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ORCIDLorenzo Rosasco
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- affiliation: MIT, Cambridge, MA, USA
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2020 – today
- 2023
[j41]Bernhard Stankewitz
, Nicole Mücke, Lorenzo Rosasco:
From inexact optimization to learning via gradient concentration. Comput. Optim. Appl. 84(1): 265-294 (2023)- [j40]Guillaume Garrigos, Lorenzo Rosasco, Silvia Villa:
Convergence of the forward-backward algorithm: beyond the worst-case with the help of geometry. Math. Program. 198(1): 937-996 (2023) - [j39]David Kozak, Cesare Molinari, Lorenzo Rosasco, Luis Tenorio, Silvia Villa:
Zeroth-order optimization with orthogonal random directions. Math. Program. 199(1): 1179-1219 (2023) - [c83]Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello:
Causal Discovery with Score Matching on Additive Models with Arbitrary Noise. CLeaR 2023: 726-751 - [c82]Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello:
Scalable Causal Discovery with Score Matching. CLeaR 2023: 752-771 - [c81]Vassilis Apidopoulos, Cesare Molinari, Lorenzo Rosasco, Silvia Villa:
Regularization Properties of Dual Subgradient Flow. ECC 2023: 1-8 - [e1]Gergely Neu, Lorenzo Rosasco:
The Thirty Sixth Annual Conference on Learning Theory, COLT 2023, 12-15 July 2023, Bangalore, India. Proceedings of Machine Learning Research 195, PMLR 2023 [contents] - [i96]Andrea Maracani, Raffaello Camoriano, Elisa Maiettini, Davide Talon, Lorenzo Rosasco, Lorenzo Natale:
Key Design Choices for Double-Transfer in Source-Free Unsupervised Domain Adaptation. CoRR abs/2302.05379 (2023) - [i95]Gaia Grosso, Nicolò Lai, Marco Letizia, Jacopo Pazzini, Marco Rando, Lorenzo Rosasco, Andrea Wulzer, Marco Zanetti:
Fast kernel methods for Data Quality Monitoring as a goodness-of-fit test. CoRR abs/2303.05413 (2023) - [i94]Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello:
Causal Discovery with Score Matching on Additive Models with Arbitrary Noise. CoRR abs/2304.03265 (2023) - [i93]Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello:
Scalable Causal Discovery with Score Matching. CoRR abs/2304.03382 (2023) - [i92]Federico Ceola, Elisa Maiettini, Lorenzo Rosasco, Lorenzo Natale:
A Grasp Pose is All You Need: Learning Multi-fingered Grasping with Deep Reinforcement Learning from Vision and Touch. CoRR abs/2306.03484 (2023) - [i91]Giacomo Meanti, Antoine Chatalic, Vladimir R. Kostic, Pietro Novelli, Massimiliano Pontil, Lorenzo Rosasco:
Estimating Koopman operators with sketching to provably learn large scale dynamical systems. CoRR abs/2306.04520 (2023) - 2022
- [j38]Paolo Maria Viceconte, Raffaello Camoriano, Giulio Romualdi, Diego Ferigo, Stefano Dafarra, Silvio Traversaro, Giuseppe Oriolo, Lorenzo Rosasco, Daniele Pucci:
ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots. IEEE Robotics Autom. Lett. 7(2): 2779-2786 (2022) - [j37]Cristian Rusu, Lorenzo Rosasco:
Fast approximation of orthogonal matrices and application to PCA. Signal Process. 194: 108451 (2022) - [j36]Federico Ceola, Elisa Maiettini, Giulia Pasquale, Giacomo Meanti, Lorenzo Rosasco, Lorenzo Natale:
Learn Fast, Segment Well: Fast Object Segmentation Learning on the iCub Robot. IEEE Trans. Robotics 38(5): 3154-3172 (2022) - [c80]Giacomo Meanti, Luigi Carratino, Ernesto De Vito, Lorenzo Rosasco:
Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression. AISTATS 2022: 6554-6572 - [c79]Marco Rando, Luigi Carratino, Silvia Villa, Lorenzo Rosasco:
