On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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18 results for “Sub-Weibull”

Sharper Sub-Weibull Concentrationsmath.ST

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

problem Improving concentration inequalities for sub-Weibull random variables.

method Developed new concentration inequalities for sums of independent sub-Weibull random variables, including a new sub-Weibull parameter.

result New concentration inequalities with sharper constants and a mixture of sub-Gaussian and sub-Weibull tails.

concentration inequalities sub-Weibull distribution machine learning statistical applications random matrices

2021-02-04abs ↗pdf ↗

Fast and Robust: Computationally Efficient Covariance Estimation for Sub-Weibull Vectorsstat.ML

Efficiently estimates covariance for sub-Weibull vectors with sub-Gaussian rate.

problem Outliers in high-dimensional covariance estimation.

method Cross-Fitted Norm-Truncated Estimator for Sub-Weibull distributions.

result Achieves optimal sub-Gaussian rate with

O(Nd^2)

operations.

covariance estimation sub-Weibull distributions sub-Gaussian rate Hanson-Wright inequalities outliers

2025-12-19abs ↗pdf ↗

Concentration Inequalities for Sub-Weibull Random Tensorsq-fin.MF

New concentration inequalities for tensors with heavy-tailed coefficients.

problem Developing bounds for Euclidean functions of tensors with sub-Weibull distributions.

method Extending concentration inequalities to sub-Weibull random tensors, using new inequalities for heavy-tailed random variables and martingale analysis.

result Established a phase transition between sub-gaussian and heavy-tailed regimes for Euclidean functions of tensors.

concentration inequalities sub-Weibull distributions random tensors heavy-tailed random variables phase transition

2025-09-03abs ↗pdf ↗

On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problemsstat.ML

We investigate the optimality of perturbation based algorithms in the stochastic and adversarial multi-armed bandit problems. For the stochastic case, we provide a unified regret analysis for both sub-Weibull and bounded perturbations when rewards are sub-Gaussian. Our bounds are instance optimal for sub-Weibull pertur…

2019-02-02abs ↗pdf ↗

Stochastic Weakly Convex Optimization Under Heavy-Tailed Noisesmath.OC

Paper analyzes convergence of stochastic methods under heavy-tailed noise.

problem Analyzing convergence of stochastic methods under heavy-tailed noise.

method Investigates vanilla and clipped stochastic subgradient descent methods.

result Demonstrates convergence properties under sub-Weibull and p-BCM noise assumptions.

Stochastic Optimization Weakly Convex Heavy-Tailed Noise Sub-Weibull p-BCM

2025-07-17abs ↗pdf ↗

Tail-Aware Information-Theoretic Generalization for RLHF and SGLDstat.ML

New framework controls generalization for heavy-tailed data in RLHF and SGLD.

problem Heavy-tailed data in modern learning pipelines.

method Tail-dependent information-theoretic framework for sub-Weibull data.

result Sharp generalization bounds for heavy-tailed data.

Heavy-tailed data Information-theoretic bounds Sub-Weibull processes PAC-Bayes Rényi divergence

2026-04-12abs ↗pdf ↗

Sparsified-Learning for High-Dimensional Heavy-Tailed Locally Stationary Time Series, Concentration and Oracle Inequalitiesstat.ML

Paper develops sparse learning for heavy-tailed time series with locally stationary dynamics.

problem Sparse learning for high-dimensional heavy-tailed locally stationary time series.

method Additive modeling with kernel smoothing, sparsity-inducing penalized estimation.

result Prediction-error bounds and convergence rates for different sparsity structures.

sparse learning time series heavy-tailed locally stationary concentration inequalities

2025-04-08abs ↗pdf ↗

Concentration inequalities for high-dimensional linear processes with dependent innovationsmath.ST

Develops inequalities for high-dimensional linear processes with dependent innovations.

problem Estimating high-dimensional VAR(p) systems and HAC covariance estimation.

method Concentration inequalities for

l_\infty

norm of vector linear processes with sub-Weibull, mixingale innovations.

result Obtained concentration bounds for the maximum entrywise norm of lag-

h

autocovariance matrices.

concentration inequalities high-dimensional linear processes dependent innovations VAR(p) systems HAC covariance estimation

2023-07-23abs ↗pdf ↗

Understanding Priors in Bayesian Neural Networks at the Unit Levelstat.ML

We investigate deep Bayesian neural networks with Gaussian weight priors and a class of ReLU-like nonlinearities. Bayesian neural networks with Gaussian priors are well known to induce an L2, "weight decay", regularization. Our results characterize a more intricate regularization effect at the level of the unit activat…

