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Number of items: 24.
Article
Ciliberto, C;
(2017)
Connecting YARP to the Web with yarp.js.
Frontiers in Robotics and AI
, 4
, Article 67. 10.3389/frobt.2017.00067.
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Ciliberto, C;
Herbster, M;
Ialongo, AD;
Pontil, M;
Rocchetto, A;
Severini, S;
Wossnig, L;
(2018)
Quantum machine learning: a classical perspective.
Proceedings Of The Royal Society A: Mathematical, Physical and Engineering Sciences
, 474
(2209)
, Article 20170551. 10.1098/rspa.2017.0551.
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Ciliberto, C;
Rocchetto, A;
Rudi, A;
Wossnig, L;
(2020)
Statistical limits of supervised quantum learning.
Physical Review A
, 102
(4)
, Article 042414. 10.1103/PhysRevA.102.042414.
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Denevi, G;
Pontil, M;
Ciliberto, C;
(2021)
Conditional Meta-Learning of Linear Representations.
arXiv.org
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Goo, June Moh;
Milidonis, Xenios;
Artusi, Alessandro;
Boehm, Jan;
Ciliberto, Carlo;
(2025)
Hybrid-Segmentor: Hybrid approach for automated fine-grained crack segmentation in civil infrastructure.
Automation in Construction
, 170
, Article 105960. 10.1016/j.autcon.2024.105960.
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Luise, G;
Stamos, D;
Pontil, M;
Ciliberto, C;
(2019)
Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction.
Proceedings of the 36th International Conference on Machine Learning
, 97
pp. 4193-4202.
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Marconi, GM;
Camoriano, R;
Rosasco, L;
Ciliberto, C;
(2021)
Structured Prediction for CRISP Inverse Kinematics Learning With Misspecified Robot Models.
IEEE Robotics and Automation Letters
, 6
(3)
pp. 5650-5657.
10.1109/LRA.2021.3063978.
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Rudi, A;
Wossnig, L;
Ciliberto, C;
Rocchetto, A;
Pontil, M;
Severini, S;
(2020)
Approximating Hamiltonian dynamics with the Nyström method.
Quantum
, 4
10.22331/q-2020-02-20-234.
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Wang, R;
Ciccone, M;
Pontil, M;
Ciliberto, C;
(2025)
Schedule-Robust Continual Learning.
IEEE Transactions on Pattern Analysis and Machine Intelligence
pp. 1-13.
10.1109/TPAMI.2025.3614868.
(In press).
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Proceedings paper
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Antotsiou, Dafni;
Ciliberto, Carlo;
Kim, Tae-Kyun;
(2022)
Modular Adaptive Policy Selection for Multi-Task Imitation Learning through Task Division.
In:
2022 International Conference on Robotics and Automation (ICRA).
(pp. pp. 2459-2465).
IEEE: Philadelphia, PA, USA.
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Antotsiou, Dafni;
Ciliberto, Carlo;
Kim, Tae-Kyun;
(2021)
Adversarial Imitation Learning with Trajectorial Augmentation and Correction.
In:
Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2021.
(pp. pp. 4724-4730).
Institute of Electrical and Electronics Engineers (IEEE)
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Ciliberto, C;
Rudi, A;
Rosasco, L;
Pontil, M;
(2017)
Consistent multitask learning with nonlinear output relations.
In: Guyon, I and Luxburg, U.V. and Bengio, S and Wallach, H and Fergus, R and Vishwanathan, S and Garnett., R, (eds.)
Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017).
(pp. pp. 1987-1997).
Neural Information Processing Systems Foundation: California, Canada.
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Denevi, G;
Ciliberto, C;
Grazzi, R;
Pontil, M;
(2019)
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization.
In: Chaudhuri, Kamalika and Salakhutdinov, Ruslan, (eds.)
Proceedings of Machine Learning Research - International Conference on Machine Learning, 2019,.
PMLR: Long Beach, California, USA.
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Denevi, G;
Ciliberto, C;
Stamos, D;
Pontil, M;
(2018)
Incremental learning-to-learn with statistical guarantees.
