arXiv research

This paper identifies and bounds ICE central moments using PO marginal central moments.

Estimates high-dimensional posterior densities by marginal distributions and neural networks.

The paper examines higher moments in insurance, focusing on coskewness and its impact on actuarial quantities.

Optimal Transport (OT) problems arise in a wide range of applications, from physics to economics. Getting numerical approximate solution of these problems is a challenging issue of practical importance. In this work, we investigate the relaxation of the OT problem when the marginal constraints are replaced by some mome…

New method finds closest martingale to Brownian motion.

The problem of determining the joint probability distributions for correlated random variables with pre-specified marginals is considered. When the joint distribution satisfying all the required conditions is not unique, the "most unbiased" choice corresponds to the distribution of maximum entropy. The calculation of t…

New margin-based learning guarantees improve generalization bounds.

New method approximates diffusion process posteriors using moment functions.

New findings show score matching's accuracy doesn't ensure numerical stability in diffusion sampling.

Study on convergence rates for optimal transport with regularization.

A new method for estimating causal parameters from observables reduces the need for finite moment conditions.

The paper introduces moment multicalibration for estimating uncertainty across subgroups.

We present a semi-supervised learning algorithm for learning discrete factor analysis models with arbitrary structure on the latent variables. Our algorithm assumes that every latent variable has an "anchor", an observed variable with only that latent variable as its parent. Given such anchors, we show that it is possi…

We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its Bethe approximation. We show that there exists a regime of empirical marginals where such Bethe learning will fail. By failure we mean that th…

The paper introduces a new method for tail bounds of random vectors and matrices.

Entropy based ideas find wide-ranging applications in finance for calibrating models of portfolio risk as well as options pricing. The abstracted problem, extensively studied in the literature, corresponds to finding a probability measure that minimizes relative entropy with respect to a specified measure while satisfy…

A new method for generating samples without training, using smoothed score matching.

Any optimization algorithm based on the risk parity approach requires the formulation of portfolio total risk in terms of marginal contributions. In this paper we use the independence of the underlying factors in the market to derive the centered moments required in the risk decomposition process when the modified vers…

Paper identifies tensor ranks via prior predictive matching, solving system of equations.

Polynomial-time algorithm learns high-dimensional halfspaces without labels.

Bayesian models use hyperparameters to indirectly assign priors, and this work shows how these priors can be derived from maximum entropy principles.

This paper generalizes an important result from the PAC-Bayesian literature for binary classification to the case of ensemble methods for structured outputs. We prove a generic version of the \Cbound, an upper bound over the risk of models expressed as a weighted majority vote that is based on the first and second stat…

We introduce a class of probability measure-valued diffusions, coined polynomial, of which the well-known Fleming--Viot process is a particular example. The defining property of finite dimensional polynomial processes considered by Cuchiero et al. (2012) and Filipovic and Larsson (2016) is transferred to this infinite …

DiPhon generates scalable graphs via diffusion on graphons.

The paper tackles fVaR prediction methods in finance.

New method detects changes in high-dimensional data from small samples.

Infinite dimensional measure-valued processes modeled as polynomial diffusions.

Conditional DGP learns effective kernels from low-fidelity data.

AdaBoost improves binary classification in robust one-bit compressed sensing with adversarial errors.

This paper uses HCR to predict bid-ask spreads from accessible data.

Signals coming from multivariate higher order conditional moments as well as the information contained in exogenous covariates, can be effectively exploited by rational investors to allocate their wealth among different risky investment opportunities. This paper proposes a new flexible dynamic copula model being able t…

New method quantifies uncertainty in denoising models.

New algorithms minimize PAC-Bayesian C-Bound for majority voting, leading to scalable and accurate predictors.

We address the problem of computing approximate marginals in Gaussian probabilistic models by using mean field and fractional Bethe approximations. We define the Gaussian fractional Bethe free energy in terms of the moment parameters of the approximate marginals, derive a lower and an upper bound on the fractional Beth…

We address the problem of computing approximate marginals in Gaussian probabilistic models by using mean field and fractional Bethe approximations. As an extension of Welling and Teh (2001), we define the Gaussian fractional Bethe free energy in terms of the moment parameters of the approximate marginals and derive an …

Paper finds robust $Λ$-quantiles equal to extremal distributions.

We propose a novel method for closed-form predictive distribution modeling with neural nets. In quantifying prediction uncertainty, we build on Evidential Deep Learning, which has been impactful as being both simple to implement and giving closed-form access to predictive uncertainty. We employ it to model aleatoric un…

Paper presents robust confidence sequences for means with known moment bounds and arbitrary corruption.

Generative model captures complex dependence in financial data.

Study detects edge correlation between unlabeled random graphs.

In structured prediction problems where we have indirect supervision of the output, maximum marginal likelihood faces two computational obstacles: non-convexity of the objective and intractability of even a single gradient computation. In this paper, we bypass both obstacles for a class of what we call linear indirectl…

We introduce a new set of consistent measures of risks, in terms of the semi-invariants of pdf's, such that the centered moments and the cumulants of the portfolio distribution of returns that put more emphasis on the tail the distributions. We derive generalized efficient frontiers, based on these novel measures of ri…

Generative neural networks model multivariate time series data.

The study tests a functional-form restriction on risk exposure dynamics using margin debt data.

ELSA efficiently adapts to label shift without post-prediction calibrations.

Unified view of label shift estimation methods.

Estimates mean of random vector with near-optimal error in all directions.

Christoffel function characterizes the corruption a bounded-degree certificate cannot remove in robust halfspace learning.