arXiv:2506.10899v2 Announce Type: replace Abstract: We address the problem of causal effect estimation in the presence of hidden confounders, using nonparametric instrumental variable (IV) regression. A leading strategy employs spectral features – that is, learned features spanning the top eigensubspaces…
arXiv:2509.18349v1 Announce Type: new Abstract: Meta-learning has emerged as a powerful paradigm for leveraging information across related tasks to improve predictive performance on new tasks. In this paper, we propose a statistical framework for analyzing meta-learning through the lens of…
arXiv:2509.19189v1 Announce Type: cross Abstract: Scaling laws have played a cornerstone role in guiding the training of large language models (LLMs). However, most existing works on scaling laws primarily focus on the final-step loss, overlooking the loss dynamics during the…
arXiv:2509.19276v1 Announce Type: new Abstract: Solving ill-posed inverse problems requires powerful and flexible priors. We propose leveraging pretrained latent diffusion models for this task through a new training-free approach, termed Diffusion-regularized Wasserstein Gradient Flow (DWGF). Specifically, we formulate the posterior…
arXiv:2504.12617v2 Announce Type: replace-cross Abstract: We introduce a novel and scalable Bayesian framework for multivariate-density-density regression (DDR), designed to model relationships between multivariate distributions. Our approach addresses the critical issue of distributions residing in spaces of differing dimensions. We utilize…
arXiv:2509.19226v1 Announce Type: new Abstract: This paper proposes the use of the Hellinger–Kantorovich metric from unbalanced optimal transport (UOT) in a dimensionality reduction and learning (supervised and unsupervised) pipeline. The performance of UOT is compared to that of regular OT…
arXiv:2509.19250v1 Announce Type: new Abstract: This paper proposes two algorithms for estimating square Wasserstein distance matrices from a small number of entries. These matrices are used to compute manifold learning embeddings like multidimensional scaling (MDS) or Isomap, but contrary to…
arXiv:2509.18739v1 Announce Type: new Abstract: This paper studies how insurers can chose which claims to investigate for fraud. Given a prediction model, typically only claims with the highest predicted propability of being fraudulent are investigated. We argue that this can…
arXiv:2509.18484v1 Announce Type: new Abstract: Estimating causal effects on networks is important for both scientific research and practical applications. Unlike traditional settings that assume the Stable Unit Treatment Value Assumption (SUTVA), interference allows an intervention/treatment on one unit to affect…
arXiv:2509.18477v1 Announce Type: new Abstract: The end-cut preference (ECP) problem, referring to the tendency to favor split points near the boundaries of a feature’s range, is a well-known issue in CART (Breiman et al., 1984). ECP may induce highly imbalanced…
