Browse by UCL people
Group by: Type | Date
Number of items: 50.
Article
Alliez, P;
Cosmo, RD;
Guedj, B;
Girault, A;
Hacid, M-S;
Legrand, A;
Rougier, NP;
(2020)
Attributing and Referencing (Research) Software: Best Practices and Outlook from Inria.
Computing in Science and Engineering
, 22
(1)
pp. 39-52.
10.1109/MCSE.2019.2949413.
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Biggs, Felix;
Guedj, Benjamin;
(2021)
Differentiable PAC–Bayes Objectives with Partially Aggregated Neural Networks.
Entropy
, 23
(10)
, Article 1280. 10.3390/e23101280.
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Clerico, Eugenio;
Guedj, Benjamin;
(2024)
A note on regularised NTK dynamics with an application to PAC-Bayesian training.
Transactions on Machine Learning Research
, 2024
(04)
pp. 1-20.
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Dewez, Florent;
Guedj, Benjamin;
Talpaert, Arthur;
Vandewalle, Vincent;
(2022)
An end-to-end data-driven optimization framework for constrained trajectories.
Data-Centric Engineering
, 3
, Article e6. 10.1017/dce.2022.6.
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Dewez, F;
Guedj, B;
Vandewalle, V;
(2020)
From industry-wide parameters to aircraft-centric on-flight inference: Improving aeronautics performance prediction with machine learning.
Data-Centric Engineering
, 1
, Article e11. 10.1017/dce.2020.12.
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Guedj, B;
Pujol, L;
(2021)
Still no free lunches: the price to pay for tighter PAC-Bayes bounds.
Entropy
, 23
(11)
, Article 1529. 10.3390/e23111529.
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Guedj, B;
Desikan, BS;
(2020)
Kernel-Based Ensemble Learning in Python.
Information
, 11
(2)
, Article 63. 10.3390/info11020063.
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Guedj, B;
Desikan, BS;
(2018)
Pycobra: A python toolbox for ensemble learning and visualisation.
Journal of Machine Learning Research
, 18
(190)
pp. 1-5.
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Guedj, B;
Robbiano, S;
(2018)
PAC-Bayesian high dimensional bipartite ranking.
Journal of Statistical Planning and Inference
, 196
pp. 70-86.
10.1016/j.jspi.2017.10.010.
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Haddouche, M;
Guedj, B;
Rivasplata, O;
Shawe-Taylor, J;
(2021)
PAC-Bayes Unleashed: Generalisation Bounds with Unbounded Losses.
Entropy
, 23
(10)
, Article 1330. 10.3390/e23101330.
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Haddouche, Maxime;
Guedj, Benjamin;
(2023)
PAC-Bayes Generalisation Bounds for Heavy-Tailed Losses through Supermartingales.
Transactions on Machine Learning Research
, 2023
(4)
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Leroy, Arthur;
Latouche, Pierre;
Guedj, Benjamin;
Gey, Servane;
(2023)
Cluster-Specific Predictions with Multi-Task Gaussian Processes.
Journal of Machine Learning Research
, 24
(5)
pp. 1-49.
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Leroy, Arthur;
Latouche, Pierre;
Guedj, Benjamin;
Gey, Servane;
(2022)
MAGMA: inference and prediction using multi-task Gaussian processes with common mean.
Machine Learning
10.1007/s10994-022-06172-1.
(In press).
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Li, Le;
Guedj, Benjamin;
(2021)
Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly.
Entropy
, 23
(11)
, Article 1534. 10.3390/e23111534.
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Li, L;
Guedj, B;
Loustau, S;
(2018)
A quasi-Bayesian perspective to online clustering.
Electronic Journal of Statistics
, 12
(2)
pp. 3071-3113.
10.1214/18-EJS1479.
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Picard-Weibel, Antoine;
Capson-Tojo, Gabriel;
Guedj, Benjamin;
Moscoviz, Roman;
(2024)
Bayesian uncertainty quantification for anaerobic digestion models.
Bioresource Technology
, 394
, Article 130147. 10.1016/j.biortech.2023.130147.
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Vendeville, Antoine;
Zhou, Shi;
Guedj, Benjamin;
(2024)
Discord in the voter model for complex networks.
Physical Review E
, 109
(2)
, Article 024312. 10.1103/physreve.109.024312.
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Vendeville, A;
Guedj, B;
Zhou, S;
(2021)
Forecasting elections results via the voter model with stubborn nodes.
Applied Network Science
, 6
, Article 1.
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Zhang, JM;
Harman, M;
Guedj, B;
Barr, ET;
Shawe-Taylor, J;
(2023)
Model validation using mutated training labels: An exploratory study.
Neurocomputing
, 539
, Article 126116. 10.1016/j.neucom.2023.02.042.
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Proceedings paper
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Adams, Reuben;
Shawe-Taylor, John;
Guedj, Benjamin;
(2024)
Controlling Multiple Errors Simultaneously with a PAC-Bayes Bound.
