arXiv:2509.10693v1 Announce Type: cross Abstract: This work proposes a bid shading strategy for first-price auctions as a measure-valued optimization problem. We consider a standard parametric form for bid shading and formulate the problem as convex optimization over the joint distribution…
arXiv:2410.14090v2 Announce Type: replace Abstract: Projection-based model reduction is among the most widely adopted methods for constructing parametric Reduced-Order Models (ROM). Utilizing the snapshot data from solving full-order governing equations, the Proper Orthogonal Decomposition (POD) computes the optimal basis modes…
arXiv:2509.10825v1 Announce Type: cross Abstract: We propose FACTORS, a framework that combines design of experiments with Shapley decomposition to address performance and stability issues that are sensitive to combinations of training factors. Our approach consistently estimates main effects and two-factor…
arXiv:2505.04613v2 Announce Type: replace Abstract: We prove that kernel covariance embeddings lead to information-theoretically perfect separation of distinct probability distributions. In statistical terms, we establish that testing for the equality of two probability measures on a compact and separable metric…
arXiv:2509.11007v1 Announce Type: cross Abstract: Part I of this work [Gao25] establishes online scaled gradient methods (OSGM), a framework that utilizes online convex optimization to adapt stepsizes in gradient methods. This paper focuses on the practical aspects of OSGM. We…
arXiv:2509.11089v1 Announce Type: cross Abstract: For premium consumer products, pricing strategy is not about a single number, but about understanding the perceived monetary value of the features that justify a higher cost. This paper proposes a robust methodology to deconstruct…
arXiv:2509.09078v2 Announce Type: replace Abstract: Given-data methods for variance-based sensitivity analysis have significantly advanced the feasibility of Sobol’ index computation for computationally expensive models and models with many inputs. However, the limitations of existing methods still preclude their application to…
arXiv:2509.11236v1 Announce Type: cross Abstract: We study online Riemannian optimization on Hadamard manifolds under the framework of horospherical convexity (h-convexity). Prior work mostly relies on the geodesic convexity (g-convexity), leading to regret bounds scaling poorly with the manifold curvature. To…
arXiv:2211.14908v3 Announce Type: replace-cross Abstract: The kernel Maximum Mean Discrepancy~(MMD) is a popular multivariate distance metric between distributions that has found utility in two-sample testing. The usual kernel-MMD test statistic is a degenerate U-statistic under the null, and thus it…
arXiv:2509.11265v1 Announce Type: cross Abstract: Deep neural networks tend to memorize noisy labels, severely degrading their generalization performance. Although Mixup has demonstrated effectiveness in improving generalization and robustness, existing Mixup-based methods typically perform indiscriminate mixing without principled guidance on sample…