2018-10-11abs ↗pdf ↗

Moving Beyond Sub-Gaussianity in High-Dimensional Statistics: Applications in Covariance Estimation and Linear Regressionmath.ST

Concentration inequalities form an essential toolkit in the study of high dimensional (HD) statistical methods. Most of the relevant statistics literature in this regard is based on sub-Gaussian or sub-exponential tail assumptions. In this paper, we first bring together various probabilistic inequalities for sums of in…

2018-04-08abs ↗pdf ↗

Concentration Inequalities for Statistical Inferencemath.ST

The paper reviews and improves concentration inequalities for statistical inference.

problem Analyzing statistical inference in various settings with high-dimensional data.

method Review and improvement of concentration inequalities for different types of random variables and statistical measures.

result Fresh new results and improved bounds with sharper constants.

concentration inequalities statistical inference high-dimensional data random variables sharp constants

2020-11-04abs ↗pdf ↗

Stability beyond Bounded Differences: Sharp Generalization Bounds under Finite

L_p

Momentsstat.ML

New stability framework relaxes boundedness assumptions for generalization bounds.

problem Overly restrictive assumptions for modern learning settings with heavy-tailed or unbounded losses.

method Develops a stability-based framework requiring only finite

L_p

moment conditions.

result Sharp generalization bounds derived for various learning paradigms.

stability generalization learning algorithms L_p moment concentration inequalities

2026-06-05abs ↗pdf ↗

Moment-Based Selection of Multiresponse Linear Mixed-Effects Modelsstat.ME

MOMENT selects and estimates mixed-effects models using moment identities.

problem Selecting and estimating random-effects covariance matrix and fixed-effects coefficients in multiresponse linear mixed-effects models.

method MOMENT is a stage-wise moment-based framework that reduces the random-effects selection problem to a smooth constrained convex optimization problem.

result MOMENT performs competitively and can outperform separate univariate analyses for correlated responses.

moment-based mixed-effects models covariance matrix fixed-effects coefficients sparsity

2026-07-02abs ↗pdf ↗

Minimax optimal transfer learning for high-dimensional additive regressionstat.ML

The paper develops a minimax optimal method for high-dimensional regression using auxiliary data.

problem High-dimensional additive regression with heavy-tailed errors and transfer learning.

method Smooth backfitting estimator with local linear smoothing, followed by a two-stage estimation method.

result The method achieves the minimax optimal rate under certain conditions.

high-dimensional regression transfer learning minimax optimality additive models local linear smoothing

2025-09-08abs ↗pdf ↗

Reinforcement learning for linear-convex models with jumps via stability analysis of feedback controlsmath.OC

Study shows how to control jump-diffusion processes with stable feedback controls in reinforcement learning.

problem Control jump-diffusion processes with unknown coefficients in reinforcement learning.

method Lipschitz continuous optimal feedback controls, stability analysis of forward-backward SDEs, least-squares algorithm.

result Achieves

O(\sqrt{N\ln N})

regret for linear-convex learning problems with jumps.

Reinforcement Learning Linear-Convex Models Jump-Diffusion Processes Stability Analysis Least-Squares Algorithm

2021-04-19abs ↗pdf ↗

Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noisemath.PR

Maximal concentration bounds for stochastic approximation with heavy-tailed noise.

problem Analyzing the convergence of stochastic approximation algorithms under heavy-tailed Markovian noise.

method Novel Lyapunov function and black-box truncation argument.

result Tail behavior of the error can be sub-Gaussian, sub-Weibull, or lighter than any Pareto but heavier than any Weibull.

stochastic approximation heavy-tailed noise Markovian noise concentration bounds Lyapunov function

2026-05-20abs ↗pdf ↗

Revisiting the Last-Iterate Convergence of Stochastic Gradient Methodscs.LG

This work addresses the convergence of SGD's final iterate without restrictive assumptions.

problem Prove optimal convergence rate of SGD's final iterate without compact domains or bounded noise.

method Unified proof for general domains, composite objectives, non-Euclidean norms, etc.

result First unified convergence rates in expectation and high probability.

SGD Convergence Optimization Lipschitz Convex

2023-12-13abs ↗pdf ↗

Sharp Concentration Inequalities: Phase Transition and Mixing of Orlicz Tails with Variancemath.ST

Sharp concentration inequalities for sub-Orlicz random variables with phase transition at α=2.

problem Developing concentration inequalities for sub-Orlicz random variables with phase transition.

method New theoretical analysis framework involving variance and min/max functions of Orlicz tails.

result Sharp concentration inequalities with phase transition at α=2 for sub-Orlicz random variables.

concentration inequalities Orlicz norm phase transition sub-Orlicz min/max functions

2026-03-26abs ↗pdf ↗