In: Globersons, Amir and Mackey, Lester and Belgrave, Danielle and Fan, Angela and Paquet, Ulrich and Tomczak, Jakub M and Zhang, Cheng, (eds.)
Advances in Neural Information Processing Systems 37 (NeurIPS 2024).
(pp. pp. 1-30).
NeurIPS
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Biggs, F;
Guedj, B;
(2023)
Tighter PAC-Bayes Generalisation Bounds by Leveraging Example Difficulty.
In:
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics.
(pp. pp. 8165-8182).
PMLR 206: Palau de Congressos, Valencia, Spain.
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Biggs, F;
Zantedeschi, V;
Guedj, B;
(2022)
On Margins and Generalisation for Voting Classifiers.
In:
Advances in Neural Information Processing Systems.
NeurIPS
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Biggs, Felix;
Guedj, Benjamin;
(2022)
Non-Vacuous Generalisation Bounds for Shallow Neural Networks.
In:
Proceedings of the 39th International Conference on Machine Learning.
(pp. pp. 1963-1981).
MLResearchPress
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Biggs, Felix;
Guedj, Benjamin;
(2022)
On Margins and Derandomisation in PAC-Bayes.
In: Camps-Valls, Gustau and Ruiz, Francisco JR and Valera, Isabel, (eds.)
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics.
(pp. pp. 3709-3731).
Proceedings of Machine Learning Research (PMLR): Virtual conference.
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Cantelobre, Theophile;
Ciliberto, Carlo;
Guedj, Benjamin;
Rudi, Alessandro;
(2022)
Measuring dissimilarity with diffeomorphism invariance.
In: Chaudhuri, K and Jegelka, S and Song, L and Szepesvari, C and Niu, G and Sabato, S, (eds.)
Proceedings of the 39th International Conference on Machine Learning.
(pp. pp. 2572-2596).
PMLR 162
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Chérief-Abdellatif, Badr-Eddine;
Shi, Yuyang;
Doucet, Arnaud;
Guedj, Benjamin;
(2022)
On PAC-Bayesian reconstruction guarantees for VAEs.
In: Camps-Valls, Gustau and Ruiz, Francisco JR and Valera, Isabel, (eds.)
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics.
(pp. pp. 3066-2079).
Proceedings of Machine Learning Research (PMLR): Virtual conference.
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Chrétien, S;
Guedj, B;
(2021)
Revisiting clustering as matrix factorisation on the Stiefel manifold.
In: Nicosia, G and Ojha, V and La Malfa, E and Jansen, G and Sciacca, V and Pardalos, P and Giuffrida, G and Umeton, R, (eds.)
Machine Learning, Optimization, and Data Science. LOD 2020.
(pp. pp. 1-12).
Springer: Cham, Switzerland.
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Cohen-Addad, V;
Guedj, B;
Kanade, V;
Rom, G;
(2021)
Online k-means Clustering.
In: Banerjee, A and Fukumizu, K, (eds.)
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics.
(pp. pp. 1126-1134).
Proceedings of Machine Learning Research (PMLR): Virtual conference.
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Guedj, B;
(2019)
A Primer on PAC-Bayesian Learning.
In:
SMF 2018: Congrès de la Société Mathématique de France.
(pp. pp. 391-414).
Société Mathématique de France: Lille, France.
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Guedj, B;
Rengot, J;
(2020)
Non-linear Aggregation of Filters to Improve Image Denoising.
In: Arai, K and Kapoor, S and Bhatia, R, (eds.)
SAI 2020: Intelligent Computing.
(pp. pp. 314-327).
Springer: London, UK.
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Haddouche, M;
Guedj, B;
(2022)
Online PAC-Bayes Learning.
In:
Advances in Neural Information Processing Systems.
NeurIPS
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Hellström, Fredrik;
Guedj, Benjamin;
(2024)
Comparing Comparators in Generalization Bounds.
In: Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen, (eds.)
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics.
(pp. pp. 73-81).
PMLR (Proceedings of Machine Learning Research)
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Klein, J;
Albardan, M;
Guedj, B;
Colot, O;
(2019)
Decentralized Learning with Budgeted Network Load Using Gaussian Copulas and Classifier Ensembles.
In: Cellier, P and Driessens, K, (eds.)
Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Communications in Computer and Information Science.
(pp. pp. 301-316).
Springer: Cham.
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Letarte, G;
Germain, P;
Guedj, B;
Laviolette, F;
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks.
In:
Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS) 2019.
Neural Information Processing Systems (NIPS): Vancouver, Canada.
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Mhammedi, Z;
Grunwald, PD;
Guedj, B;
(2019)
PAC-Bayes Un-Expected Bernstein Inequality.
In:
Proceedings of the Thirty-third Conference on Neural Information Processing Systems 2019.
(pp. p. 9387).
NIPS: Vancouver, Canada..
