Browse by UCL people
Group by: Type | Date
Number of items: 66.
Article
Akhavan, A;
Pontil, M;
Tsybakov, AB;
(2021)
Distributed Zero-Order Optimization under Adversarial Noise.
Advances in Neural Information Processing Systems
, 13
pp. 10209-10220.
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Baldassarre, L;
Pontil, M;
Mourão-Miranda, J;
(2017)
Sparsity Is Better with Stability: Combining Accuracy and Stability for Model Selection in Brain Decoding.
Front Neurosci
, 11
p. 62.
10.3389/fnins.2017.00062.
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Cavallo, A;
Romeo, L;
Ansuini, C;
Podda, J;
Battaglia, F;
Veneselli, E;
Pontil, M;
(2018)
Prospective motor control obeys to idiosyncratic strategies in autism.
Scientific Reports
, 8
, Article 13717. 10.1038/s41598-018-31479-2.
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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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Donini, M;
Monteiro, JM;
Pontil, M;
Hahn, T;
Fallgatter, AJ;
Shawe-Taylor, J;
Mourão-Miranda, J;
(2019)
Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important.
Neuroimage
, 195
pp. 215-231.
10.1016/j.neuroimage.2019.01.053.
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Fiorucci, M;
Khoroshiltseva, M;
Pontil, M;
Traviglia, A;
Del Bue, A;
James, S;
(2020)
Machine Learning for Cultural Heritage: A Survey.
Pattern Recognition Letters
, 133
pp. 102-108.
10.1016/j.patrec.2020.02.017.
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Koul, A;
Cavallo, A;
Cauda, F;
Costa, T;
Diano, M;
Pontil, M;
Becchio, C;
(2018)
Action Observation Areas Represent Intentions From Subtle Kinematic Features.
Cerebral Cortex
, 28
(7)
pp. 2647-2654.
10.1093/cercor/bhy098.
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Lise, S;
Archambeau, C;
Pontil, M;
Jones, DT;
(2009)
Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods.
BMC Bioinformatics
, 10
, Article 365. 10.1186/1471-2105-10-365.
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Lise, S;
Buchan, D;
Pontil, M;
Jones, DT;
(2011)
Predictions of Hot Spot Residues at Protein-Protein Interfaces Using Support Vector Machines.
PLOS ONE
, 6
(2)
, Article e16774. 10.1371/journal.pone.0016774.
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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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Magaña, OAV;
Barasuol, V;
Camurri, M;
Franceschi, L;
Focchi, M;
Pontil, M;
Caldwell, DG;
(2019)
Fast and Continuous Foothold Adaptation for Dynamic Locomotion Through CNNs.
IEEE Robotics and Automation Letters
, 4
(2)
pp. 2140-2147.
10.1109/LRA.2019.2899434.
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Maurer, A;
Pontil, M;
Romera-Paredes, B;
(2016)
The benefit of multitask representation learning.
Journal of Machine Learning Research
, 17
(81)
pp. 1-32.
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Noulas, A;
Scellato, S;
Lambiotte, R;
Pontil, M;
Mascolo, C;
(2012)
A Tale of Many Cities: Universal Patterns in Human Urban Mobility.
PLOS ONE
, 7
(5)
, Article e37027. 10.1371/journal.pone.0037027.
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Oneto, L;
Donini, M;
Pontil, M;
Shawe-Taylor, J;
(2020)
Randomized learning and generalization of fair and private classifiers: From PAC-Bayes to stability and differential privacy.
Neurocomputing
, 416
pp. 231-243.
10.1016/j.neucom.2019.12.137.
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Peng, P;
Tian, Y;
Xiang, T;
Wang, Y;
Pontil, M;
Huang, T;
(2018)
Joint Semantic and Latent Attribute Modelling for Cross-Class Transfer Learning.
IEEE Transactions on Pattern Analysis and Machine Intelligence
, 40
(7)
pp. 1625-1638.
10.1109/TPAMI.2017.2723882.
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Romeo, L;
Cavallo, A;
Pepa, L;
Berthouze, N;
Pontil, M;
(2019)
Multiple Instance Learning for Emotion Recognition using Physiological Signals.
IEEE Transactions on Affective Computing
10.1109/taffc.2019.2954118.
