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Found 11 papers, 3 papers with code
Consistent Long-Term Forecasting of Ergodic Dynamical Systems
We study the evolution of distributions under the action of an ergodic dynamical system, which may be stochastic in nature.
Learning invariant representations of time-homogeneous stochastic dynamical systems
We consider the general class of time-homogeneous stochastic dynamical systems, both discrete and continuous, and study the problem of learning a representation of the state that faithfully captures its dynamics.
Meta Representation Learning with Contextual Linear Bandits
We aim to leverage this information in order to learn a new downstream bandit task, which shares the same representation.
AdaCap: Adaptive Capacity control for Feed-Forward Neural Networks
AdaCap is the combination of two novel ingredients, the Muddling labels for Regularization (MLR) loss and the Tikhonov operator training scheme.
Multi-task Representation Learning with Stochastic Linear Bandits
We study the problem of transfer-learning in the setting of stochastic linear bandit tasks.
Muddling Label Regularization: Deep Learning for Tabular Datasets
Deep Learning (DL) is considered the state-of-the-art in computer vision, speech recognition and natural language processing.
Muddling Labels for Regularization, a novel approach to generalization
The main goal of this paper is to introduce a novel approach to achieve generalization without any data splitting, which is based on a new risk measure which directly quantifies a model's tendency to overfit.
Optimizing generalization on the train set: a novel gradient-based framework to train parameters and hyperparameters simultaneously
We observe in our experiments a significantly smaller runtime for our methods as compared to benchmark methods for equivalent prediction score.
Large scale Lasso with windowed active set for convolutional spike sorting
Spike sorting is a fundamental preprocessing step in neuroscience that is central to access simultaneous but distinct neuronal activities and therefore to better understand the animal or even human brain.
Concentration bounds for linear Monge mapping estimation and optimal transport domain adaptation
This article investigates the quality of the estimator of the linear Monge mapping between distributions.
Nuclear norm penalization and optimal rates for noisy low rank matrix completion
We show that the obtained rates are optimal up to logarithmic factors in a minimax sense and also derive, for any fixed matrix $A_0$, a non-minimax lower bound on the rate of convergence of our estimator, which coincides with the upper bound up to a constant factor.
