This paper studies a dynamic assortment problem on a two-sided service platform with incomplete information and heterogeneous customers, and develops a data-driven algorithm to learn parameters and op…

This paper presents a compositional cost analysis for probabilistic programs with hierarchical cost structures, allowing computation of mean and higher moments of non-additive costs.

The paper introduces Hierarchical Adaptive Budgeter (HAB), a framework that improves LLM reasoning efficiency by adaptively allocating computational resources to match the intrinsic complexity of both…

This paper proposes an approximation algorithm for the submodular joint replenishment problem with decomposable submodular ordering cost functions, achieving an O(k)-approximation.

The paper proposes PatentXAI, a scalable framework that uses graph-conditioned Shapley values to fairly attribute product profit among thousands of patents, significantly improving computational tract…

This paper analyzes data-access cost in a memory hierarchy and shows it scales with the fourth root of data size, predicting scalability.

This paper introduces Neural Certificate Pricing (NCP), an unsupervised learning framework that exploits the asymmetry between certifiable discrete structures and structural feasibility in combinatori…

This paper proposes Hierarchical Block-Local Learning (HBLL), a framework for training deep neural networks without full end-to-end backpropagation, achieving $\mathcal{O}(\log N)$ parallel time compl…

APEX-Accounting benchmark is introduced to assess the capability of frontier models in performing accounting tasks. Claude-Fable-5 (Max) outperforms other models with 56.4% Mean Criteria@3.

This paper presents an optimized algorithm for computing cut sets of a path set in graph theory and introduces a vectorized computational framework for property calculations.

This paper presents three new algorithms for maximizing revenue in the Stackelberg Vertex Cover problem on certain kinds of trees.

This paper studies ranking and aggregation under Kendall tau distance with matroid or flag matroid constraints on prefixes.

This paper reports on SciGMark 1.5, a benchmark study of specialized and generic implementations in modern languages, examining the consequences of various generic-realization strategies and extending…

This paper proposes a reliability-aware framework to solve the fuzzy shortest path problem in directed graphs, optimizing routes based not only on cost but also on the reliability of the associated fu…

This paper proposes a hierarchical reinforcement learning framework to adapt interdependent long-term and short-term policies in global operations, improving resilience and profit.

This paper presents Calf, a dependent type theory for cost verification, which synthesizes the physicist's and banker's views on amortized analysis using potential functions and credit annotations.

The paper proposes a robust, multi-stage pipeline combining rule-based classification and machine learning to map noisy retail product names to standardized consumption categories, finding that simple…

The paper presents an adaptive scaling algorithm with a competitive ratio of 1.373 for incremental submodular maximization under increasing cardinality constraint, improving upon the previous best res…