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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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Tremmel, Christoph;
Fernandez-Vargas, Jacobo;
Stamos, Dimitris;
Cinel, Caterina;
Pontil, Massimiliano;
Citi, Luca;
Poli, Riccardo;
(2022)
A meta-learning BCI for estimating decision confidence.
Journal of Neural Engineering
, 19
(4)
, Article 046009. 10.1088/1741-2552/ac7ba8.
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Turri, G;
Cavallo, A;
Romeo, L;
Pontil, M;
Sanfey, A;
Panzeri, S;
Becchio, C;
(2022)
Decoding social decisions from movement kinematics.
iScience
, 25
(12)
, Article 105550. 10.1016/j.isci.2022.105550.
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Wang, Ruohan;
Falk, John Isak Texas;
Pontil, Massimiliano;
Ciliberto, Carlo;
(2024)
Robust Meta-Representation Learning via Global Label Inference and Classification.
IEEE Transactions on Pattern Analysis and Machine Intelligence
, 46
(4)
pp. 1996-2010.
10.1109/TPAMI.2023.3328184.
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Proceedings paper
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Akhavan, A;
Chzhen, E;
Pontil, M;
Tsybakov, AB;
(2022)
A gradient estimator via L1-randomization for online zero-order optimization with two point feedback.
In:
Advances in Neural Information Processing Systems.
(pp. pp. 2-12).
Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS 2022): 36th Conference on Neural Information Processing Systems (NeurIPS 2022).
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Akhavan, A;
Pontil, M;
Tsybakov, AB;
(2020)
Exploiting higher order smoothness in derivative-free optimization and continuous bandits.
In:
NIPS'20: Proceedings of the 34th International Conference on Neural Information Processing Systems.
(pp. pp. 9017-9027).
ACM
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Alquier, P;
Mai, TT;
Pontil, M;
(2017)
Regret Bounds for Lifelong Learning.
In: Singh, A and Zhu, XJ, (eds.)
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS 2017).
(pp. pp. 261-269).
PMLR (Proceedings of Machine Learning Research): Fort Lauderdale, FL, USA.
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Badino, L;
Franceschi, L;
Arora, R;
Donini, M;
Pontil, M;
(2017)
A speaker adaptive DNN training approach for speaker-independent acoustic inversion.
In: Lacerda, F, (ed.)
Proceedings of Interspeech 2017.
(pp. pp. 984-988).
International Speech Communication Association (ISCA): Stockholm, Sweden.
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Cella, L;
Lounici, K;
Pacreau, G;
Pontil, M;
(2023)
Multi-task Representation Learning with Stochastic Linear Bandits.
In:
Proceedings of Machine Learning Research (PMLR).
(pp. pp. 4822-4847).
MLResearchPress
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Cella, L;
Pontil, M;
(2021)
Multi-Task and Meta-Learning with Sparse Linear Bandits.
In:
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence.
(pp. pp. 1692-1702).
PMLR
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Cella, Leonardo;
Pontil, Massimiliano;
Gentile, Claudio;
(2021)
Best Model Identification: A Rested Bandit Formulation.
In: Meila, M and Zhang, T, (eds.)
Proceedings of the 38th International Conference on Machine Learning.
(pp. pp. 1-11).
PMLR
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Cella, L;
Lazaric, A;
Pontil, M;
(2020)
Meta-learning with Stochastic Linear Bandits.
In: Daumé III, H and Singh, A, (eds.)
Proceedings of the 37th International Conference on Machine Learning.
(pp. pp. 1337-1347).
Proceedings of Machine Learning Research (PMLR): Virtual conference.
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Chzhen, E;
Denis, C;
Hebiri, M;
Oneto, L;
Pontil, M;
(2020)
Fair regression with wasserstein barycenters.
In:
Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS 2020).
(pp. pp. 1-11).
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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;
Pontil, M;
Ciliberto, C;
(2022)
Conditional Meta-Learning of Linear Representations.
In:
Advances in Neural Information Processing Systems.
NeurIPS
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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;
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;
Pontil, M;
Stamos, D;
(2020)
Online Parameter-Free Learning of Multiple Low Variance Tasks.
In: Adams, RP and Gogate, V, (eds.)
Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI).
AUAI Press: 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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Donini, M;
Martinez-Rego, D;
Goodson, M;
Shawe-Taylor, J;
Pontil, M;
(2016)
Distributed variance regularized Multitask Learning.