Ada-BKB: Scalable Gaussian Process Optimization on Continuous Domains by Adaptive Discretization. AISTATS 2022: 7320-7348 - [c78]Antoine Chatalic, Luigi Carratino, Ernesto De Vito, Lorenzo Rosasco:
Mean Nyström Embeddings for Adaptive Compressive Learning. AISTATS 2022: 9869-9889 - [c77]Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco:
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times. ICML 2022: 2523-2541 - [c76]Antoine Chatalic, Nicolas Schreuder, Lorenzo Rosasco, Alessandro Rudi:
Nyström Kernel Mean Embeddings. ICML 2022: 3006-3024 - [c75]Stefano Vigogna, Giacomo Meanti, Ernesto De Vito, Lorenzo Rosasco:
Multiclass learning with margin: exponential rates with no bias-variance trade-off. ICML 2022: 22260-22269 - [c74]Paolo Didier Alfano, Marco Rando, Marco Letizia, Francesca Odone, Lorenzo Rosasco, Vito Paolo Pastore:
Efficient Unsupervised Learning for Plankton Images. ICPR 2022: 1314-1321 - [c73]Vladimir Kostic, Pietro Novelli, Andreas Maurer, Carlo Ciliberto, Lorenzo Rosasco, Massimiliano Pontil:
Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces. NeurIPS 2022 - [c72]Elisa Maiettini, Andrea Maracani, Raffaello Camoriano, Giulia Pasquale, Vadim Tikhanoff, Lorenzo Rosasco, Lorenzo Natale:
From Handheld to Unconstrained Object Detection: a Weakly-supervised On-line Learning Approach. RO-MAN 2022: 942-949 - [i90]Giacomo Meanti, Luigi Carratino, Ernesto De Vito, Lorenzo Rosasco:
Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression. CoRR abs/2201.06314 (2022) - [i89]Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco:
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times. CoRR abs/2201.12909 (2022) - [i88]Antoine Chatalic, Nicolas Schreuder, Alessandro Rudi, Lorenzo Rosasco:
Nyström Kernel Mean Embeddings. CoRR abs/2201.13055 (2022) - [i87]Stefano Vigogna, Giacomo Meanti, Ernesto De Vito, Lorenzo Rosasco:
Multiclass learning with margin: exponential rates with no bias-variance trade-off. CoRR abs/2202.01773 (2022) - [i86]Jaouad Mourtada, Lorenzo Rosasco:
An elementary analysis of ridge regression with random design. CoRR abs/2203.08564 (2022) - [i85]Daniele Lagomarsino-Oneto, Giacomo Meanti, Nicolò Pagliana, Alessandro Verri, Andrea Mazzino, Lorenzo Rosasco, Agnese Seminara:
Physics Informed Shallow Machine Learning for Wind Speed Prediction. CoRR abs/2204.00495 (2022) - [i84]Marco Letizia, Gianvito Losapio, Marco Rando, Gaia Grosso, Andrea Wulzer, Maurizio Pierini, Marco Zanetti, Lorenzo Rosasco:
Learning new physics efficiently with nonparametric methods. CoRR abs/2204.02317 (2022) - [i83]Vladimir Kostic, Pietro Novelli, Andreas Maurer, Carlo Ciliberto, Lorenzo Rosasco, Massimiliano Pontil:
Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces. CoRR abs/2205.14027 (2022) - [i82]Pierre Laforgue, Andrea Della Vecchia, Nicolò Cesa-Bianchi, Lorenzo Rosasco:
AdaTask: Adaptive Multitask Online Learning. CoRR abs/2205.15802 (2022) - [i81]Marco Rando, Cesare Molinari, Silvia Villa, Lorenzo Rosasco:
Stochastic Zeroth order Descent with Structured Directions. CoRR abs/2206.05124 (2022) - [i80]Federico Ceola, Elisa Maiettini, Giulia Pasquale, Giacomo Meanti, Lorenzo Rosasco, Lorenzo Natale:
Learn Fast, Segment Well: Fast Object Segmentation Learning on the iCub Robot. CoRR abs/2206.13462 (2022) - [i79]Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig:
