January 2026

arXiv:2601.18145v1 Announce Type: new Abstract: Computation of confidence sets is central to data science and machine learning, serving as the workhorse of A/B testing and underpinning the operation and analysis of reinforcement learning algorithms. Among all valid confidence sets for the multinomial parameter, minimum-volume confidence sets (MVCs) are optimal in that they minimize average volume, but they are defined as level sets of an exact p-value that is discontinuous and difficult to compute. Rather than attempting to characterize […]

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arXiv:2601.18128v1 Announce Type: new Abstract: High-dimensional data often exhibit variation that can be captured by lower dimensional factors. For high-dimensional data from multiple studies or environments, one goal is to understand which underlying factors are common to all studies, and which factors are study or environment-specific. As a particular example, we consider platelet gene expression data from patients in different disease groups. In this data, factors correspond to clusters of genes which are co-expressed; we may expect some […]

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arXiv:2601.17990v1 Announce Type: new Abstract: Shaping multi-megawatt loads, such as data centers, impacts generator dispatch on the electric grid, which in turn affects system CO2 emissions and energy cost. Substantiating the effectiveness of prevalent load shaping strategies, such as those based on grid-level average carbon intensity, locational marginal price, or marginal emissions, is challenging due to the lack of detailed counterfactual data required for accurate attribution. This study uses a series of calibrated granular ERCOT day-ahead direct current […]

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arXiv:2601.17510v1 Announce Type: new Abstract: This article presents the full, original record of the 2024 Joint Statistical Meetings (JSM) town hall, “Statistics in the Age of AI,” which convened leading statisticians to discuss how the field is evolving in response to advances in artificial intelligence, foundation models, large-scale empirical modeling, and data-intensive infrastructures. The town hall was structured around open panel discussion and extensive audience Q&A, with the aim of eliciting candid, experience-driven perspectives rather than formal presentations […]

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arXiv:2601.17139v1 Announce Type: new Abstract: Civil society has been deemed by various scholars, such as Robert D. Putnam, to be a predictor and a cornerstone of a robust and consolidated democracy (Putnam et al., 1993). Putnam highlights in his book Bowling Alone (2000) that American civil society has become weaker: people organize less, and literally, they bowl alone. But what if there is yet another aspect to Putnam’s story that has not been fully accounted for, namely the […]

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arXiv:2601.17136v1 Announce Type: new Abstract: Clustering is an important tool in data analysis, with K-means being popular for its simplicity and versatility. However, it cannot handle non-linearly separable clusters. Kernel K-means addresses this limitation but requires a large kernel matrix, making it computationally and memory intensive. Prior work has accelerated Kernel K-means by formulating it using sparse linear algebra primitives and implementing it on a single GPU. However, that approach cannot run on datasets with more than approximately […]

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arXiv:2601.17135v1 Announce Type: new Abstract: Imitation learning enables robots to acquire complex manipulation skills from human demonstrations, but current methods rely solely on low-level sensorimotor data while ignoring the rich semantic knowledge humans naturally possess about tasks. We present ConceptACT, an extension of Action Chunking with Transformers that leverages episode-level semantic concept annotations during training to improve learning efficiency. Unlike language-conditioned approaches that require semantic input at deployment, ConceptACT uses human-provided concepts (object properties, spatial relationships, task constraints) […]

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arXiv:2601.17134v1 Announce Type: new Abstract: Understanding and modeling consumers’ stylistic taste such as “sporty” is crucial for creating designs that truly connect with target audiences. However, capturing taste during the design process remains challenging because taste is abstract and subjective, and preference data alone provides limited guidance for concrete design decisions. This paper proposes an integrated human-centered computational framework that links subjective evaluations (e.g., perceived luxury of car wheels) with domain-specific features (e.g., spoke configuration) and computer vision-based […]

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arXiv:2601.17133v1 Announce Type: new Abstract: Fine-tuning Large Language Models (LLMs) for specialized domains is constrained by a fundamental challenge: the need for diverse, cross-organizational data conflicts with the principles of data privacy and sovereignty. While Federated Learning (FL) provides a framework for collaboration without raw data exchange, its classic centralized form introduces a single point of failure and remains vulnerable to model inversion attacks. Decentralized FL (DFL) mitigates this risk by removing the central aggregator but typically relies […]

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arXiv:2601.17132v1 Announce Type: new Abstract: Few forces rival fear in their ability to mobilize societies, distort communication, and reshape collective behavior. In computational linguistics, fear is primarily studied as an emotion, but not as a distinct form of speech. Fear speech content is widespread and growing, and often outperforms hate-speech content in reach and engagement because it appears “civiler” and evades moderation. Yet the computational study of fear speech remains fragmented and under-resourced. This can be understood by […]

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