support-conditioned boundary estimation

This paper addresses the challenge of vast and structurally complex decision boundaries in DNA read mapping to reference genomes in next-generation sequencing (NGS). It investigates the boundary properties of naïve Bayes classifiers under graph-structured input spaces. To this end, the authors propose “neighborhood similarity” — a novel uncertainty measure that is both theoretically interpretable and universally computable, overcoming the reliance of conventional Bayesian confidence on model outputs. Leveraging graph-model-driven boundary analysis, neighborhood distribution statistics, and uncertainty quantification, the study reveals the high-dimensional complexity of decision boundaries and proves that the proposed measure simultaneously captures intrinsic Bayesian uncertainty. Moreover, it seamlessly extends to black-box classifiers lacking built-in confidence mechanisms. Empirically, neighborhood similarity significantly enhances classification interpretability and robustness, offering a principled framework for uncertainty-aware read mapping in NGS applications.

本文提出了一种基于上下文自适应阈值的方法，以解决分类器和监控程序在重要外部变量条件下分布代表性不足的问题。

This paper addresses the challenge of simultaneously achieving scalability, local interpretability, and multi-attribute, multi-class fairness in rule-based classification models. To this end, we propose the first column generation–based rule learning framework. Methodologically, we introduce column generation—previously unexplored in rule learning—integrating a linear programming master problem, a decision-tree–inspired column generation heuristic, a surrogate pricing subproblem solver, and weighted rule optimization; we further formulate generalized fairness constraints supporting multiple sensitive attributes and multi-class outcomes. Our key contributions are: (1) enabling local interpretability via rule weights, and (2) unifying support for complex fairness constraints and scalable search over large rule spaces. Extensive experiments on benchmark datasets demonstrate that our approach achieves significant trade-off improvements among accuracy, interpretability, and fairness, substantially enhancing the practicality of rule models in real-world, large-scale applications.

Few-Shot Class-Incremental Learning (FSCIL) confronts three key challenges: rigid decision boundaries, weak inter-class discriminability, and catastrophic forgetting. To address these, we propose the Adaptive Decision Boundary Strategy (ADBS), which learns category-specific decision boundaries that dynamically adapt during incremental learning. A novel inter-class constraint loss is further introduced to jointly optimize boundary parameters and class prototypes. Our method adopts a plug-and-play architecture—requiring no modification to the backbone network—ensuring seamless integration with existing FSCIL frameworks. Extensive experiments on CIFAR-100, miniImageNet, and CUB-200 demonstrate consistent and significant performance gains over state-of-the-art FSCIL approaches, achieving new SOTA results. Notably, ADBS exhibits strong generalization under low-shot settings and multi-stage incremental scenarios, validating its robustness and scalability across diverse data regimes and incremental protocols.

For black-box models applied to complex tasks such as image segmentation, defining meaningful conditional events is challenging, leading to uncertainty estimates that fail to reflect inherent sample difficulty. Method: This paper proposes an input-dependent statistical risk control framework grounded in conformal prediction. It introduces a novel, algorithm-driven mechanism for dynamically selecting conditional function classes—bypassing manual discretization—by adaptively constructing these classes based on test-sample difficulty and integrating online parameter tuning for fine-grained, approximately conditional risk control. Contribution/Results: Experiments on regression and image segmentation demonstrate substantial improvements in uncertainty calibration accuracy. The method guarantees strict statistical risk control while enhancing generalization robustness and predictive reliability.

This study addresses the challenge of simultaneously controlling both types of error in binary classification tasks, where overlapping class distributions inherently hinder such control. Within the Neyman-Pearson framework, this work introduces a rejection mechanism and proposes a model-agnostic joint calibration strategy to achieve selective dual error rate control. Furthermore, by leveraging martingale theory to precisely compute finite-sample crossing probabilities, the method ensures strict statistical validity without requiring multiple testing corrections. This approach overcomes the limitations of conventional threshold search procedures by guaranteeing that both error types strictly satisfy their predefined bounds. Its effectiveness is empirically validated in high-stakes applications, including recidivism prediction and credit default assessment, thereby providing reliable statistical safeguards for critical decision-making scenarios.

This study investigates why models with comparable predictive performance exhibit significant differences in the feasibility and proximity of their counterfactual explanations. By fixing a pretrained encoder and varying only the linear classification head, and by integrating standardized local search probes with geometric analysis of the representation space, the work demonstrates that counterfactual behavior constitutes a dimension distinct from predictive accuracy. The findings reveal that the interplay between the decision boundary geometry and local data support jointly determines counterfactual feasibility. Leveraging this insight enables improved counterfactual generation within a fixed model architecture without compromising predictive performance.

本文探讨了在场景优化和无分布认证中，通过确定性边界机制及随机观察边界大小来精确计算违反风险的方法。

This work addresses the limitations of traditional decision boundary maps (DBMs) in high-dimensional settings, where reliance on dimensionality reduction in the original feature space often leads to class overlap and ambiguous visualizations. To overcome this, the study introduces Shapley values into DBM construction for the first time, mapping data into a Shapley value space before applying dimensionality reduction techniques such as t-SNE or UMAP. This approach substantially enhances class separability and structural compactness in the resulting visualizations. Empirical evaluations demonstrate that the proposed method matches or exceeds state-of-the-art alternatives across established visualization quality metrics, yielding decision boundary maps that are not only clearer but also more interpretable—thereby facilitating deeper exploration and understanding of decision-making behaviors in high-dimensional models.

This work addresses the potential nonexistence of a global optimum in linear ensembles of multiple binary classifiers by proposing a theoretical framework grounded in truth-table logical structuring and equivalence class partitioning, which establishes sufficient conditions for the existence of a convexified empirical risk minimizer. By introducing a multidimensional generalization of classification-calibrated loss functions and the notion of φ-frontiers, the study analyzes solution stability in relation to data quality. Under exponential (Boost) and logistic (Logit) losses, the authors derive, for the first time, explicit closed-form expressions for the optimal ensemble weights and fully characterize all solution regimes in the three-classifier setting. This approach circumvents iterative optimization, thereby substantially enhancing both the interpretability and computational efficiency of ensemble models.