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. CoRR abs/2208.01565 (2022) - [i78]Paolo Didier Alfano, Marco Rando, Marco Letizia, Francesca Odone, Lorenzo Rosasco, Vito Paolo Pastore:
Efficient Unsupervised Learning for Plankton Images. CoRR abs/2209.06726 (2022) - [i77]Paolo Didier Alfano, Vito Paolo Pastore, Lorenzo Rosasco, Francesca Odone:
Fine-tuning or top-tuning? Transfer learning with pretrained features and fast kernel methods. CoRR abs/2209.07932 (2022) - [i76]Vassilis Apidopoulos, Tomaso A. Poggio, Lorenzo Rosasco, Silvia Villa:
Iterative regularization in classification via hinge loss diagonal descent. CoRR abs/2212.12675 (2022) - 2021
- [j35]Gian Maria Marconi, Raffaello Camoriano, Lorenzo Rosasco, Carlo Ciliberto:
Structured Prediction for CRiSP Inverse Kinematics Learning With Misspecified Robot Models. IEEE Robotics Autom. Lett. 6(3): 5650-5657 (2021) - [j34]Diego Ferigo, Raffaello Camoriano, Paolo Maria Viceconte, Daniele Calandriello, Silvio Traversaro, Lorenzo Rosasco, Daniele Pucci:
On the Emergence of Whole-Body Strategies From Humanoid Robot Push-Recovery Learning. IEEE Robotics Autom. Lett. 6(4): 8561-8568 (2021) - [j33]Luca Calatroni, Guillaume Garrigos, Lorenzo Rosasco, Silvia Villa:
Accelerated Iterative Regularization via Dual Diagonal Descent. SIAM J. Optim. 31(1): 754-784 (2021) - [j32]Cristian Rusu, Lorenzo Rosasco:
Constructing Fast Approximate Eigenspaces With Application to the Fast Graph Fourier Transforms. IEEE Trans. Signal Process. 69: 5037-5050 (2021) - [c71]Cesare Molinari, Mathurin Massias, Lorenzo Rosasco, Silvia Villa:
Iterative regularization for convex regularizers. AISTATS 2021: 1684-1692 - [c70]Dominic Richards, Jaouad Mourtada, Lorenzo Rosasco:
Asymptotics of Ridge(less) Regression under General Source Condition. AISTATS 2021: 3889-3897 - [c69]Andrea Della Vecchia, Jaouad Mourtada, Ernesto De Vito, Lorenzo Rosasco:
Regularized ERM on random subspaces. AISTATS 2021: 4006-4014 - [c68]Federico Ceola, Elisa Maiettini, Giulia Pasquale, Lorenzo Rosasco, Lorenzo Natale:
Fast Object Segmentation Learning with Kernel-based Methods for Robotics. ICRA 2021: 13581-13588 - [c67]Luigi Carratino, Stefano Vigogna, Daniele Calandriello, Lorenzo Rosasco:
ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions. NeurIPS 2021: 6430-6441 - [i75]Gian Maria Marconi, Raffaello Camoriano, Lorenzo Rosasco, Carlo Ciliberto:
Structured Prediction for CRiSP Inverse Kinematics Learning with Misspecified Robot Models. CoRR abs/2102.12942 (2021) - [i74]Diego Ferigo, Raffaello Camoriano, Paolo Maria Viceconte, Daniele Calandriello, Silvio Traversaro, Lorenzo Rosasco, Daniele Pucci:
On the Emergence of Whole-body Strategies from Humanoid Robot Push-recovery Learning. CoRR abs/2104.14534 (2021) - [i73]Bernhard Stankewitz, Nicole Mücke, Lorenzo Rosasco:
From inexact optimization to learning via gradient concentration. CoRR abs/2106.05397 (2021) - [i72]Marco Rando, Luigi Carratino, Silvia Villa, Lorenzo Rosasco:
Ada-BKB: Scalable Gaussian Process Optimization on Continuous Domain by Adaptive Discretization. CoRR abs/2106.08598 (2021) - [i71]Luigi Carratino, Stefano Vigogna, Daniele Calandriello, Lorenzo Rosasco:
ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions. CoRR abs/2106.12231 (2021) - [i70]Francesca Bartolucci, Ernesto De Vito, Lorenzo Rosasco, Stefano Vigogna:
Understanding neural networks with reproducing kernel Banach spaces. CoRR abs/2109.09710 (2021) - [i69]Antoine Chatalic, Luigi Carratino, Ernesto De Vito, Lorenzo Rosasco:
