montagna; Nicoletta Noceti; Lorenzo Rosasco; Kun Zhang 001; Francesco Locatello (CLeaR, 2023)

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

Regularization Properties of Dual Subgradient Flow. - Vassilis Apidopoulos; Cesare Molinari; Lorenzo Rosasco; Silvia Villa (ECC, 2023)

A Grasp Pose is All You Need: Learning Multi-Fingered Grasping with Deep Reinforcement Learning from Vision and Touch. - Federico Ceola; Elisa Maiettini; Lorenzo Rosasco; Lorenzo Natale (IROS, 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 (NeurIPS, 2023)

Assumption violations in causal discovery and the robustness of score matching. - Francesco Montagna; Atalanti-Anastasia Mastakouri; Elias Eulig; Nicoletta Noceti; Lorenzo Rosasco; Dominik Janzing; Bryon Aragam; Francesco Locatello (NeurIPS, 2023)

An Optimal Structured Zeroth-order Algorithm for Non-smooth Optimization. - Marco Rando; Cesare Molinari; Lorenzo Rosasco; Silvia Villa (NeurIPS, 2023)

From inexact optimization to learning via gradient concentration. - Bernhard Stankewitz; Nicole Mücke; Lorenzo Rosasco (Comput. Optim. Appl., 2023)

Key Design Choices for Double-Transfer in Source-Free Unsupervised Domain Adaptation. - Andrea Maracani; Raffaello Camoriano; Elisa Maiettini; Davide Talon; Lorenzo Rosasco; Lorenzo Natale (CoRR, 2023)

Shortcuts for causal discovery of nonlinear models by score matching. - Francesco Montagna; Nicoletta Noceti; Lorenzo Rosasco; Francesco Locatello (CoRR, 2023)

Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling. - Antoine Chatalic; Nicolas Schreuder; Ernesto De Vito; Lorenzo Rosasco (CoRR, 2023)

Convergence of the forward-backward algorithm: beyond the worst-case with the help of geometry. - Guillaume Garrigos; Lorenzo Rosasco; Silvia Villa (Math. Program., 2023)

Zeroth-order optimization with orthogonal random directions. - David Kozak; Cesare Molinari; Lorenzo Rosasco; Luis Tenorio; Silvia Villa (Math. Program., 2023)

Fast kernel methods for data quality monitoring as a goodness-of-fit test. - Gaia Grosso; Nicolò Lai; Marco Letizia; Jacopo Pazzini; Marco Rando; Lorenzo Rosasco; Andrea Wulzer; Marco Zanetti (Mach. Learn. Sci. Technol., 2023)

Automatic Gain Tuning for Humanoid Robots Walking Architectures Using Gradient-Free Optimization Techniques. - Carlotta Sartore; Marco Rando; Giulio Romualdi; Cesare Molinari; Lorenzo Rosasco; Daniele Pucci (Humanoids, 2024)

Sim2Real Bilevel Adaptation for Object Surface Classification using Vision-Based Tactile Sensors. - Gabriele M. Caddeo; Andrea Maracani; Paolo Didier Alfano; Nicola A. Piga; Lorenzo Rosasco; Lorenzo Natale (ICRA, 2024)

Key Design Choices in Source-Free Unsupervised Domain Adaptation: An In-depth Empirical Analysis. - Andrea Maracani; Raffaello Camoriano; Elisa Maiettini; Davide Talon; Lorenzo Rosasco; Lorenzo Natale (CoRR, 2024)

Neural reproducing kernel Banach spaces and representer theorems for deep networks. - Francesca Bartolucci; Ernesto De Vito; Lorenzo Rosasco; Stefano Vigogna (CoRR, 2024)

Q-Learning to navigate turbulence without a map. - Marco Rando; Martin James; Alessandro Verri; Lorenzo Rosasco; Agnese Seminara (CoRR, 2024)

A New Formulation for Zeroth-Order Optimization of Adversarial EXEmples in Malware Detection. - Marco Rando; Luca Demetrio; Lorenzo Rosasco; Fabio Roli (CoRR, 2024)

