Distributionally Robust Optimization with Polynomial Densities: Theory, Models and Algorithms

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Distributionally robust chance constrained programs minimize a deterministic cost function subject to the satisfaction of one or more safety conditions with high probability, given that the probability distribution of the uncertain problem parameters affec ...

We use generalized Ray-Knight theorems, introduced by B. Toth in 1996, together with techniques developed for excited random walks as main tools for establishing positive and negative results concerning convergence of some classes of diffusively scaled sel ...

Diffusion models generating images conditionally on text, such as Dall-E 2 [51] and Stable Diffusion[53], have recently made a splash far beyond the computer vision com- munity. Here, we tackle the related problem of generating point clouds, both unconditi ...

In this work, we consider the problem of estimating the probability distribution, the quantile or the conditional expectation above the quantile, the so called conditional-value-at-risk, of output quantities of complex random differential models by the MLM ...

Two related methods for inverting line-integrated measurements are presented in this research paper in the context of the recent deuterium-tritium experiments in the JET tokamak. Unlike traditional methods of tomography, these methods rely on making use of ...

Distribution-on-distribution regression considers the problem of formulating and es-timating a regression relationship where both covariate and response are probability distributions. The optimal transport distributional regression model postulates that th ...

Measuring conditional dependencies among the variables of a network is of great interest to many disciplines. This paper studies some shortcomings of the existing dependency measures in detecting direct causal influences or their lack of ability for group ...

This paper proposes an algorithm to upper-bound maximal quantile statistics of a state function over the course of a Stochastic Differential Equation (SDE) system execution. This chance-peak problem is posed as a nonconvex program aiming to maximize the Va ...

Graph comparison deals with identifying similarities and dissimilarities between graphs. A major obstacle is the unknown alignment of graphs, as well as the lack of accurate and inexpensive comparison metrics. In this work we introduce the filter graph dis ...

The goal of this paper is to characterize function distributions that general neural networks trained by descent algorithms (GD/SGD), can or cannot learn in polytime. The results are: (1) The paradigm of general neural networks trained by SGD is poly-time ...

This article presents the Lightning Performance (LP) assessment of a realistic portion of the Italian distribution network with the use of probability distributions for lightning parameters inferred from local data recorded by a Lightning Location System ( ...

This work extends the range of pathways for the production of metallic microcomponents by downscaling metal casting. This is accomplished by using either of two different molding techniques, namely femtosecond laser micromachining or lithographic silicon m ...

The increasing usage of nonlinear analyses for the design of reinforced concrete structures and the necessity of codes of practice to provide a consistent safety format for them is one of the challenges that new generations of codes of practice are facing. ...

Within the context of contemporary machine learning problems, efficiency of optimization process depends on the properties of the model and the nature of the data available, which poses a significant problem as the complexity of either increases ad infinit ...

We consider increasingly complex models of matrix denoising and dictionary learning in the Bayes-optimal setting, in the challenging regime where the matrices to infer have a rank growing linearly with the system size. This is in contrast with most existin ...

We present a method for reducing errors-variance and bias-in physically based differentiable rendering (PBDR). Typical applications of PBDR repeatedly render a scene as part of an optimization loop involving gradient descent. The actual change introduced b ...

Distributionally Robust Optimization with Polynomial Densities: Theory, Models and Algorithms

Chattez avec Graph Search

On Approximations of Data-Driven Chance Constrained Programs over Wasserstein Balls

CONVERGENCE AND NONCONVERGENCE OF SCALED SELF-INTERACTING RANDOM WALKS TO BROWNIAN MOTION PERTURBED AT EXTREMA

Geometrically-Conditioned Point Diffusion Models

Quantifying uncertain system outputs via the multi-level Monte Carlo method --- distribution and robustness measures

Regressive Tomography and Direct Regressive Reconstruction: A Research Note

Minimax rate for optimal transport regression between distributions

A Wasserstein-based measure of conditional dependence

Peak Value-at-Risk Estimation for Stochastic Differential Equations using Occupation Measures

fGOT: Graph Distances Based on Filters and Optimal Transport

Polynomial-time universality and limitations of deep learning

Assessment of the Lightning Performance of overhead distribution lines based on Lightning Location Systems data

Bringing casting to microfabrication: processing and deformation of microcastings

Considerations on the partial safety factor format for reinforced concrete structures accounting for multiple failure modes

Universal and adaptive methods for robust stochastic optimization

Statistical limits of dictionary learning: Random matrix theory and the spectral replica method

Recursive Control Variates for Inverse Rendering