Mean Nyström Embeddings for Adaptive Compressive Learning. CoRR abs/2110.10996 (2021) - 2020
- [j31]Elisa Maiettini, Giulia Pasquale, Lorenzo Rosasco, Lorenzo Natale:
On-line object detection: a robotics challenge. Auton. Robots 44(5): 739-757 (2020) - [j30]Anqing Duan, Raffaello Camoriano, Diego Ferigo, Yanlong Huang, Daniele Calandriello, Lorenzo Rosasco, Daniele Pucci:
Learning to Avoid Obstacles With Minimal Intervention Control. Frontiers Robotics AI 7: 60 (2020) - [j29]Xuefei Lu, Alessandro Rudi, Emanuele Borgonovo, Lorenzo Rosasco:
Faster Kriging: Facing High-Dimensional Simulators. Oper. Res. 68(1): 233-249 (2020) - [j28]Carlo Ciliberto, Lorenzo Rosasco, Alessandro Rudi:
A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings. J. Mach. Learn. Res. 21: 98:1-98:67 (2020) - [c66]Gian Maria Marconi, Carlo Ciliberto, Lorenzo Rosasco:
Hyperbolic Manifold Regression. AISTATS 2020: 2570-2580 - [c65]Nicholas Sterge, Bharath K. Sriperumbudur, Lorenzo Rosasco, Alessandro Rudi:
Gain with no Pain: Efficiency of Kernel-PCA by Nyström Sampling. AISTATS 2020: 3642-3652 - [c64]Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco:
Near-linear time Gaussian process optimization with adaptive batching and resparsification. ICML 2020: 1295-1305 - [c63]Dominic Richards, Patrick Rebeschini, Lorenzo Rosasco:
Decentralised Learning with Random Features and Distributed Gradient Descent. ICML 2020: 8105-8115 - [c62]Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro Rudi:
Kernel Methods Through the Roof: Handling Billions of Points Efficiently. NeurIPS 2020 - [i68]Carlo Ciliberto, Lorenzo Rosasco, Alessandro Rudi:
A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings. CoRR abs/2002.05424 (2020) - [i67]Cristian Rusu, Lorenzo Rosasco:
Constructing fast approximate eigenspaces with application to the fast graph Fourier transforms. CoRR abs/2002.09723 (2020) - [i66]Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco:
Near-linear Time Gaussian Process Optimization with Adaptive Batching and Resparsification. CoRR abs/2002.09954 (2020) - [i65]Gian Maria Marconi, Lorenzo Rosasco, Carlo Ciliberto:
Hyperbolic Manifold Regression. CoRR abs/2005.13885 (2020) - [i64]Cesare Molinari, Mathurin Massias, Lorenzo Rosasco, Silvia Villa:
Implicit regularization for convex regularizers. CoRR abs/2006.09859 (2020) - [i63]Andrea Della Vecchia, Jaouad Mourtada, Ernesto De Vito, Lorenzo Rosasco:
Regularized ERM on random subspaces. CoRR abs/2006.10016 (2020) - [i62]Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro Rudi:
Kernel methods through the roof: handling billions of points efficiently. CoRR abs/2006.10350 (2020) - [i61]Akshay Rangamani, Lorenzo Rosasco, Tomaso A. Poggio:
For interpolating kernel machines, the minimum norm ERM solution is the most stable. CoRR abs/2006.15522 (2020) - [i60]Dominic Richards, Patrick Rebeschini, Lorenzo Rosasco:
Decentralised Learning with Random Features and Distributed Gradient Descent. CoRR abs/2007.00360 (2020) - [i59]Federico Ceola, Elisa Maiettini, Giulia Pasquale, Lorenzo Rosasco, Lorenzo Natale:
Fast Region Proposal Learning for Object Detection for Robotics. CoRR abs/2011.12790 (2020) - [i58]Federico Ceola, Elisa Maiettini, Giulia Pasquale, Lorenzo Rosasco, Lorenzo Natale:
Fast Object Segmentation Learning with Kernel-based Methods for Robotics. CoRR abs/2011.12805 (2020) - [i57]Elisa Maiettini, Raffaello Camoriano, Giulia Pasquale, Vadim Tikhanoff, Lorenzo Rosasco, Lorenzo Natale:
