Daniele Calandriello

发表

Statistical and Computational Trade-Offs in Kernel K-Means

Daniele Calandriello, Lorenzo Rosasco, L. Rosasco, 2019, NeurIPS.

Pack only the essentials: Adaptive dictionary learning for kernel ridge regression

Daniele Calandriello, Alessandro Lazaric, Michal Valko, 2016, NIPS 2016.

Analysis of Kelner and Levin graph sparsification algorithm for a streaming setting pdf

Daniele Calandriello, Alessandro Lazaric, Michal Valko, 2016, ArXiv.

Physically Interactive Robogames: Definition and design guidelines

Daniele Calandriello, Andrea Bonarini, Diego Martinoia, 2013, Robotics Auton. Syst..

Analysis of Nyström method with sequential ridge leverage scores

Daniele Calandriello, Alessandro Lazaric, Michal Valko, 2016, UAI.

Sparse multi-task reinforcement learning

Daniele Calandriello, Alessandro Lazaric, Marcello Restelli, 2014, Intelligenza Artificiale.

Constrained DMPs for Feasible Skill Learning on Humanoid Robots

Daniele Pucci, Daniele Calandriello, Lorenzo Rosasco, 2018, 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids).

Exact sampling of determinantal point processes with sublinear time preprocessing

Daniele Calandriello, Michal Valko, Michal Derezinski, 2019, NeurIPS.

Distributed Adaptive Sampling for Kernel Matrix Approximation pdf

Daniele Calandriello, Alessandro Lazaric, Michal Valko, 2017, AISTATS.

Sampling from a k-DPP without looking at all items pdf

Daniele Calandriello, Michal Valko, Michal Derezi'nski, 2020, NeurIPS.

Learning to Sequence Multiple Tasks with Competing Constraints

Daniele Pucci, Daniele Calandriello, Yanlong Huang, 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Second-Order Kernel Online Convex Optimization with Adaptive Sketching

Daniele Calandriello, Alessandro Lazaric, Michal Valko, 2017, ICML.

Efficient Second-Order Online Kernel Learning with Adaptive Embedding

Daniele Calandriello, Alessandro Lazaric, Michal Valko, 2017, NIPS.

Near-linear Time Gaussian Process Optimization with Adaptive Batching and Resparsification

Daniele Calandriello, Lorenzo Rosasco, Luigi Carratino, 2020, ICML.

Incremental Spectral Sparsification for Large-Scale Graph-Based Semi-Supervised Learning pdf

Daniele Calandriello, Alessandro Lazaric, Michal Valko, 2016, ArXiv.

Efficient Sequential Learning in Structured and Constrained Environments. (Apprentissage séquentiel efficace dans des environnements structurés avec contraintes)

Daniele Calandriello, Daniele Calandriello, 2017 .

Safe Policy Iteration

Daniele Calandriello, Marcello Restelli, Alessio Pecorino, 2013, ICML.

Semi-supervised information-maximization clustering

Gang Niu, Masashi Sugiyama, Daniele Calandriello, 2014, Neural Networks.

Improved Large-Scale Graph Learning through Ridge Spectral Sparsification

Daniele Calandriello, Alessandro Lazaric, Michal Valko, 2018, ICML.

Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret pdf

Daniele Calandriello, Lorenzo Rosasco, Luigi Carratino, 2019, COLT.

On Fast Leverage Score Sampling and Optimal Learning

Daniele Calandriello, Lorenzo Rosasco, Luigi Carratino, 2018, NeurIPS.

Learning to Avoid Obstacles With Minimal Intervention Control

Daniele Pucci, Daniele Calandriello, Lorenzo Rosasco, 2020, Frontiers in Robotics and AI.

ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions pdf

Daniele Calandriello, Lorenzo Rosasco, Luigi Carratino, 2021, ArXiv.

One Pass ImageNet pdf

Dilan Görür, Daniele Calandriello, Huiyi Hu, 2021, ArXiv.

On the Emergence of Whole-Body Strategies From Humanoid Robot Push-Recovery Learning

Daniele Pucci, Daniele Calandriello, Lorenzo Rosasco, 2021, IEEE Robotics and Automation Letters.

Analysis of Nyström method with sequential ridge leverage score sampling

Daniele Calandriello, Alessandro Lazaric, Michal Valko, 2016, UAI 2016.

Large-scale semi-supervised learning with online spectral graph sparsification

Daniele Calandriello, Alessandro Lazaric, Michal Valko, 2015, ICML 2015.