In:
2016 International Joint Conference on Neural Networks (IJCNN).
(pp. pp. 3101-3109).
IEEE
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Donini, M;
Monteiro, JM;
Pontil, M;
Shawe-Taylor, J;
Mourao-Miranda, J;
(2016)
A multimodal multiple kernel learning approach to Alzheimer's disease detection.
In:
Proceedings of the 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).
IEEE: Vietri sul Mare, Italy.
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Donini, M;
Oneto, L;
Ben-David, S;
Shawe-Taylor, J;
Pontil, M;
(2018)
Empirical Risk Minimization Under Fairness Constraints.
In: Bengio, S and Wallach, H and Larochelle, H and Grauman, K and CesaBianchi, N and Garnett, R, (eds.)
Proceedings of the 32nd Conference on Neural Information Processing Systems.
Neural Information Processing Systems (NIPS): Montreal, Canada.
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Falk, JIT;
Ciliberto, C;
Pontil, M;
(2022)
Implicit Kernel Meta-Learning Using Kernel Integral Forms.
In:
Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022.
(pp. pp. 652-662).
PMLR 180
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Franceschi, L;
Donini, M;
Frasconi, P;
Pontil, M;
(2017)
Forward and Reverse Gradient-Based Hyperparameter Optimization.
In: Precup, D and Teh, YW, (eds.)
Proceedings of the 34th International Conference on Machine Learning 2017.
(pp. pp. 1165-1173).
JMLR: Sydney, Australia.
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Franceschi, L;
Frasconi, P;
Salzo, S;
Grazzi, R;
Pontil, M;
(2018)
Bilevel Programming for Hyperparameter Optimization and Meta-Learning.
In: Dy, JG and Krause, A, (eds.)
Proceedings of the 25th International Conference on Machine Learning (2018).
(pp. pp. 1563-1572).
PMLR (Proceedings of Machine Learning Research): Stockholm.
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Franceschi, L;
Niepert, M;
Pontil, M;
He, X;
(2019)
Learning Discrete Structures for Graph Neural Networks.
In: Chaudhuri,, Kamalika and Salakhutdinov, Ruslan, (eds.)
Proceedings of International Conference on Machine Learning - 2019.
PMLR: Long Beach, California, USA.
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Frecon, Jordan;
Gasso, Gilles;
Pontil, Massimiliano;
Salzo, Saverio;
(2022)
Bregman Neural Networks.
In:
Proceedings of Machine Learning Research.
(pp. pp. 6779-6792).
Proceedings of Machine Learning Research (PMLR)
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Gouk, Henry;
Hospedales, Timothy M;
Pontil, Massimiliano;
(2021)
Distance-Based Regularisation of Deep Networks for Fine-Tuning.
In:
Proceedings of the International Conference on Learning Representations ICLR 2021.
ICLR
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Grazzi, R;
Akhavan, A;
Falk, JIT;
Cella, L;
Pontil, M;
(2023)
Group Meritocratic Fairness in Linear Contextual Bandits.
In:
Advances in Neural Information Processing Systems.
NIPS
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Grazzi, Riccardo;
Pontil, Massimiliano;
Salzo, Saverio;
(2021)
Convergence Properties of Stochastic Hypergradients.
In: Banerjee, Arindam and Fukumizu, Kenji, (eds.)
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics.
(pp. pp. 3826-3834).
PMLR 130
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Grazzi, R;
Franceschi, L;
Pontil, M;
Salzo, S;
(2020)
On the Iteration Complexity of Hypergradient Computation.
In: Daumé III, H and Singh, A, (eds.)
Proceedings of the 37th International Conference on Machine Learning.
(pp. pp. 3706-3716).
Proceedings of Machine Learning Research (PMLR): Virtual conference.
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Herbster, M;
Pasteris, S;
Vitale, F;
Pontil, M;
(2021)
A Gang of Adversarial Bandits.
In: Ranzato, M and Beygelzimer, A and Dauphin, Y and Liang, PS and Wortman Vaughan, J, (eds.)
Advances in Neural Information Processing Systems 34.
(pp. pp. 2265-2279).
NeurIPS
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Herbster, M;
Lever, G;
Pontil, M;
(2008)
Online prediction on large diameter graphs.
