← Authors

Massimiliano Pontil

90 papers · 2005–2025 · 10 conferences · across top CS/AI conferences

Achievements

πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (11) πŸƒ Academic Marathon (20) 🌍 Conference Polyglot (10) 🐣 Hot Topic Early Bird 🧭 Keyword Pioneer 🌈 Renaissance Researcher (6) πŸ—ΊοΈ Taxonomy Completionist (30) 🌟 Keyword Trendsetter Combo (3) 🏠 Conference Loyalist (39) πŸ† Keyword Champion (6) πŸ”¬ Deep Specialist (14) 🌱 Topic Pioneer 🀝 Dynamic Duo (14) πŸ‘‘ Triple Crown πŸ’Ž Century Club (90) ❓ The Questioner ⚑ Prolific Year (9) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (14) πŸ—ƒοΈ Keyword Collector (123) πŸš€ Conference Pioneer

Conferences

NIPS (39) ICML (17) JMLR (12) AISTATS (7) COLT (4) UAI (4) ICLR (3) CVPR (2) IJCAI (1) INTERSPEECH (1)

Top co-authors

Carlo Ciliberto (14) Andreas Maurer (12) Saverio Salzo (10) Pietro Novelli (9) Riccardo Grazzi (9) Charles A. Micchelli (7) Karim Lounici (7) Luca Franceschi (6) Andreas Argyriou (6) Giulia Denevi (6)

Research topics

Statistics (1)

Keywords

multi-task learning (14) transfer learning (12) online learning (8) regret bound (7) matrix completion (7) multitask learning (6) bilevel optimization (6) kernel methods (6) hyperparameter optimization (6) optimal transport (6) representation learning (5) feature learning (4) trace norm regularization (4) structured sparsity (4) mistake bound (4) gradient descent (4) feature selection (4) reproducing kernel hilbert space (4) convex optimization (4) zero-order optimization (4)