Data-efficient Weakly-supervised Learning for On-line Object Detection under Domain Shift in Robotics. CoRR abs/2012.14345 (2020)
2010 – 2019
- 2019
- [j27]Fabio Anselmi, Georgios Evangelopoulos, Lorenzo Rosasco, Tomaso A. Poggio:
Symmetry-adapted representation learning. Pattern Recognit. 86: 201-208 (2019) - [j26]Giulia Pasquale, Carlo Ciliberto, Francesca Odone, Lorenzo Rosasco, Lorenzo Natale:
Are we done with object recognition? The iCub robot's perspective. Robotics Auton. Syst. 112: 260-281 (2019) - [c61]Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco:
Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret. COLT 2019: 533-557 - [c60]Elisa Maiettini, Giulia Pasquale, Vadim Tikhanoff, Lorenzo Rosasco, Lorenzo Natale:
A Weakly Supervised Strategy for Learning Object Detection on a Humanoid Robot. Humanoids 2019: 194-201 - [c59]Fabio Anselmi, Nicoletta Noceti, Lorenzo Rosasco, Robert Ward:
Genuine Personality Recognition from Highly Constrained Face Images. ICIAP (1) 2019: 421-431 - [c58]Anqing Duan, Raffaello Camoriano, Diego Ferigo, Yanlong Huang, Daniele Calandriello, Lorenzo Rosasco, Daniele Pucci:
Learning to Sequence Multiple Tasks with Competing Constraints. IROS 2019: 2672-2678 - [c57]Nicole Mücke, Gergely Neu, Lorenzo Rosasco:
Beating SGD Saturation with Tail-Averaging and Minibatching. NeurIPS 2019: 12568-12577 - [c56]Nicolò Pagliana, Lorenzo Rosasco:
Implicit Regularization of Accelerated Methods in Hilbert Spaces. NeurIPS 2019: 14454-14464 - [i56]Fabio Anselmi, Benedetta Franceschiello, Micah M. Murray, Lorenzo Rosasco:
A computational model for grid maps in neural populations. CoRR abs/1902.06553 (2019) - [i55]Nicole Mücke, Gergely Neu, Lorenzo Rosasco:
Beating SGD Saturation with Tail-Averaging and Minibatching. CoRR abs/1902.08668 (2019) - [i54]Andrzej Banburski, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Bob Liang, Jack Hidary, Tomaso A. Poggio:
Theory III: Dynamics and Generalization in Deep Networks. CoRR abs/1903.04991 (2019) - [i53]Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco:
Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret. CoRR abs/1903.05594 (2019) - [i52]Ernesto De Vito, Nicole Mücke, Lorenzo Rosasco:
Reproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion spaces. CoRR abs/1905.10913 (2019) - [i51]Nicolò Pagliana, Lorenzo Rosasco:
Implicit Regularization of Accelerated Methods in Hilbert Spaces. CoRR abs/1905.13000 (2019) - [i50]Enrico Cecini, Ernesto De Vito, Lorenzo Rosasco:
Multi-Scale Vector Quantization with Reconstruction Trees. CoRR abs/1907.03875 (2019) - [i49]Nicholas Sterge, Bharath K. Sriperumbudur, Lorenzo Rosasco, Alessandro Rudi:
Gain with no Pain: Efficient Kernel-PCA by Nyström Sampling. CoRR abs/1907.05226 (2019) - [i48]Cristian Rusu, Lorenzo Rosasco:
Fast approximation of orthogonal matrices and application to PCA. CoRR abs/1907.08697 (2019) - [i47]Daniele Calandriello, Lorenzo Rosasco:
Statistical and Computational Trade-Offs in Kernel K-Means. CoRR abs/1908.10284 (2019) - 2018
- [j25]Junhong Lin, Lorenzo Rosasco, Silvia Villa, Ding-Xuan Zhou:
Modified Fejér sequences and applications. Comput. Optim. Appl. 71(1): 95-113 (2018) - [j24]Junhong Lin, Lorenzo Rosasco:
Generalization properties of doubly stochastic learning algorithms. J. Complex. 47: 42-61 (2018) - [j23]Guillaume Garrigos, Lorenzo Rosasco, Silvia Villa:
Iterative Regularization via Dual Diagonal Descent. J. Math. Imaging Vis. 60(2): 189-215 (2018) - [c55]Saverio Salzo, Lorenzo Rosasco, Johan A. K. Suykens:
