paper-with-me

홈 › Papers

The extended Ville's inequality for nonintegrable nonnegative supermartingales

2023-04-03 · Hongjian Wang, Aaditya Ramdas

Following the initial work by Robbins, we rigorously present an extended theory of nonnegative supermartingales, requiring neither integrability nor finiteness. In particular, we derive a key maximal inequality foreshadowed by Robbins, which we call the extended Ville's inequality, that strengthens the classical Ville's inequality (for integrable nonnegative supermartingales), and also applies to our nonintegrable setting. We derive an extension of the method of mixtures, which applies to $\sigma$-finite mixtures of our extended nonnegative supermartingales. We present some implications of our theory for sequential statistics, such as the use of improper mixtures (priors) in deriving nonparametric confidence sequences and (extended) e-processes.

📄 PDF Abstract BibTeX arXiv:2304.01163

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

A unified recipe for deriving (time-uniform) PAC-Bayes bounds

2023-02-07 · Ben Chugg, Hongjian Wang, Aaditya Ramdas

We present a unified framework for deriving PAC-Bayesian generalization bounds. Unlike most previous literature on this topic, our bounds are anytime-valid (i.e., time-uniform), meaning that they hold at all stopping tim…

Generalization Boundsvalid

Anytime-valid t-tests and confidence sequences for Gaussian means with unknown variance

2023-10-05 · Hongjian Wang, Aaditya Ramdas

In 1976, Lai constructed a nontrivial confidence sequence for the mean $\mu$ of a Gaussian distribution with unknown variance $\sigma^2$. Curiously, he employed both an improper (right Haar) mixture over $\sigma$ and an …

valid

Positive Semidefinite Matrix Supermartingales

2024-01-28 · Hongjian Wang, Aaditya Ramdas

We explore the asymptotic convergence and nonasymptotic maximal inequalities of supermartingales and backward submartingales in the space of positive semidefinite matrices. These are natural matrix analogs of scalar nonn…

Econometricsvalid

PAC-Bayes Generalisation Bounds for Heavy-Tailed Losses through Supermartingales

2022-10-03 · Maxime Haddouche, Benjamin Guedj

While PAC-Bayes is now an established learning framework for light-tailed losses (\emph{e.g.}, subgaussian or subexponential), its extension to the case of heavy-tailed losses remains largely uncharted and has attracted …

Anytime-Valid Confirmation of Label-Shift Corrections

2026-06-12 · Seungjin Choi arxiv

In small-batch scientific deployments, labeled target outcomes may be too scarce for reliable shift estimation even when unlabeled target inputs are available. We address the complementary setting where the practitioner …