arXiv Machine Learning
Oct 7
Anytime-valid simulation-based hypothesis testing
arXiv:2610. 08210v1 Announce Type: cross Abstract: For a given data distribution $(X_t)_{t \in \mathbb{N}} \sim Q$ i.
Anytime-valid simulation-based hypothesis testing
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arXiv Machine Learning
Jun 16
Asymptotically Optimal Sequential Testing with Markovian Data
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.
arXiv Machine Learning
Aug 18
On Stopping Times of Power-one Sequential Tests: Tight Lower and Upper Bounds
arXiv:2504. 19952v2 Announce Type: replace-cross Abstract: We present two general lower bounds for stopping times of sequential tests between arbitrary composite nulls $\mathcal P$ and alternatives $\mathcal Q$.
arXiv Machine Learning
Sep 24
Type-II Error Bounds for Test Supermartingales from Lower-Tail Hypotheses
arXiv:2609.27766v1 Announce Type: cross Abstract: In safe hypothesis testing with test supermartingals, Ville's inequality provides anytime-valid type-I error guarantees for every significance level...
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 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
Jun 5
Multi-Armed Sequential Hypothesis Testing by Betting
arXiv:2603. 17925v2 Announce Type: replace-cross Abstract: We consider a variant of sequential testing by betting where, at each time step, the statistician is presented with multiple data sources (arms) and obtains data by choosing one of the arms.
arXiv Machine Learning
Aug 11
From Approachability Residuals to Anytime-Valid Evidence: The Online Convex Geometry of Testing by Betting
arXiv:2608. 09450v1 Announce Type: new Abstract: Betting-based sequential tests and Blackwell approachability are linked by a rate-explicit reduction through support-function residuals.
arXiv Machine Learning
Sep 24
The Type-II Error of Test Supermartingales: e-Power versus the Chernoff-Stein Exponent
arXiv:2609. 27765v1 Announce Type: cross Abstract: In safe hypothesis testing with test supermartingales, Ville's inequality provides anytime-valid type-I error guarantees for every significance level $\alpha\in(0,1]$, if one rejects the null hypothesis whenever the wealth process first exceeds $1/\alpha$.
arXiv Statistics ML
2d ago
Optimal distribution-free sequential change detection with conformal martingales
arXiv:2609.27179v2 Announce Type: replace-cross Abstract: We study the challenging problem of distribution-free sequential changepoint detection for independent observations with unknown and \emph{co...
arXiv Machine Learning
Jun 9
Asymptotic Optimality of Thompson Sampling for Risk-Averse Bandits with Sub-Gaussian Rewards
arXiv:2606. 09191v1 Announce Type: new Abstract: We prove that $\rho\text{-}\mathrm{NPTS}_{\mathrm{SG}}$, an anchor-free nonparametric Thompson Sampling algorithm for risk-averse bandits, achieves regret matching the instance-dependent lower bound to leading order in $\log n$, establishing it as asymptotically optimal for any continuous risk functional $\rho$ (CVaR, mean-variance, Sharpe ratio, distortion risk measures, and more) on the class of distributions with bounded density and sub-Gaussian tails, including Gaussian arms.