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07/19/2023
Deep projection networks for learning time-homogeneous dynamical systems
We consider the general class of time-homogeneous dynamical systems, bot...
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06/07/2023
Estimating Koopman operators with sketching to provably learn large scale dynamical systems
The theory of Koopman operators allows to deploy non-parametric machine ...
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06/03/2023
Gradient-free optimization of highly smooth functions: improved analysis and a new algorithm
This work studies minimization problems with zero-order noisy oracle inf...
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06/02/2023
Transfer learning for atomistic simulations using GNNs and kernel mean embeddings
Interatomic potentials learned using machine learning methods have been ...
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02/03/2023
Koopman Operator Learning: Sharp Spectral Rates and Spurious Eigenvalues
Non-linear dynamical systems can be handily described by the associated ...
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12/22/2022
Robust Meta-Representation Learning via Global Label Inference and Classification
Few-shot learning (FSL) is a central problem in meta-learning, where lea...
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08/17/2022
High Probability Bounds for Stochastic Subgradient Schemes with Heavy Tailed Noise
In this work we study high probability bounds for stochastic subgradient...
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06/07/2022
Group Meritocratic Fairness in Linear Contextual Bandits
We study the linear contextual bandit problem where an agent has to sele...
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05/30/2022
Meta Representation Learning with Contextual Linear Bandits
Meta-learning seeks to build algorithms that rapidly learn how to solve ...
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05/27/2022
Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces
We study a class of dynamical systems modelled as Markov chains that adm...
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05/27/2022
A gradient estimator via L1-randomization for online zero-order optimization with two point feedback
This work studies online zero-order optimization of convex and Lipschitz...
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04/15/2022
Characterizing metastable states with the help of machine learning
Present-day atomistic simulations generate long trajectories of ever mor...
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02/21/2022
Multi-task Representation Learning with Stochastic Linear Bandits
We study the problem of transfer-learning in the setting of stochastic l...
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02/08/2022
Distribution Regression with Sliced Wasserstein Kernels
The problem of learning functions over spaces of probabilities - or dist...
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02/07/2022
Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start
We analyze a general class of bilevel problems, in which the upper-level...
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08/09/2021
The Role of Global Labels in Few-Shot Classification and How to Infer Them
Few-shot learning (FSL) is a central problem in meta-learning, where lea...
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06/04/2021
Multitask Online Mirror Descent
We introduce and analyze MT-OMD, a multitask generalization of Online Mi...
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03/30/2021
Conditional Meta-Learning of Linear Representations
Standard meta-learning for representation learning aims to find a common...
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02/11/2021
Some Hoeffding- and Bernstein-type Concentration Inequalities
We prove concentration inequalities for functions of independent random ...
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02/01/2021
Distributed Zero-Order Optimization under Adversarial Noise
We study the problem of distributed zero-order optimization for a class ...
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12/14/2020
A Perturbation Resilient Framework for Unsupervised Learning
Designing learning algorithms that are resistant to perturbations of the...
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12/07/2020
Online Model Selection: a Rested Bandit Formulation
Motivated by a natural problem in online model selection with bandit inf...
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11/13/2020
Convergence Properties of Stochastic Hypergradients
Bilevel optimization problems are receiving increasing attention in mach...
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08/25/2020
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning
Biased regularization and fine-tuning are two recent meta-learning appro...
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07/29/2020
Generalization Properties of Optimal Transport GANs with Latent Distribution Learning
The Generative Adversarial Networks (GAN) framework is a well-establishe...
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07/11/2020
Online Parameter-Free Learning of Multiple Low Variance Tasks
We propose a method to learn a common bias vector for a growing sequence...
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06/29/2020
On the Iteration Complexity of Hypergradient Computation
We study a general class of bilevel problems, consisting in the minimiza...
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06/23/2020
Multi-source Domain Adaptation via Weighted Joint Distributions Optimal Transport
The problem of domain adaptation on an unlabeled target dataset using kn...
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06/14/2020
Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits
We study the problem of zero-order optimization of a strongly convex fun...
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06/12/2020
Fair Regression with Wasserstein Barycenters
We study the problem of learning a real-valued function that satisfies t...
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05/18/2020
Meta-learning with Stochastic Linear Bandits
We investigate meta-learning procedures in the setting of stochastic lin...
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03/23/2020
Efficient Tensor Kernel methods for sparse regression
Recently, classical kernel methods have been extended by the introductio...
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02/19/2020
Distance-Based Regularisation of Deep Networks for Fine-Tuning
We investigate approaches to regularisation during fine-tuning of deep n...
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10/18/2019
Scheduling the Learning Rate via Hypergradients: New Insights and a New Algorithm
We study the problem of fitting task-specific learning rate schedules fr...
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06/25/2019
Learning Fair and Transferable Representations
Developing learning methods which do not discriminate subgroups in the p...
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06/12/2019
Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification
We study the problem of fair binary classification using the notion of E...
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05/30/2019
Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm
We present a novel algorithm to estimate the barycenter of arbitrary pro...
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03/28/2019
Learning Discrete Structures for Graph Neural Networks
Graph neural networks (GNNs) are a popular class of machine learning mod...
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03/25/2019
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization
We study the problem of learning-to-learn: inferring a learning algorith...
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03/02/2019
Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction
We study the interplay between surrogate methods for structured predicti...
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02/05/2019
Uniform concentration and symmetrization for weak interactions
The method to derive uniform bounds with Gaussian and Rademacher complex...
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01/29/2019
General Fair Empirical Risk Minimization
We tackle the problem of algorithmic fairness, where the goal is to avoi...
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10/19/2018
Taking Advantage of Multitask Learning for Fair Classification
A central goal of algorithmic fairness is to reduce bias in automated de...
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09/25/2018
Fast and Continuous Foothold Adaptation for Dynamic Locomotion through Convolutional Neural Networks
Legged robots can outperform wheeled machines for most navigation tasks ...
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06/13/2018
Far-HO: A Bilevel Programming Package for Hyperparameter Optimization and Meta-Learning
In (Franceschi et al., 2018) we proposed a unified mathematical framewor...
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05/30/2018
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance
Applications of optimal transport have recently gained remarkable attent...
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04/06/2018
Approximating Hamiltonian dynamics with the Nyström method
Simulating the time-evolution of quantum mechanical systems is BQP-hard ...
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03/21/2018
Incremental Learning-to-Learn with Statistical Guarantees
In learning-to-learn the goal is to infer a learning algorithm that work...
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03/11/2018
Empirical bounds for functions with weak interactions
We provide sharp empirical estimates of expectation, variance and normal...
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02/23/2018
