arXiv Statistics ML
Optimal distribution-free sequential change detection with conformal martingales
Read the original on arXiv Statistics ML →The Flow has not summarised this story yet — read it at arXiv Statistics ML.
Related stories
arXiv Statistics ML
Sep 24
Change detection with conformal martingales: new optimal constructions, and suboptimality of existing methods
arXiv:2609.27179v1 Announce Type: cross Abstract: We study distribution-free sequential changepoint detection for independent observations with unknown and unrestricted pre- and post-change laws. We...
arXiv Machine Learning
Oct 19
Information-Geometric Blind Spots of Conformal Test Martingales
arXiv:2610. 23131v1 Announce Type: cross Abstract: Conformal test martingales (CTMs) are e-processes for sequentially testing exchangeability via p-values produced by a conformity function $A$.
arXiv Machine Learning
Jun 15
Testing For Distribution Shifts with Conditional Conformal Test Martingales
arXiv:2602. 13848v2 Announce Type: replace Abstract: We propose a sequential test for detecting arbitrary distribution shifts that allows conformal test martingales (CTMs) to work under a fixed, reference-conditional setting.
arXiv Machine Learning
Jun 2
Distribution-free changepoint localization after sequential change detection
arXiv:2606. 01256v1 Announce Type: cross Abstract: This paper introduces a distribution-free framework for constructing post-detection confidence sets for changepoints after stopping a sequential change detection procedure.
arXiv Machine Learning
Jun 15
Nonlinear Two-Time-Scale Stochastic Approximation: A Sharp Phase Transition and How to Beat It
arXiv:2606. 14488v1 Announce Type: cross Abstract: Recent finite-time analyses of nonlinear two-time-scale stochastic approximation show that under contractive assumptions the slow iterate $Y_k$ with stepsizes $\beta_k=\Theta(k^{-1})$ and $\alpha_k=\Theta(k^{-a})$, $a\in(1/2,1)$, generally satisfies a mean-square rate of order $k^{-a}$; decoupled $k^{-1}$ rates require strong local linearity.
arXiv Machine Learning
Jul 14
Betting on Moments: Legendre Jumper Martingales for Online Exchangeability Testing
arXiv:2606. 20859v2 Announce Type: replace-cross Abstract: A fundamental assumption in statistics and machine learning is that ``the future looks like the past,'' formalized as exchangeability: the joint data distribution is order-invariant.