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Found 16 papers, 5 papers with code
Sliced-Wasserstein Estimation with Spherical Harmonics as Control Variates
Spherical harmonics are polynomials on the sphere that form an orthonormal basis of the set of square-integrable functions on the sphere.
Scalable and hyper-parameter-free non-parametric covariate shift adaptation with conditional sampling
In this paper, we propose a new non-parametric approach to covariate shift adaptation which avoids estimating weights and has no hyper-parameter to be tuned.
Sharp error bounds for imbalanced classification: how many examples in the minority class?
When dealing with imbalanced classification data, reweighting the loss function is a standard procedure allowing to equilibrate between the true positive and true negative rates within the risk measure.
A Quadrature Rule combining Control Variates and Adaptive Importance Sampling
Within the standard adaptive importance sampling framework, a simple weighted least squares approach is proposed to improve the procedure with control variates.
Adaptive Importance Sampling meets Mirror Descent: a Bias-variance tradeoff
Adaptive importance sampling is a widely spread Monte Carlo technique that uses a re-weighting strategy to iteratively estimate the so-called target distribution.
Nearest neighbor empirical processes
First, the associated empirical process is shown to satisfy a uniform central limit theorem under a local bracketing entropy condition on the underlying class of functions reflecting the localizing nature of the nearest neighbor algorithm.
SGD with Coordinate Sampling: Theory and Practice
While classical forms of stochastic gradient descent algorithm treat the different coordinates in the same way, a framework allowing for adaptive (non uniform) coordinate sampling is developed to leverage structure in data.
Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications
Motivated by a wide variety of applications, ranging from stochastic optimization to dimension reduction through variable selection, the problem of estimating gradients accurately is of crucial importance in statistics and learning theory.
Conditional independence testing via weighted partial copulas and nearest neighbors
This paper introduces the \textit{weighted partial copula} function for testing conditional independence.
Risk bounds when learning infinitely many response functions by ordinary linear regression
Consider the problem of learning a large number of response functions simultaneously based on the same input variables.
Asymptotic Analysis of Conditioned Stochastic Gradient Descent
In this paper, we investigate a general class of stochastic gradient descent (SGD) algorithms, called Conditioned SGD, based on a preconditioning of the gradient direction.
High dimensional regression for regenerative time-series: an application to road traffic modeling
A statistical predictive model in which a high-dimensional time-series regenerates at the end of each day is used to model road traffic.
Control variate selection for Monte Carlo integration
Monte Carlo integration with variance reduction by means of control variates can be implemented by the ordinary least squares estimator for the intercept in a multiple linear regression model with the integrand as response and the control variates as covariates.
Statistics Theory Statistics Theory
Empirical Risk Minimization under Random Censorship: Theory and Practice
As ignoring censorship in the risk computation may clearly lead to a severe underestimation of the target duration and jeopardize prediction, we propose to consider a plug-in estimate of the true risk based on a Kaplan-Meier estimator of the conditional survival function of the censorship $C$ given $X$, referred to as Kaplan-Meier risk, in order to perform empirical risk minimization.
On an extension of the promotion time cure model
We consider the problem of estimating the distribution of time-to-event data that are subject to censoring and for which the event of interest might never occur, i. e., some subjects are cured.
Statistics Theory Methodology Statistics Theory
Asymptotic optimality of adaptive importance sampling
Each stage $t$ is formed with two steps : (i) to explore the space with $n_t$ points according to $q_t$ and (ii) to exploit the current amount of information to update the sampling policy.
