arXiv:2504.03228v4 Announce Type: replace-cross Abstract: A triangular structural panel data model with additive separable individual-specific effects is used to model the causal effect of a covariate on an outcome variable when there are unobservable confounders with some of them time-invariant.…
arXiv:2509.11381v1 Announce Type: cross Abstract: Recursive decision trees have emerged as a leading methodology for heterogeneous causal treatment effect estimation and inference in experimental and observational settings. These procedures are fitted using the celebrated CART (Classification And Regression Tree) algorithm…
arXiv:2501.06376v2 Announce Type: replace-cross Abstract: In Reward Learning (ReL), we are given feedback on an unknown target reward, and the goal is to use this information to recover it in order to carry out some downstream application, e.g., planning. When…
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…
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:2509.11236v1 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: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:2509.11089v1 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:2509.11007v1 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: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…
