AI systems increasingly make consequential decisions, but fully auditing every output is infeasible at scale. This paper studies selective auditing: sequentially deciding when to trust an AI decision versus escalate it to an expert, using only a scalar confidence score. The authors frame this as an online constrained optimization problem and derive algorithms that guarantee the fraction of unaudited errors stays below a user-specified threshold α without any distributional assumptions. The core contribution is an optimally efficient algorithm that provably minimizes audit effort for both adversarial and i.i.d. data streams.

Standard computer-model calibration focuses on parameter posteriors, not the predictive accuracy of the calibrated simulator. The authors propose a two-layer framework: a flow-matching model amortizes posterior inference, then conformal risk control tunes parameter sets so the simulator pushforward hits a target miscoverage on synthetic calibration data. This targets the quantity that actually matters downstream—predictive coverage—while exploiting amortization to avoid expensive per-observation MCMC. Experiments on synthetic and real epidemiological data show roughly 5× sharper bands than KOH–MCMC with near-nominal coverage.

AI agents that act as statistical analysts evolve through model upgrades, prompt edits, and new tools, but each change risks inflating Type I error. This paper introduces continual validation: before any update is deployed, it is frozen and tested on newly simulated null data using exact binomial confidence bounds with a decaying per-update risk budget. The result is a lifetime guarantee that no invalid version passes, which matters because classical fixed-procedure guarantees break under adaptive, open-ended evolution.

This paper tackles measurement-constrained linear regression, where responses are expensive to collect and must be selected from a large candidate pool before being observed. The authors propose Expected Loss Optimal Selection (ELOS), which derives an A-optimality-type criterion that depends only on covariates and remains valid under potential model misspecification. The core insight is that averaging the expected prediction loss over uniformly random misspecification directions recovers the same trace-inverse criterion as under a correctly specified model, yielding a principled response-free selection rule.

The nonparametric maximum likelihood estimator (NPMLE) of a Gaussian mixture is a convex program over an infinite-dimensional space, yet its maximizers can be nonunique and are only known to have at most $n$ atoms. This paper shows that a vanishingly small random reweighting of the likelihood---using independent Gamma weights that concentrate around one---selects a unique maximizer with polylogarithmically many atoms in any fixed dimension $d$. The resulting estimator retains a Hellinger rate that is parametric up to logarithmic factors, solving the open problem of exact sparsity for multivariate Gaussian NPMLEs without any support penalty.

Most fairness tests ask whether a model treats groups exactly equally, but real audits often need to decide whether a disparity exceeds a legally or practically meaningful tolerance. This paper formalizes tolerance-based fairness auditing as two distinct statistical objectives—formal violation certification and early-warning sensitivity screening—and develops empirical-likelihood procedures calibrated to each. Constrained empirical likelihood (CEL) controls false violation declarations over a tolerance region, while split empirical likelihood (SEL) and an adjusted variant (ASEL) use an adaptive boundary-proxy principle to increase detection near the boundary. The work matters because it translates abstract fairness requirements into actionable, objective-dependent statistical guarantees.

Treating model release as a design problem rather than a convention, this paper asks how many of $k$ automated tests a model should pass before deployment to guarantee a stated reliability target while retaining as many good models as possible. Using a two-class Gaussian latent-factor model with common within-class correlation $\rho$, the author proves that precision rises strictly with the required pass count $r$, so the optimal gate is the most lenient $r$-of-$k$ rule that meets the target. This matters because teams often default to pass-all gating, which can decimate the pool of acceptable models when tests are even modestly correlated; the paper shows that misjudging dependence by a factor of two can hurt more than shifting the gate by one test.

Neyman–Pearson multiclass classification (NPMC) handles asymmetric error costs by constraining class-specific misclassification rates, yet existing methods fail when training labels are corrupted. This paper proposes an empirical likelihood (EL) framework that recovers true class proportions and posterior probabilities from noisy labels via an exponential tilting density ratio model, enabling valid error control without prior knowledge of the noise transition matrix. The approach combines semiparametric estimation theory with a practical EM algorithm, yielding classifiers that satisfy NP oracle inequalities asymptotically.

This paper addresses the critical challenge of detecting occult hemorrhage (internal bleeding) in intensive care units, where delayed diagnosis leads to preventable physiological shock and death. The authors develop a Bayesian regime switching model (RSM) that tracks five latent physiological states—including stable, hemorrhage, and recovery—using longitudinal vital signs (heart rate, MAP, hemoglobin, lactate) and medication history. Applied to 33,924 Mayo Clinic ICU encounters, the model aims to provide interpretable, probabilistic early warnings that outperform standard vital sign monitoring by accounting for autoregressive trends and pre-admission physiological changes.

Paper introduces PnPMass, a plug-and-play framework for weak lensing mass mapping that reconciles reconstruction accuracy with practical deployment constraints of upcoming Stage-IV surveys. The key innovation is a carefully chosen data-fidelity operator that decouples denoiser training from observation-specific noise statistics, enabling a single trained model to handle varying survey conditions without retraining. Coupled with moment-network-based uncertainty quantification and conformal calibration, the method offers fast inference with coverage guarantees, addressing limitations of both end-to-end deep learning and costly MCMC sampling approaches.

ALMAB-DC unifies Gaussian process active learning, multi-armed bandit scheduling, and asynchronous distributed computing to tackle expensive black-box optimization in sequential experimental design. The framework targets dose-finding, spatial field estimation, and ML/engineering tasks, claiming superior sample efficiency and near-linear parallel speedups up to $K=16$ agents. While the modular architecture and ablation analyses are rigorous, all empirical results derive from calibrated surrogate emulators rather than live systems, substantially limiting external validity.

This paper addresses brain encoding and decoding by focusing on the alignment step between fMRI neural representations and visual stimulus embeddings. The authors propose two lightweight statistical learning methods—Inverse Semi-supervised Learning (ISL) and Meta Transfer Learning (MTL)—that operate with frozen encoders and decoders to improve sample efficiency under limited paired data and subject heterogeneity. The core innovation lies in leveraging abundant unpaired stimuli through inverse mapping with residual debiasing, and borrowing strength across subjects via sparse aggregation, all while maintaining rigorous theoretical guarantees.

Constraint-based causal discovery algorithms like PC require exponentially many conditional independence (CI) tests in the worst case---specifically $p^{\mathcal{O}(d)}$ where $d$ is the maximum degree. This paper establishes that the fundamental complexity parameter is actually $s$, the maximum undirected clique size in the essential graph, which can be much smaller than $d$ (e.g., $s=2$ vs $d=p-2$ in Figure 1). The authors propose Greedy Ancestral Search (GAS), which achieves $p^{\mathcal{O}(s)}$ CI tests, and prove a matching lower bound of $2^{\Omega(s)}$, establishing exponent-optimality up to a logarithmic factor.