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Mhammedi, Z;
Guedj, B;
Williamson, RC;
(2020)
PAC-Bayesian Bound for the Conditional Value at Risk.
In:
Proceedings of the 34th Conference on Neural Information Processing Systems.
Neural Information Processing Systems Foundation: Vancouver, Canada.
(In press).
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Nozawa, K;
Germain, P;
Guedj, B;
(2020)
PAC-Bayesian Contrastive Unsupervised Representation Learning.
In: Peters, Jonas and Sontag, David, (eds.)
Proceedings of Machine Learning Research - Conference on Uncertainty in Artificial Intelligence.
ML Research Press: Virtual.
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Perez-Ortiz, M;
Rivasplata, O;
Parrado-Hernandez, E;
Guedj, B;
Shawe-Taylor, J;
(2021)
Progress in Self-Certified Neural Networks.
In:
Advances in Neural Information Processing Systems 34 pre-proceedings (NeurIPS 2021).
NeurIPS
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Picard-Weibel, Antoine;
Moscoviz, Roman;
Guedj, Benjamin;
(2024)
Learning via Surrogate PAC-Bayes.
In: Globersons, Amir and Mackey, Lester and Belgrave, Danielle and Fan, Angela and Paquet, Ulrich and Tomczak, Jakub M and Zhang, Cheng, (eds.)
Advances in Neural Information Processing Systems (NeurIPS 2024).
NeurIPs
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Vendeville, A;
Giovanidis, A;
Papanastasiou, E;
Guedj, B;
(2023)
Opening up Echo Chambers via Optimal Content Recommendation.
In: Cherifi, H and Mantegna, RN and Rocha, LM and Cherifi, C and Miccichè, S, (eds.)
Complex Networks and Their Applications XI: Proceedings of The Eleventh International Conference on Complex Networks and Their Applications: COMPLEX NETWORKS 2022 — Volume 1.
(pp. pp. 74-85).
Springer: Cham, Switzerland.
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Vendeville, Antoine;
Guedj, Benjamin;
Zhou, Shi;
(2022)
Towards control of opinion diversity by
introducing zealots into a polarised social
group.
In: Benito, RM and Cherifi, C and Cherifi, H and Moro, E and Rocha, LM and Sales-Pardo, M, (eds.)
International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2021: Complex Networks & Their Applications X.
(pp. pp. 341-352).
Springer, Cham
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Viallard, Paul;
Haddouche, Maxime;
Simsekli, Umut;
Guedj, Benjamin;
(2023)
Learning via Wasserstein-Based High Probability Generalisation Bounds.
In: Oh, Alice and Naumann, Tristan and Globerson, Amir and Saenko, Kate and Hardt, Moritz and Levine, Sergey, (eds.)
Advances in Neural Information Processing Systems (NeurIPS 2023).
NeurIPS
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Zantedeschi, V;
Viallard, P;
Morvant, E;
Emonet, R;
Habrard, A;
Germain, P;
Guedj, B;
(2021)
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound.
In:
Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021).
Advances in Neural Information Processing Systems (NeurIPS 2021)
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Working / discussion paper
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Cantelobre, Théophile;
Guedj, Benjamin;
Pérez-Ortiz, María;
Shawe-Taylor, John;
(2020)
A PAC-Bayesian Perspective on Structured Prediction with Implicit Loss Embeddings.
ArXiv
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Haddouche, M;
Guedj, B;
Rivasplata, O;
Shawe-Taylor, J;
(2020)
Upper and Lower Bounds on the Performance of Kernel PCA.
arXiv: Ithaca, NY, USA.
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Perez-Ortiz, Maria;
Rivasplata, Omar;
Guedj, Benjamin;
Gleeson, Matthew;
Zhang, Jingyu;
Shawe-Taylor, John;
Bober, Miroslaw;
(2021)
Learning PAC-Bayes Priors for Probabilistic Neural Networks.
ArXiv: Ithaca, NY, USA.
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Picard-Weibel, Antoine;
Moscoviz, Roman;
Guedj, Benjamin;
(2024)
Learning via Surrogate PAC-Bayes.
ArXiv: Ithaca, NY, USA.
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Schrab, Antonin;
Kim, Ilmun;
Albert, Mélisande;
Laurent, Béatrice;
Guedj, Benjamin;
Gretton, Arthur;
(2022)
MMD Aggregated Two-Sample Test.
ArXiv: Ithaca, NY, USA.
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Schrab, Antonin;
Kim, Ilmun;
Guedj, Benjamin;
Gretton, Arthur;
(2022)
Efficient Aggregated Kernel Tests using Incomplete U-statistics.
ArXiv: Ithaca, NY, USA.
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Vendeville, Antoine;
Guedj, Benjamin;
Zhou, Shi;
(2022)
Depolarising Social Networks: Optimisation of Exposure to Adverse Opinions in the Presence of a Backfire Effect.
arXiv.org: Ithaca (NY), USA.
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