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05/10/2023
Speeding up Monte Carlo Integration: Control Neighbors for Optimal Convergence
A novel linear integration rule called control neighbors is proposed in ...
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05/24/2022
A Quadrature Rule combining Control Variates and Adaptive Importance Sampling
Driven by several successful applications such as in stochastic gradient...
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04/12/2022
High-dimensional nonconvex lasso-type M-estimators
This paper proposes a theory for ℓ_1-norm penalized high-dimensional M-e...
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02/21/2022
Consistent Cross Validation with stable learners
This paper investigates the efficiency of different cross-validation (CV...
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02/01/2022
Cross Validation for Rare Events
We derive sanity-check bounds for the cross-validation (CV) estimate of ...
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10/29/2021
Adaptive Importance Sampling meets Mirror Descent: a Bias-variance tradeoff
Adaptive importance sampling is a widely spread Monte Carlo technique th...
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10/27/2021
Nearest neighbor process: weak convergence and non-asymptotic bound
An empirical measure that results from the nearest neighbors to a given ...
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07/30/2021
Tail inverse regression for dimension reduction with extreme response
We consider the problem of dimensionality reduction for prediction of a ...
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07/27/2021
Individual Survival Curves with Conditional Normalizing Flows
Survival analysis, or time-to-event modelling, is a classical statistica...
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05/25/2021
SGD with Coordinate Sampling: Theory and Practice
While classical forms of stochastic gradient descent algorithm treat the...
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06/26/2020
Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications
Motivated by a wide variety of applications, ranging from stochastic opt...
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06/23/2020
Conditional independence testing via weighted partial copulas
This paper introduces the weighted partial copula function for testing c...
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06/16/2020
Risk bounds when learning infinitely many response functions by ordinary linear regression
Consider the problem of learning a large number of response functions si...
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06/04/2020
Towards Asymptotic Optimality with Conditioned Stochastic Gradient Descent
In this paper, we investigate a general class of stochastic gradient des...
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05/20/2020
Infinite-dimensional gradient-based descent for alpha-divergence minimisation
This paper introduces the (α, Γ)-descent, an iterative algorithm which o...
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10/24/2019
High dimensional regression for regenerative time-series: an application to road traffic modeling
This paper investigates statistical models for road traffic modeling. Th...
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09/26/2019
The f-Divergence Expectation Iteration Scheme
This paper introduces the f-EI(ϕ) algorithm, a novel iterative algorithm...
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06/26/2019
Control variate selection for Monte Carlo integration
Monte Carlo integration with variance reduction by means of control vari...
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06/05/2019
Empirical Risk Minimization under Random Censorship: Theory and Practice
We consider the classic supervised learning problem, where a continuous ...
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03/20/2019
Adaptive importance sampling by kernel smoothing
A key determinant of the success of Monte Carlo simulation is the sampli...
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06/15/2018
Parametric versus nonparametric: the fitness coefficient
The fitness coefficient, introduced in this paper, results from a compet...
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06/06/2018
Rademacher complexity for Markov chains : Applications to kernel smoothing and Metropolis-Hasting
Following the seminal approach by Talagrand, the concept of Rademacher c...
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06/04/2018
On an extension of the promotion time cure model
We consider the problem of estimating the distribution of time-to-event ...
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06/04/2018
Asymptotic optimality of adaptive importance sampling
Adaptive importance sampling (AIS) uses past samples to update the sampl...
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06/04/2018
Efficiency of adaptive importance sampling
The sampling policy of stage t, formally expressed as a probability dens...
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01/05/2018