arXiv:2604.26128v1 Announce Type: new Abstract: We consider learning from labeled data collected across multiple environments, where the data distribution may vary across these environments. This problem is commonly approached from a causal perspective, seeking invariant representations that retain causal factors while discarding spurious ones. However, this framework assumes that the environment has no direct effect on the target. In contrast, we consider settings in which this assumption fails, but still aim to learn representations that support robust prediction […]

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arXiv:2601.08870v1 Announce Type: new Abstract: Artificial intelligence (AI) has become a transformative force across global societies, reshaping the ways we communicate, collaborate, and make decisions. Yet, as AI systems increasingly mediate interactions between humans, questions about the ability to take into account and understand culture, language, and context have taken center stage. This book explores these questions through a series of articles that try to assess AI’s capacity to navigate cross-cultural, multilingual, and high-stakes policy environments, emphasizing human-centered […]

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arXiv:2505.13660v2 Announce Type: replace-cross Abstract: This paper introduces a new constraint-free concave dual formulation for the Wasserstein barycenter. Tailoring the vanilla dual gradient ascent algorithm to the Sobolev geometry, we derive a scalable Sobolev gradient ascent (SGA) algorithm to compute the barycenter for input distributions discretized over a regular grid. Despite the algorithmic simplicity, we provide a global convergence analysis that achieves the same rate as the classical subgradient descent methods for minimizing nonsmooth convex functions in the […]

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arXiv:2603.23682v1 Announce Type: new Abstract: The rapid adoption of large language models (LLMs) in education raises profound challenges for assessment design. To adapt assessments to the presence of LLM-based tools, it is crucial to characterize the strengths and weaknesses of LLMs in a generalizable, valid and reliable manner. However, current LLM evaluations often rely on descriptive statistics derived from benchmarks, and little research applies theory-grounded measurement methods to characterize LLM capabilities relative to human learners in ways that […]

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Hey there. I’m currently writing an assignment paper comparing the performance of various deep RL algorithms for continuous control. All was going pretty smoothly, until I hit a wall with finding publicly available data for MuJoCo v4/v5 environments. I searched the most common sources, such as algorithm implementation papers or StableBaselines / Tianshou repositories, but almost all reported results are based on older MuJoCo versions (v1/v2/v3), which are not really comparable to the modern environments. If anyone knows […]

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arXiv:2604.09806v1 Announce Type: new Abstract: We study the standard-form ILP problem $max{ c^top x colon A x = b,; x in Z_{geq 0}^n }$, where $Ain Z^{ktimes n}$ has full row rank. We obtain refined FPT algorithms parameterized by $k$ and $Delta$, the maximum absolute value of a $ktimes k$ minor of $A$. Our approach combines discrepancy-based dynamic programming with matrix discrepancy bounds in Koml’os’ setting. Let $kappa_k$ denote the maximum discrepancy over all matrices with $k$ columns […]

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Clinical risk prediction models often underperform in real-world settings due to poor calibration, limited transportability, and subgroup disparities. These challenges are amplified in high-dimensional multimodal cancer datasets characterized by complex feature interactions and a p >> n structure. We present a fully reproducible multimodal machine learning framework for 5-year overall survival prediction in breast cancer, integrating clinical variables with high-dimensional transcriptomic and copy-number alteration (CNA) features from the METABRIC cohort. After variance- and sparsity-based filtering and dimensionality reduction, […]

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arXiv:2603.20903v1 Announce Type: cross Abstract: Data unfolding — the removal of noise or artifacts from measurements — is a fundamental task across the experimental sciences. Of particular interest are applications in physics, where the dominant approach is Richardson-Lucy (RL) deconvolution. The classical RL approach aims to find denoised data that, once passed through the noise model, is as close as possible to the measured data in terms of Kullback-Leibler (KL) divergence. This requires that the support of the […]

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