arXiv research

The paper provides a finite-sample deviation bound for stable autoregressive processes.

Sharp concentration bounds for i.i.d. variables.

The paper improves generative models to avoid replicating observed examples.

New risk class defined based on loss location and deviation.

Paper provides finite-sample guarantees for Wasserstein DRO without dimensionality curse.

New method predicts state evolution for non-first-order algorithms on nonconvex problems.

Study on discrepancy principle for learning algorithms in nonparametric regression.

Paper improves variational inference for complex models.

Recognizing subtle historical patterns is central to modeling and forecasting problems in time series analysis. Here we introduce and develop a new approach to quantify deviations in the underlying hidden generators of observed data streams, resulting in a new efficiently computable universal metric for time series. Th…

The paper improves PCS approximation for ranking and selection under limited simulation budgets.

Rank-statistic method approximates $f$-divergences without density-ratio estimation.

Improved conformal prediction for better conditional coverage of classifier predictions.

We propose a new class of models specifically tailored for spatio-temporal data analysis. To this end, we generalize the spatial autoregressive model with autoregressive and heteroskedastic disturbances, i.e. SARAR(1,1), by exploiting the recent advancements in Score Driven (SD) models typically used in time series eco…

The study provides a theory for causal machine learning with generalization bounds.

Method constructs confidence regions for linear models with arbitrary predictors.

Improved concentration inequalities for sub-Weibull variables enhance statistical and machine learning applications.

Introduces Star-Shaped deviation measures for risk analysis.

A robust conformal method for set estimation using non-conformity scores.

Unified stopping rules ensure accurate policies in contextual learning.

Gradient descent converges to a small neighborhood of the true parameter in logistic regression with Gaussian design.

Paper characterizes monotonic mean-deviation risk measures.

KRCD detects unobserved confounders in nonlinear observational data.

We extend previous large deviations results for the randomised Heston model to the case of moderate deviations. The proofs involve the Gärtner-Ellis theorem and sharp large deviations tools.

Paper proves large deviation principle for stochastic approximations.

New algorithm ensures fair matching in resource allocation.

In this paper we propose the notion of dynamic deviation measure, as a dynamic time-consistent extension of the (static) notion of deviation measure. To achieve time-consistency we require that a dynamic deviation measures satisfies a generalised conditional variance formula. We show that, under a domination condition,…

Study large deviations in life insurance portfolios without identical distributions.

ASK-NN detects distribution drifts in LLM-generated text.

Study large deviations for hypoelliptic diffusion on sub-Riemannian manifolds.

Proposes new deviation measures using Minkowski gauges.

In this paper we analyze a dynamic recursive extension of the (static) notion of a deviation measure and its properties. We study distribution invariant deviation measures and show that the only dynamic deviation measure which is law invariant and recursive is the variance. We also solve the problem of optimal risk-sha…

This paper analyzes momentum Q-learning with finite-sample guarantees.

We provide a unifying treatment of pathwise moderate deviations for models commonly used in financial applications, and for related integrated functionals. Suitable scaling allows us to transfer these results into small-time, large-time and tail asymptotics for diffusions, as well as for option prices and realised vari…

Importance sampling has become an important tool for the computation of tail-based risk measures. Since such quantities are often determined mainly by rare events standard Monte Carlo can be inefficient and importance sampling provides a way to speed up computations. This paper considers moderate deviations for the wei…

Study on financial systems using perturbed unimodal maps with heteroscedastic noise.

Confidence intervals based on penalized maximum likelihood estimators such as the LASSO, adaptive LASSO, and hard-thresholding are analyzed. In the known-variance case, the finite-sample coverage properties of such intervals are determined and it is shown that symmetric intervals are the shortest. The length of the sho…

Estimates class prior for unlabeled data using kernel embedding.

Connections between Lie derivatives and the deviation equation has been investigated in spaces with affine connection. The deviation equations of the geodesics as well as deviation equations of non-geodesics trajectories have been obtained on this base. This is done via imposing certain conditions on the Lie derivative…

Unified approach to stochastic Volterra systems' deviations.

Study large deviations in random walks on Lie groups.

Deviation inequalities and limit laws for random walks on metric spaces.

Let $M$ be a smooth manifold and $\mathcal{S}$ a semi-spray defined on a sub-bundle $\mathcal{C}$ of the tangent bundle $TM$. In this work it is proved that the only non-trivial $k$-jet approximation to the exact geodesic deviation equation of $\mathcal{S}$, linear on the deviation functions and invariant under an spec…

Optimizes variance reduction in Heston model using large and moderate deviations.

Paper analyzes Greedy-GQ for reinforcement learning with Markovian noise.

Large deviations theory applied to policy gradient methods.

Study examines large deviations in random walks on hyperbolic spaces.

The paper analyzes GTD algorithms with finite-sample bounds.

Study large deviations and speed of random walks in hyperbolic spaces.