Taking Advantage of Sparsity in Multi-Task Learning. - Karim Lounici; Massimiliano Pontil; Alexandre B. Tsybakov; Sara A. van de Geer (COLT, 2009)

Concentration bounds for linear Monge mapping estimation and optimal transport domain adaptation. - Rémi Flamary; Karim Lounici; André Ferrari (CoRR, 2019)

Large scale Lasso with windowed active set for convolutional spike sorting. - Laurent Dragoni; Rémi Flamary; Karim Lounici; Patricia Reynaud-Bouret (CoRR, 2019)

Optimizing generalization on the train set: a novel gradient-based framework to train parameters and hyperparameters simultaneously. - Karim Lounici; Katia Meziani; Benjamin Riu (CoRR, 2020)

Muddling Labels for Regularization, a novel approach to generalization. - Karim Lounici; Katia Meziani; Benjamin Riu (CoRR, 2021)

Muddling Label Regularization: Deep Learning for Tabular Datasets. - Karim Lounici; Katia Meziani; Benjamin Riu (CoRR, 2021)

Multi-task Representation Learning with Stochastic Linear Bandits. - Leonardo Cella; Karim Lounici; Massimiliano Pontil (CoRR, 2022)

AdaCap: Adaptive Capacity control for Feed-Forward Neural Networks. - Katia Meziani; Karim Lounici; Benjamin Riu (CoRR, 2022)

Meta Representation Learning with Contextual Linear Bandits. - Leonardo Cella; Karim Lounici; Massimiliano Pontil (CoRR, 2022)

Multi-task Representation Learning with Stochastic Linear Bandits. - Leonardo Cella; Karim Lounici; Grégoire Pacreau; Massimiliano Pontil (AISTATS, 2023)

Sharp Spectral Rates for Koopman Operator Learning. - Vladimir Kostic; Karim Lounici; Pietro Novelli; Massimiliano Pontil (NeurIPS, 2023)

Robust covariance estimation with missing values and cell-wise contamination. - Grégoire Pacreau; Karim Lounici (NeurIPS, 2023)

Koopman Operator Learning: Sharp Spectral Rates and Spurious Eigenvalues. - Vladimir Kostic; Karim Lounici; Pietro Novelli; Massimiliano Pontil (CoRR, 2023)

Deep projection networks for learning time-homogeneous dynamical systems. - Vladimir R. Kostic; Pietro Novelli; Riccardo Grazzi; Karim Lounici; Massimiliano Pontil (CoRR, 2023)

Learning invariant representations of time-homogeneous stochastic dynamical systems. - Vladimir R. Kostic; Pietro Novelli; Riccardo Grazzi; Karim Lounici; Massimiliano Pontil (ICLR, 2024)

Consistent Long-Term Forecasting of Ergodic Dynamical Systems. - Vladimir R. Kostic; Karim Lounici; Prune Inzerilli; Pietro Novelli; Massimiliano Pontil (ICML, 2024)

Learning the Infinitesimal Generator of Stochastic Diffusion Processes. - Vladimir Kostic; Hélène Halconruy; Timothée Devergne; Karim Lounici; Massimiliano Pontil (NeurIPS, 2024)

Neural Conditional Probability for Uncertainty Quantification. - Vladimir Kostic; Grégoire Pacreau; Giacomo Turri; Pietro Novelli; Karim Lounici; Massimiliano Pontil (NeurIPS, 2024)

Contextual Continuum Bandits: Static Versus Dynamic Regret. - Arya Akhavan; Karim Lounici; Massimiliano Pontil; Alexandre B. Tsybakov (CoRR, 2024)

Neural Conditional Probability for Inference. - Vladimir R. Kostic; Karim Lounici; Grégoire Pacreau; Pietro Novelli; Giacomo Turri; Massimiliano Pontil (CoRR, 2024)

Multi-Source and Test-Time Domain Adaptation on Multivariate Signals using Spatio-Temporal Monge Alignment. - Théo Gnassounou; Antoine Collas; Rémi Flamary; Karim Lounici; Alexandre Gramfort (CoRR, 2024)

Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups. - Vladimir R. Kostic; Karim Lounici; Hélène Halconruy; Timothee Devergne; Pietro Novelli; Massimiliano Pontil (CoRR, 2024)

CoHiRF: A Scalable and Interpretable Clustering Framework for High-Dimensional Data. - Bruno Belucci; Karim Lounici; Katia Meziani (CoRR, 2025)

Equivariant Representation Learning for Symmetry-Aware Inference with Guarantees. - Daniel Felipe Ordoñez Apraez; Vladimir Kostic; Alek Fröhlich; Vivien Brandt; Karim Lounici; Massimiliano Pontil (CoRR, 2025)