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.