In: Koller, D and Schuurmans, D and Bengio, Y and Bottou, B, (eds.)
Advances in Neural Information Processing Systems 21 (NIPS 2008).
(pp. pp. 649-656).
Neural Information Processing Systems Foundation
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Herbster, M.;
Pontil, M.;
Wainder, L.;
(2005)
Online learning over graphs.
In: Dzeroski, S. and De Raedt, L. and Wrobel, S., (eds.)
Proceedings of the 22nd International Conference on Machine Learning (ICML 05).
(pp. pp. 305-312).
ACM Press: New York, NY, USA.
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Herbster, MJ;
Pasteris, S;
Pontil, M;
(2016)
Mistake Bounds for Binary Matrix Completion.
In: Lee, DD and Sugiyama, M and Luxburg, UV and Guyon, I and Garnett, R and Garnett, R, (eds.)
Proceedings of the 29th Conference on Neural Information Processing Systems (NIPS 2016).
NIPS Proceedings: Barcelona, Spain.
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Kostic, Vladimir R;
Salzo, Saverio;
Pontil, Massimiliano;
(2022)
Batch Greenkhorn Algorithm for Entropic-Regularized Multimarginal Optimal Transport: Linear Rate of Convergence and Iteration Complexity.
In: Chaudhuri, K and Jegelka, S and Song, L and Szepesvari, C and Niu, G and Sabato, S, (eds.)
Proceedings of Machine Learning Research.
(pp. pp. 11529-11558).
Proceedings of Machine Learning Research (PMLR)
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Kostic, VR;
Maurer, A;
Rosasco, L;
Novelli, P;
Ciliberto, C;
Pontil, M;
(2022)
Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces.
In:
Advances in Neural Information Processing Systems.
NeurIPS
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Lounici, K;
Pontil, M;
Tsybakov, AB;
Van De Geer, SA;
(2009)
Taking advantage of sparsity in multi-task learning.
In:
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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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Maurer, A;
Pontil, M;
(2021)
Concentration inequalities under sub-Gaussian and sub-exponential conditions.
In: Ranzato, M and Beygelzimer, A and Dauphin, Y and Liang, PS and Wortman Vaughan, J, (eds.)
Advances in Neural Information Processing Systems.
(pp. pp. 7588-7597).
NeurIPS
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Maurer, Andreas;
Parletta, Daniela Angela;
Paudice, Andrea;
Pontil, Massimiliano;
(2021)
Robust Unsupervised Learning via L-statistic Minimization.
In: Meila, Marina and Zhang, Tong, (eds.)
Proceedings of the 38th International Conference on Machine Learning.
(pp. pp. 7524-7533).
PMLR
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Maurer, A;
Pontil, M;
(2018)
Empirical bounds for functions with weak interactions.
In: Bubeck, S and Perchet, V and Rigollet, P, (eds.)
Proceedings of the 31st Annual Conference on Learning Theory (COLT 2018).
(pp. pp. 987-1010).
PMLR (Proceedings of Machine Learning Research): Stockholm.
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McDonald, AM;
Pontil, M;
Stamos, D;
(2016)
Fitting Spectral Decay with the k-Support Norm.
In: Gretton, A and Robert, CC, (eds.)
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics.
(pp. pp. 1061-1069).
Journal of Machine Learning Research
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Turrisi, R;
Flamary, R;
Rakotomamonjy, A;
Pontil, M;
(2022)
Multi-source Domain Adaptation via Weighted Joint Distributions Optimal Transport.
In:
Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022.
(pp. pp. 1970-1980).
PMLR 180
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Wang, R;
Pontil, M;
Ciliberto, C;
(2021)
The Role of Global Labels in Few-Shot Classification and How to Infer Them.
In:
Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021).
(pp. pp. 27160-27170).
NeurIPS
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Conference item
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Herbster, MJ;
Pontil M, L;
Rojas Galeano, S;
(2008)
Fast Prediction on a Tree.
Presented at: NIPS 2008: Twenty-Second Annual Conference on Neural Information Processing Systems, Vancouver, Canada.
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Thesis
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Pontil, M;
(1999)
Study and Application of Statistical Learning Theory.
Doctoral thesis , UNSPECIFIED.
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Pontil, M;
(1994)
Computation of Feynman Diagrams with MATHEMATICA.
Masters thesis , UNSPECIFIED.
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