arXiv:2601.11091v1 Announce Type: new Abstract: In this paper, we propose a distributed framework for reducing the dimensionality of high-dimensional, large-scale, heterogeneous matrix-variate time series data using a factor model. The data are first partitioned column-wise (or row-wise) and allocated to node servers, where each node estimates the row (or column) loading matrix via two-dimensional tensor PCA. These local estimates are then transmitted to a central server and aggregated, followed by a final PCA step to obtain the global […]

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arXiv:2603.25622v1 Announce Type: cross Abstract: We present an efficient algorithm for uniformly sampling from an arbitrary compact body $mathcal{X} subset mathbb{R}^n$ from a warm start under isoperimetry and a natural volume growth condition. Our result provides a substantial common generalization of known results for convex bodies and star-shaped bodies. The complexity of the algorithm is polynomial in the dimension, the Poincar’e constant of the uniform distribution on $mathcal{X}$ and the volume growth constant of the set $mathcal{X}$.

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arXiv:2603.23679v1 Announce Type: new Abstract: Agriculture remains a cornerstone of global health and economic sustainability, yet labor-intensive tasks such as harvesting high-value crops continue to face growing workforce shortages. Robotic harvesting systems offer a promising solution; however, their deployment in unstructured orchard environments is constrained by inefficient perception-to-action pipelines. In particular, existing approaches often rely on exhaustive inverse kinematics or motion planning to determine whether a target fruit is reachable, leading to unnecessary computation and delayed decision-making. Our […]

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arXiv:2605.00005v1 Announce Type: new Abstract: The increasing deployment of deep neural networks (DNNs) in cyber-physical systems (CPS) enhances perception fidelity, but imposes substantial computational demands on execution platforms, posing challenges to real-time control deadlines. Traditional distributed CPS architectures typically favor on-device inference to avoid network variability and contention-induced delays on remote platforms. However, this design choice places significant energy and computational demands on the local hardware. In this work, we revisit the assumption that cloud-based inference is intrinsically […]

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Machine learning competitions (MLCs) play a pivotal role in advancing artificial intelligence (AI) by fostering innovation, skill development, and practical problem-solving. This study provides a comprehensive analysis of major competition platforms such as Kaggle and Zindi, examining their workflows, evaluation methodologies, and reward structures. It further assesses competition quality, participant expertise, and global reach, with particular attention to demographic trends among top-performing competitors. By exploring the motivations of competition hosts, this paper underscores the significant role of MLCs […]

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Hi! I’ve tried to implement PPO for Mujoco based only on the paper and resources available at the time of publication, without looking at any existing implementations of the algorithm. I have now compared my implementation to the relevant details listed in The 37 Implementation Details of Proximal Policy Optimization, and it turns out I missed most details, see below. My question is: Were these details documented somewhere, or have they been known implicitly in the community at […]

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I’m an independent researcher, looking for an arXiv endorsement to submit a paper on adaptive, RL-driven PHI de-identification for streaming multimodal healthcare data. Venkata Krishna Azith Teja Ganti requests your endorsement to submit an article to the cs.LG section of arXiv. To tell us that you would (or would not) like to endorse this person, please visit the following URL: https://arxiv.org/auth/endorse?x=88IFE7 If that URL does not work for you, please visit http://arxiv.org/auth/endorse.php and enter the following six-digit alphanumeric […]

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