arXiv:2408.13430v3 Announce Type: replace-cross Abstract: We conducted an experiment during the review process of the 2023 International Conference on Machine Learning (ICML), asking authors with multiple submissions to rank their papers based on perceived quality. In total, we received 1,342…
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…
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:2509.18739v1 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:2509.19250v1 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:2509.19226v1 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: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:2509.19276v1 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: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:2509.18349v1 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…
