mailitics

Tag: point

  • AI in Multiple GPUs: Point-to-Point and Collective Operations

    AI in Multiple GPUs: Point-to-Point and Collective Operations Learn PyTorch distributed operations for multi GPU AI workloads The post AI in Multiple GPUs: Point-to-Point and Collective Operations appeared first on Towards Data Science. Lorenzo Cesconetto Go to original source

  • Atlas Gaussian processes on restricted domains and point clouds

    Atlas Gaussian processes on restricted domains and point clouds arXiv:2511.15822v1 Announce Type: new Abstract: In real-world applications, data often reside in restricted domains with unknown boundaries, or as high-dimensional point clouds lying on a lower-dimensional, nontrivial, unknown manifold. Traditional Gaussian Processes (GPs) struggle to capture the underlying geometry in such settings. Some existing methods assume…

  • Fixed-Confidence Multiple Change Point Identification under Bandit Feedback

    Fixed-Confidence Multiple Change Point Identification under Bandit Feedback arXiv:2507.08994v1 Announce Type: new Abstract: Piecewise constant functions describe a variety of real-world phenomena in domains ranging from chemistry to manufacturing. In practice, it is often required to confidently identify the locations of the abrupt changes in these functions as quickly as possible. For this, we introduce…

  • The Mythical Pivot Point from Buy to Build for Data Platforms

    The Mythical Pivot Point from Buy to Build for Data Platforms For companies with data-intensive architectures, there often comes a pivotal point where building in-house data platforms makes more sense than buying off-the-shelf solutions The post The Mythical Pivot Point from Buy to Build for Data Platforms appeared first on Towards Data Science. Ming Gao…

  • WWAggr: A Window Wasserstein-based Aggregation for Ensemble Change Point Detection

    WWAggr: A Window Wasserstein-based Aggregation for Ensemble Change Point Detection arXiv:2506.08066v1 Announce Type: new Abstract: Change Point Detection (CPD) aims to identify moments of abrupt distribution shifts in data streams. Real-world high-dimensional CPD remains challenging due to data pattern complexity and violation of common assumptions. Resorting to standalone deep neural networks, the current state-of-the-art detectors…

  • From a Point to L∞

    From a Point to L∞ Why you should read this  As someone who did a Bachelors in Mathematics I was first introduced to L¹ and L² as a measure of Distance… now it seems to be a measure of error — where have we gone wrong? But jokes aside, there seems to be this misconception that L₁ and L₂…

  • Deep spatio-temporal point processes: Advances and new directions

    Deep spatio-temporal point processes: Advances and new directions arXiv:2504.06364v1 Announce Type: new Abstract: Spatio-temporal point processes (STPPs) model discrete events distributed in time and space, with important applications in areas such as criminology, seismology, epidemiology, and social networks. Traditional models often rely on parametric kernels, limiting their ability to capture heterogeneous, nonstationary dynamics. Recent innovations…

  • Fixed-Budget Change Point Identification in Piecewise Constant Bandits

    Fixed-Budget Change Point Identification in Piecewise Constant Bandits arXiv:2501.12957v1 Announce Type: new Abstract: We study the piecewise constant bandit problem where the expected reward is a piecewise constant function with one change point (discontinuity) across the action space $[0,1]$ and the learner’s aim is to locate the change point. Under the assumption of a fixed…

  • Asymptotically Optimal Search for a Change Point Anomaly under a Composite Hypothesis Model

    Asymptotically Optimal Search for a Change Point Anomaly under a Composite Hypothesis Model arXiv:2412.19392v1 Announce Type: new Abstract: We address the problem of searching for a change point in an anomalous process among a finite set of M processes. Specifically, we address a composite hypothesis model in which each process generates measurements following a common…

  • Adapted Prediction Intervals by Means of Conformal Predictions and a Custom Non-Conformity Score

    Adapted Prediction Intervals by Means of Conformal Predictions and a Custom Non-Conformity Score How confident should I be in a machine learning model’s prediction for a new data point? Could I get a range of likely values? Image by author When working on a supervised task, machine learning models can be used to predict the outcome for…