Anytime Valid Confidence Bands for Distribution Functions
Hultberg, Zachariah and Ribeiro
Confidence bands for the distribution function of a data stream that hold at all times and all points at once, even when each observation depends on the past: a sequential, martingale counterpart of the Dvoretzky-Kiefer-Wolfowitz inequality, useful wherever a model is monitored continuously.
- Prerequisites
- Discrete-time martingales and Ville’s inequality; empirical processes at the level of the Dvoretzky-Kiefer-Wolfowitz inequality
- Focus on
- The bands for the time-averaged conditional distribution function and the binary-search-tree argument, then the general framework
- Reading time
- About 3 hours
arXiv:2610.36149
The Cost of an Unknown Horizon for the Last Iterate of Clipped SGD under Heavy-Tailed Noise
Peng, Tang and Jia
The optimal time-uniform rate for the last iterate of clipped SGD under noise with a bounded central \(p\)th moment is \(\tilde O(t^{-\beta_p})\) with \(\beta_p = 2(p-1)/(3p-1)\), strictly worse than \(O(T^{-\alpha_p})\), \(\alpha_p = (p-1)/p\), when the horizon is known; a clean example of what data-dependent stopping costs.
- Prerequisites
- Convergence analysis of SGD on smooth convex functions; concentration for heavy-tailed martingales
- Focus on
- The main theorem, the lower-bound construction and the section on querying gradients at the running average
- Reading time
- About 2.5 hours
arXiv:2610.33381