Solving lp-norm regularization with tensor kernels. AISTATS 2018: 1655-1663 - [c54]Gergely Neu, Lorenzo Rosasco:
Iterate Averaging as Regularization for Stochastic Gradient Descent. COLT 2018: 3222-3242 - [c53]Guillaume Garrigos, Lorenzo Rosasco, Silvia Villa:
Sparse Multiple Kernel Learning: Support Identification via Mirror Stratifiability. EUSIPCO 2018: 1077-1081 - [c52]Anqing Duan, Raffaello Camoriano, Diego Ferigo, Daniele Calandriello, Lorenzo Rosasco, Daniele Pucci:
Constrained DMPs for Feasible Skill Learning on Humanoid Robots. Humanoids 2018: 1-6 - [c51]Elisa Maiettini, Giulia Pasquale, Lorenzo Rosasco, Lorenzo Natale:
Speeding-Up Object Detection Training for Robotics with FALKON. IROS 2018: 5770-5776 - [c50]Alessandro Rudi, Carlo Ciliberto, Gian Maria Marconi, Lorenzo Rosasco:
Manifold Structured Prediction. NeurIPS 2018: 5615-5626 - [c49]Alessandro Rudi, Daniele Calandriello, Luigi Carratino, Lorenzo Rosasco:
On Fast Leverage Score Sampling and Optimal Learning. NeurIPS 2018: 5677-5687 - [c48]Dimitrios Milios, Raffaello Camoriano, Pietro Michiardi, Lorenzo Rosasco, Maurizio Filippone:
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification. NeurIPS 2018: 6008-6018 - [c47]Daniele Calandriello, Lorenzo Rosasco:
Statistical and Computational Trade-Offs in Kernel K-Means. NeurIPS 2018: 9379-9389 - [c46]Luigi Carratino, Alessandro Rudi, Lorenzo Rosasco:
Learning with SGD and Random Features. NeurIPS 2018: 10213-10224 - [i46]Tomaso A. Poggio, Kenji Kawaguchi, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Xavier Boix, Jack Hidary, Hrushikesh N. Mhaskar:
Theory of Deep Learning III: explaining the non-overfitting puzzle. CoRR abs/1801.00173 (2018) - [i45]Gergely Neu, Lorenzo Rosasco:
Iterate averaging as regularization for stochastic gradient descent. CoRR abs/1802.08009 (2018) - [i44]Elisa Maiettini, Giulia Pasquale, Lorenzo Rosasco, Lorenzo Natale:
Speeding-up Object Detection Training for Robotics with FALKON. CoRR abs/1803.08740 (2018) - [i43]Dimitrios Milios, Raffaello Camoriano, Pietro Michiardi, Lorenzo Rosasco, Maurizio Filippone:
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification. CoRR abs/1805.10915 (2018) - [i42]Alessandro Rudi, Carlo Ciliberto, Gian Maria Marconi, Lorenzo Rosasco:
Manifold Structured Prediction. CoRR abs/1806.09908 (2018) - [i41]Luigi Carratino, Alessandro Rudi, Lorenzo Rosasco:
Learning with SGD and Random Features. CoRR abs/1807.06343 (2018) - [i40]Alessandro Rudi, Daniele Calandriello, Luigi Carratino, Lorenzo Rosasco:
On Fast Leverage Score Sampling and Optimal Learning. CoRR abs/1810.13258 (2018) - 2017
- [j22]Tomaso A. Poggio, Hrushikesh N. Mhaskar, Lorenzo Rosasco, Brando Miranda, Qianli Liao:
Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review. Int. J. Autom. Comput. 14(5): 503-519 (2017) - [j21]Junhong Lin, Lorenzo Rosasco:
Optimal Rates for Multi-pass Stochastic Gradient Methods. J. Mach. Learn. Res. 18: 97:1-97:47 (2017) - [c45]Elisa Maiettini, Giulia Pasquale, Lorenzo Rosasco, Lorenzo Natale:
Interactive data collection for deep learning object detectors on humanoid robots. Humanoids 2017: 862-868 - [c44]Raffaello Camoriano, Giulia Pasquale, Carlo Ciliberto, Lorenzo Natale, Lorenzo Rosasco, Giorgio Metta:
Incremental robot learning of new objects with fixed update time. ICRA 2017: 3207-3214 - [c43]Carlo Ciliberto, Alessandro Rudi, Lorenzo Rosasco,