Papers

A Bregman Proximal Viewpoint on Neural Operators ICML 2025
An Empirical Bernstein Inequality for Dependent Data in Hilbert Spaces and Applications AISTATS 2025
Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups ICML 2025
Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues ICLR 2025
Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates ICML 2024
Learning invariant representations of time-homogeneous stochastic dynamical systems ICLR 2024
Gradient-free optimization of highly smooth functions: improved analysis and a new algorithm JMLR 2024
Operator World Models for Reinforcement Learning NIPS 2024
Learning the Infinitesimal Generator of Stochastic Diffusion Processes NIPS 2024
From Biased to Unbiased Dynamics: An Infinitesimal Generator Approach NIPS 2024
Neural Conditional Probability for Uncertainty Quantification NIPS 2024
Consistent Long-Term Forecasting of Ergodic Dynamical Systems ICML 2024
Sharp Spectral Rates for Koopman Operator Learning NIPS 2023
Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start JMLR 2023
Multi-task Representation Learning with Stochastic Linear Bandits AISTATS 2023
Estimating Koopman operators with sketching to provably learn large scale dynamical systems NIPS 2023
Transfer learning for atomistic simulations using GNNs and kernel mean embeddings NIPS 2023
Distribution Regression with Sliced Wasserstein Kernels ICML 2022
Conditional Meta-Learning of Linear Representations NIPS 2022
Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces NIPS 2022
A gradient estimator via L1-randomization for online zero-order optimization with two point feedback NIPS 2022
Group Meritocratic Fairness in Linear Contextual Bandits NIPS 2022
Bregman Neural Networks ICML 2022
Batch Greenkhorn Algorithm for Entropic-Regularized Multimarginal Optimal Transport: Linear Rate of Convergence and Iteration Complexity ICML 2022
Implicit kernel meta-learning using kernel integral forms UAI 2022
Multi-source domain adaptation via weighted joint distributions optimal transport UAI 2022
A Gang of Adversarial Bandits NIPS 2021
The Role of Global Labels in Few-Shot Classification and How to Infer Them NIPS 2021
Concentration inequalities under sub-Gaussian and sub-exponential conditions NIPS 2021
Distributed Zero-Order Optimization under Adversarial Noise NIPS 2021
Multi-task and meta-learning with sparse linear bandits UAI 2021
Distance-Based Regularisation of Deep Networks for Fine-Tuning ICLR 2021
Robust Unsupervised Learning via L-statistic Minimization ICML 2021
Convergence Properties of Stochastic Hypergradients AISTATS 2021
Best Model Identification: A Rested Bandit Formulation ICML 2021
Online Parameter-Free Learning of Multiple Low Variance Tasks UAI 2020
Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits NIPS 2020
Meta-learning with Stochastic Linear Bandits ICML 2020
Fair regression via plug-in estimator and recalibration with statistical guarantees NIPS 2020
Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning NIPS 2020
On the Iteration Complexity of Hypergradient Computation ICML 2020
Estimating weighted areas under the ROC curve NIPS 2020
Fair regression with Wasserstein barycenters NIPS 2020
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning NIPS 2020
Marthe: Scheduling the Learning Rate Via Online Hypergradients IJCAI 2020
Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm NIPS 2019
Online-Within-Online Meta-Learning NIPS 2019
Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification NIPS 2019
Uniform concentration and symmetrization for weak interactions COLT 2019
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization ICML 2019
Learning Discrete Structures for Graph Neural Networks ICML 2019
Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction ICML 2019
Learning To Learn Around A Common Mean NIPS 2018
Bilevel Programming for Hyperparameter Optimization and Meta-Learning ICML 2018
Empirical bounds for functions with weak interactions COLT 2018
Bilevel learning of the Group Lasso structure NIPS 2018
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance NIPS 2018
Empirical Risk Minimization Under Fairness Constraints NIPS 2018
Consistent Multitask Learning with Nonlinear Output Relations NIPS 2017
Regret Bounds for Lifelong Learning AISTATS 2017
Forward and Reverse Gradient-Based Hyperparameter Optimization ICML 2017
A Speaker Adaptive DNN Training Approach for Speaker-Independent Acoustic Inversion INTERSPEECH 2017
The Benefit of Multitask Representation Learning JMLR 2016
Unsupervised Cross-Dataset Transfer Learning for Person Re-Identification CVPR 2016
New Perspectives on k-Support and Cluster Norms JMLR 2016
Mistake Bounds for Binary Matrix Completion NIPS 2016
Fitting Spectral Decay with the k-Support Norm AISTATS 2016
Predicting a Switching Sequence of Graph Labelings JMLR 2015
Learning With Dataset Bias in Latent Subcategory Models CVPR 2015
An Inequality with Applications to Structured Sparsity and Multitask Dictionary Learning COLT 2014
Spectral k-Support Norm Regularization NIPS 2014
Multilinear Multitask Learning ICML 2013
Excess risk bounds for multitask learning with trace norm regularization COLT 2013
A New Convex Relaxation for Tensor Completion NIPS 2013
Structured Sparsity and Generalization JMLR 2012
A General Framework for Structured Sparsity via Proximal Optimization AISTATS 2012
Optimal kernel choice for large-scale two-sample tests NIPS 2012
Exploiting Unrelated Tasks in Multi-Task Learning AISTATS 2012
A Family of Penalty Functions for Structured Sparsity NIPS 2010
On Spectral Learning JMLR 2010
When Is There a Representer Theorem? Vector Versus Matrix Regularizers JMLR 2009
Universal Multi-Task Kernels JMLR 2008
Fast Prediction on a Tree NIPS 2008
Online Prediction on Large Diameter Graphs NIPS 2008
A Spectral Regularization Framework for Multi-Task Structure Learning NIPS 2007
Prediction on a Graph with a Perceptron NIPS 2006
Multi-Task Feature Learning NIPS 2006
Stability of Randomized Learning Algorithms JMLR 2005
Learning Multiple Tasks with Kernel Methods JMLR 2005
Learning the Kernel Function via Regularization JMLR 2005