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Found 20 papers, 7 papers with code
Variational autoencoder with weighted samples for high-dimensional non-parametric adaptive importance sampling
In this contribution, we suggest to use as the approximating model a distribution parameterised by a variational autoencoder.
Improved learning theory for kernel distribution regression with two-stage sampling
In this paper, we improve the learning theory of kernel distribution regression.
Gaussian Processes on Distributions based on Regularized Optimal Transport
We present a novel kernel over the space of probability measures based on the dual formulation of optimal regularized transport.
Large-Sample Properties of Non-Stationary Source Separation for Gaussian Signals
Non-stationary source separation is a well-established branch of blind source separation with many different methods.
Regret Analysis of Dyadic Search
We analyze the cumulative regret of the Dyadic Search algorithm of Bachoc et al. [2022].
A Near-Optimal Algorithm for Univariate Zeroth-Order Budget Convex Optimization
This paper studies a natural generalization of the problem of minimizing a univariate convex function $f$ by querying its values sequentially.
Local Identifiability of Deep ReLU Neural Networks: the Theory
Is a sample rich enough to determine, at least locally, the parameters of a neural network?
High-dimensional additive Gaussian processes under monotonicity constraints
First, we show that our framework enables to satisfy the constraints everywhere in the input space.
Parameter identifiability of a deep feedforward ReLU neural network
The possibility for one to recover the parameters-weights and biases-of a neural network thanks to the knowledge of its function on a subset of the input space can be, depending on the situation, a curse or a blessing.
Instance-Dependent Bounds for Zeroth-order Lipschitz Optimization with Error Certificates
We study the problem of zeroth-order (black-box) optimization of a Lipschitz function $f$ defined on a compact subset $\mathcal X$ of $\mathbb R^d$, with the additional constraint that algorithms must certify the accuracy of their recommendations.
The sample complexity of level set approximation
We study the problem of approximating the level set of an unknown function by sequentially querying its values.
Rate of convergence for geometric inference based on the empirical Christoffel function
We consider the problem of estimating the support of a measure from a finite, independent, sample.
Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC
Finally, on 2D and 5D coastal flooding applications, we show that more flexible and realistic GP implementations can be obtained by considering noise effects and by enforcing the (linear) inequality constraints.
Explaining Machine Learning Models using Entropic Variable Projection
In order to emphasize the impact of each input variable, this formalism uses an information theory framework that quantifies the influence of all input-output observations based on entropic projections.
Maximum likelihood estimation for Gaussian processes under inequality constraints
We first show that the (unconstrained) maximum likelihood estimator has the same asymptotic distribution, unconditionally and conditionally, to the fact that the Gaussian process satisfies the inequality constraints.
Statistics Theory Probability Statistics Theory
Gaussian Processes indexed on the symmetric group: prediction and learning
In the framework of the supervised learning of a real function defined on a space X , the so called Kriging method stands on a real Gaussian field defined on X.
Finite-dimensional Gaussian approximation with linear inequality constraints
Introducing inequality constraints in Gaussian process (GP) models can lead to more realistic uncertainties in learning a great variety of real-world problems.
A Gaussian Process Regression Model for Distribution Inputs
We prove that the Gaussian processes indexed by distributions corresponding to these kernels can be efficiently forecast, opening new perspectives in Gaussian process modeling.
A supermartingale approach to Gaussian process based sequential design of experiments
Thisobservation enables us to establish generic consistency results for abroad class of SUR strategies.
Nested Kriging predictions for datasets with large number of observations
This work falls within the context of predicting the value of a real function at some input locations given a limited number of observations of this function.
