arXiv:2512.09067v1 Announce Type: new Abstract: Neural networks, while effective for tackling many challenging scientific tasks, are not known to perform well out-of-distribution (OOD), i.e., within domains which differ from their training data. Understanding neural network OOD generalization is paramount to…
arXiv:2502.04499v2 Announce Type: replace Abstract: Knowledge distillation (KD) is a popular method of transferring knowledge from a large “teacher” model to a small “student” model. Previous work has explored various layer-selection strategies (e.g., forward matching and in-order random matching) for…
arXiv:2512.09074v1 Announce Type: new Abstract: Severe heatwaves in urban areas significantly threaten public health, calling for establishing early warning strategies. Despite predicting occurrence of heatwaves and attributing historical mortality, predicting an incoming deadly heatwave remains a challenge due to the…
arXiv:2505.17918v2 Announce Type: replace Abstract: Tabular data have been playing a vital role in diverse real-world fields, including healthcare, finance, etc. With the recent success of Large Language Models (LLMs), early explorations of extending LLMs to the domain of tabular…
arXiv:2512.09076v1 Announce Type: new Abstract: Accurate forecasting of urban air pollution is essential for protecting public health and guiding mitigation policies. While Deep Learning (DL) and hybrid pipelines dominate recent research, their complexity and limited interpretability hinder operational use. This…
arXiv:2512.09084v1 Announce Type: new Abstract: The Kolmogorov-Arnold representation theorem offers a theoretical alternative to Multi-Layer Perceptrons (MLPs) by placing learnable univariate functions on edges rather than nodes. While recent implementations such as Kolmogorov-Arnold Networks (KANs) demonstrate high approximation capabilities, they…
arXiv:2510.09660v4 Announce Type: replace Abstract: Diffusion Probabilistic Models (DPMs) have achieved strong generative performance, yet their inductive biases remain largely implicit. In this work, we aim to build inductive biases into the training and sampling of diffusion models to better…
arXiv:2512.09103v1 Announce Type: new Abstract: Data attribution methods identify which training examples are responsible for a model’s predictions, but their sensitivity to distributional perturbations undermines practical reliability. We present a unified framework for certified robust attribution that extends from convex…
arXiv:2511.19253v2 Announce Type: replace Abstract: Cooperative Multi-Agent Reinforcement Learning (MARL) faces two major design bottlenecks: crafting dense reward functions and constructing curricula that avoid local optima in high-dimensional, non-stationary environments. Existing approaches rely on fixed heuristics or use Large Language…
arXiv:2512.09106v1 Announce Type: new Abstract: Diffusion (Large) Language Models (dLLMs) now match the downstream performance of their autoregressive counterparts on many tasks, while holding the promise of being more efficient during inference. One particularly successful variant is masked discrete diffusion,…