Trust And Balance: Few Trusted Samples Pseudo-Labeling and Temperature Scaled Loss for Effective Source-Free Unsupervised Domain Adaptation. - Andrea Maracani; Lorenzo Rosasco; Lorenzo Natale (CoRR, 2024)

Optimization Insights into Deep Diagonal Linear Networks. - Hippolyte Labarrière; Cesare Molinari; Lorenzo Rosasco; Silvia Villa; Cristian Vega (CoRR, 2024)

Top-tuning: A study on transfer learning for an efficient alternative to fine tuning for image classification with fast kernel methods. - Paolo Didier Alfano; Vito Paolo Pastore; Lorenzo Rosasco; Francesca Odone (Image Vis. Comput., 2024)

RESPRECT: Speeding-up Multi-Fingered Grasping With Residual Reinforcement Learning. - Federico Ceola; Lorenzo Rosasco; Lorenzo Natale (IEEE Robotics Autom. Lett., 2024)

A structured prediction approach for robot imitation learning. - Anqing Duan; Iason Batzianoulis; Raffaello Camoriano; Lorenzo Rosasco; Daniele Pucci; Aude Billard (Int. J. Robotics Res., 2024)

Iterative regularization for low complexity regularizers. - Cesare Molinari; Mathurin Massias; Lorenzo Rosasco; Silvia Villa (Numerische Mathematik, 2024)

Stochastic zeroth order descent with structured directions. - Marco Rando; Cesare Molinari; Silvia Villa; Lorenzo Rosasco (Comput. Optim. Appl., 2024)

Towards a learning theory of representation alignment. - Francesco Insulla; Shuo Huang; Lorenzo Rosasco (ICLR, 2025)

Linear quadratic control of nonlinear systems with Koopman operator learning and the Nyström method. - Edoardo Caldarelli; Antoine Chatalic; Adrià Colomé; Cesare Molinari; Carlos Ocampo-Martinez; Carme Torras; Lorenzo Rosasco (Autom., 2025)

Learning Rhythmic Trajectories With Geometric Constraints for Laser-Based Skincare Procedures. - Anqing Duan; Wanli Liuchen; Jinsong Wu; Raffaello Camoriano; Lorenzo Rosasco; David Navarro-Alarcon (IEEE Trans. Robotics, 2025)

Q-learning with temporal memory to navigate turbulence - Environment 5. - Marco Rando; Martin James; Alessandro Verri; Lorenzo Rosasco; Agnese Seminara (Zenodo, 2025)

Q-learning with temporal memory to navigate turbulence - Environment 6 (Part 1). - Marco Rando; Martin James; Alessandro Verri; Lorenzo Rosasco; Agnese Seminara (Zenodo, 2025)

Q-learning with temporal memory to navigate turbulence - Environment 6 (Part 2). - Marco Rando; Martin James; Alessandro Verri; Lorenzo Rosasco; Agnese Seminara (Zenodo, 2025)

Learning convolution operators on compact Abelian groups. - Emilia Magnani; Ernesto De Vito; Philipp Hennig; Lorenzo Rosasco (CoRR, 2025)

Re-examining Double Descent and Scaling Laws under Norm-based Capacity via Deterministic Equivalence. - Yichen Wang; Yudong Chen 001; Lorenzo Rosasco; Fanghui Liu 001 (CoRR, 2025)

An Efficient Permutation-Based Kernel Two-Sample Test. - Antoine Chatalic; Marco Letizia; Nicolas Schreuder; Lorenzo Rosasco (CoRR, 2025)

Computational Efficiency under Covariate Shift in Kernel Ridge Regression. - Andrea Della Vecchia; Arnaud Mavakala Watusadisi; Ernesto De Vito; Lorenzo Rosasco (CoRR, 2025)

A Structured Tour of Optimization with Finite Differences. - Marco Rando; Cesare Molinari; Lorenzo Rosasco; Silvia Villa (CoRR, 2025)