The paper introduces a new quasi‑stationarity diagnostic for accelerated dynamics methods, replacing traditional MCMC diagnostics with a coarse‑graining framework for killed Markov jump processes and a recurrent neural network trained on particle approximations of non‑linear effective flows. It provides uniform‑in‑time, non‑asymptotic bounds for approximating the true process’s probability flow without assuming reversibility, and demonstrates that the learned RNN diagnostic outperforms the MCMC approach in ROC analysis, is less conservative, fails less often, and offers an interpretable decision threshold.
By No\'e Blassel
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.
By Shalev Shaer, Yarin Bar, Drew Prinster, Yaniv Romano
arXiv:2602. 17587v3 Announce Type: replace-cross Abstract: We study one-sided and $\alpha$-correct sequential hypothesis testing for data generated by an ergodic, finite-state Markov chain.
By Alhad Sethi, Kavali Sofia Sagar, Shubhada Agrawal, Debabrota Basu, P. N. Karthik
The paper presents a non‑asymptotic analysis of Markov chain Monte Carlo (MCMC) algorithms that learn and apply a preconditioner based on either the target covariance or the expected Hessian of the target potential. It compares the finite‑time computational costs of these preconditioned schemes with unpreconditioned counterparts, providing guarantees for algorithms such as the Unadjusted Langevin Algorithm (ULA) and the proximal sampler. The analysis relies on a contraction assumption in the Wasserstein‑2 distance to formalize approximate independence and bridge modern MCMC theory with classical effective sample size heuristics.
By Max Hird, Florian Maire, Jeffrey Negrea
arXiv:2509. 26175v2 Announce Type: replace Abstract: The Metropolis-within-Gibbs (MwG) algorithm is a widely used Markov chain Monte Carlo method for sampling from high-dimensional distributions when exact conditional sampling is intractable.
By Cecilia Secchi, Giacomo Zanella
arXiv:2607. 16811v4 Announce Type: replace Abstract: Drift detectors that work tend not to explain themselves, and drift detectors that explain themselves tend to fail in high dimension.
By Behnam Asadi
arXiv:2605. 30722v2 Announce Type: replace Abstract: We propose CerT-MCMC, a framework that equips learned-transport Markov chain Monte Carlo with automatic, rigorous convergence certificates.
By Jun Hu
arXiv:2602. 21479v3 Announce Type: replace-cross Abstract: Across many risk-sensitive areas, it is critical to continuously audit machine learning systems as we receive more data to quickly determine if they are performing as designed.
By Beepul Bharti, Ambar Pal, Jeremias Sulam
The paper introduces a leave‑a‑window‑out estimator for next‑token functionals, such as the surprise probability and test error, in sequences of random variables. By deleting a window of length τ after each index, the estimator generalizes leave‑one‑out and achieves parametric error decay for stationary β‑mixing processes that admit a Marton coupling. The authors provide both upper bounds and a minimax lower bound for the surprise probability, and demonstrate through simulations that their method outperforms traditional baselines on Markov, moving‑average, and autoregressive processes.
By Milind Nakul, Vidya Muthukumar, Ashwin Pananjady
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...
By Swapnaneel Bhattacharyya, Aaditya Ramdas
arXiv:2608. 20337v1 Announce Type: cross Abstract: Accounting for information flow on the path space of trajectories of a nonnegative martingale yields exact variational identities for it, even at arbitrary random times.
By Akshay Balsubramani
The paper investigates how to decide when to stop collecting posterior samples in classification‑oriented adaptive sensing. It shows that a simple threshold‑based plug‑in rule does not guarantee the desired confidence level, and proposes calibrated fixed‑sample and finite‑horizon sequential stopping rules that control the false‑declaration probability. Experiments on MNIST demonstrate that the sequential rule can reduce sensing cost the most, and that a curtailment strategy can save up to 62% of posterior samples while maintaining accuracy.
By Vincent Corlay, Andriy Enttsel