In: Globerson, Amir and Silva, Ricardo, (eds.)
Proceedings of the Thirty-Fourth Conference (2018), Uncertainty in Artificial Intelligence.
(pp. pp. 457-466).
AUAI: California, USA.
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Denevi, G;
Ciliberto, C;
Stamos, D;
Pontil, M;
(2018)
Learning To Learn Around A Common Mean.
In: Bengio, S and Wallach, H and Larochelle, H and Grauman, K and CesaBianchi, N and Garnett, R, (eds.)
Advances in Neural Information Processing Systems 31.
NIPS Proceedings: Montréal, Canada.
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Denevi, G;
Pontil, M;
Ciliberto, C;
(2020)
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning.
In:
Proceedings of NeurIPS 2020: Thirty-fourth Conference on Neural Information Processing Systems.
Neural Information Processing Systems: Virtual conference.
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Denevi, G;
Stamos, D;
Ciliberto, C;
Pontil, M;
(2019)
Online-Within-Online Meta-Learning.
In: Wallach, H and Larochelle, H and Beygelzimer, A and d'Alche-Buc, F and Fox, E and Garnett, R, (eds.)
Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019).
(pp. pp. 1-11).
Neural Information Processing Systems (NeurIPS 2019)
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Fanello, SR;
Valentin, J;
Kowdle, A;
Rhemann, C;
Tankovich, V;
Ciliberto, C;
Davidson, P;
(2017)
Low Compute and Fully Parallel Computer Vision with HashMatch.
In:
Proceedings of the IEEE International Conference on Computer Vision (ICCV) 2017.
(pp. pp. 3894-3903).
Institute of Electrical and Electronics Engineers (IEEE)
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Luise, G;
Rudi, A;
Pontil, M;
Ciliberto, C;
(2018)
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance.
In: Bengio, S and Wallach, H and Larochelle, H and Grauman, K and CesaBianchi, N and Garnett, R, (eds.)
Advances in Neural Information Processing Systems 31 (NIPS 2018).
Neural Information Processing Systems Foundation, Inc.: Montréal, Canada.
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Luise, G;
Salzo, S;
Pontil, M;
Ciliberto, C;
(2019)
Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm.
In: Wallach, H and Larochelle, H and Beygelzimer, A and D'Alche-Buc, F and Fox, E and Garnett, R, (eds.)
Proceedings of Advances in Neural Information Processing Systems 32 (NeurIPS 2019).
NeurIPS Proceedings: Vancouver, Canada.
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Meunier, Dimitri;
Pontil, Massimiliano;
Ciliberto, Carlo;
(2022)
Distribution Regression with Sliced Wasserstein Kernels.
In: Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan, (eds.)
Proceedings of the 39th International Conference on Machine Learning.
(pp. pp. 15501-15523).
Proceedings of Machine Learning Research (PMLR): Baltimore, Maryland, USA.
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Novelli, Pietro;
Pratticò, Marco;
Pontil, Massimiliano;
Ciliberto, Carlo;
(2024)
Operator World Models for Reinforcement Learning.
In: Globersons, Amir and Mackey, Lester and Belgrave, Danielle and Fan, Angela and Paquet, Ulrich and Tomczak, Jakub M and Zhang, Cheng, (eds.)
Proceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS 2024).
(pp. pp. 1-32).
NeurIPS
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Rudi, Alessandro;
Ciliberto, Carlo;
(2021)
PSD Representations for Effective Probability Models.
In: Ranzato, M and Beygelzimer, A and Dauphin, Y and Liang, PS and Vaughan, JW, (eds.)
Advances in Neural Information Processing Systems 34 (NeurIPS 2021).
NeurIPS Proceedings: Online conference.
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Rudi, A;
Ciliberto, C;
Marconi, GM;
Rosasco, L;
(2018)
Manifold Structured Prediction.
In: Bengio, S and Wallach, H and Larochelle, H and Grauman, K and CesaBianchi, N and Garnett, R, (eds.)
Advances In Neural Information Processing Systems 31 (Nips 2018).
Neural Information Processing Systems (NIPS): Montreal, Canada.
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