arXiv:2502.01226v2 Announce Type: replace-cross Abstract: Gaussian process (GP) bandits provide a powerful framework for performing blackbox optimization of unknown functions. The characteristics of the unknown function depends heavily on the assumed GP prior. Most work in the literature assume that…
arXiv:2509.22531v1 Announce Type: new Abstract: In observational settings where treatment and outcome share unmeasured confounders but an observed mediator remains unconfounded, the front-door (FD) adjustment identifies causal effects through the mediator. We study the heterogeneous treatment effect (HTE) under FD…
arXiv:2509.22549v1 Announce Type: new Abstract: We introduce a general framework for analyzing data modeled as parameterized families of networks. Building on a Gromov-Wasserstein variant of optimal transport, we define a family of parameterized Gromov-Wasserstein distances for comparing such parametric data,…
arXiv:2509.22553v1 Announce Type: new Abstract: Causal representation learning (CRL) has garnered increasing interests from the causal inference and artificial intelligence community, due to its capability of disentangling potentially complex data-generating mechanism into causally interpretable latent features, by leveraging the heterogeneity…
arXiv:2509.22088v1 Announce Type: cross Abstract: We propose a novel conditional diffusion model for portfolio optimization that learns the cross-sectional distribution of next-day stock returns conditioned on asset-specific factors. The model builds on the Diffusion Transformer with token-wise conditioning, linking each…
arXiv:2509.22633v1 Announce Type: new Abstract: Reinforcement learning with human feedback (RLHF), which learns a reward model from human preference data and then optimizes a policy to favor preferred responses, has emerged as a central paradigm for aligning large language models…
arXiv:2509.22122v1 Announce Type: cross Abstract: This study considers the estimation of the average treatment effect (ATE). For ATE estimation, we estimate the propensity score through direct bias-correction term estimation. Let ${(X_i, D_i, Y_i)}_{i=1}^{n}$ be the observations, where $X_i in mathbb{R}^p$…
arXiv:2509.22240v1 Announce Type: cross Abstract: In clinical applications, the utility of segmentation models is often based on the accuracy of derived downstream metrics such as organ size, rather than by the pixel-level accuracy of the segmentation masks themselves. Thus, uncertainty…
arXiv:2509.22446v1 Announce Type: cross Abstract: Double robustness is a major selling point of semiparametric and missing data methodology. Its virtues lie in protection against partial nuisance misspecification and asymptotic semiparametric efficiency under correct nuisance specification. However, in many applications, complete…
arXiv:2509.22613v1 Announce Type: cross Abstract: Recent reinforcement learning (RL) methods have substantially enhanced the planning capabilities of Large Language Models (LLMs), yet the theoretical basis for their effectiveness remains elusive. In this work, we investigate RL’s benefits and limitations through…
