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Tag: metric

  • On Generation in Metric Spaces

    On Generation in Metric Spaces arXiv:2602.07710v1 Announce Type: new Abstract: We study generation in separable metric instance spaces. We extend the language generation framework from Kleinberg and Mullainathan [2024] beyond countable domains by defining novelty through metric separation and allowing asymmetric novelty parameters for the adversary and the generator. We introduce the $(varepsilon,varepsilon’)$-closure dimension, a…

  • Metric Deception: When Your Best KPIs Hide Your Worst Failures

    Metric Deception: When Your Best KPIs Hide Your Worst Failures The most dangerous KPIs aren’t broken; they’re the ones trusted long after they’ve lost their meaning. The post Metric Deception: When Your Best KPIs Hide Your Worst Failures appeared first on Towards Data Science. Shafeeq Ur Rahaman Go to original source

  • Conic Formulations of Transport Metrics for Unbalanced Measure Networks and Hypernetworks

    Conic Formulations of Transport Metrics for Unbalanced Measure Networks and Hypernetworks arXiv:2508.10888v1 Announce Type: new Abstract: The Gromov-Wasserstein (GW) variant of optimal transport, designed to compare probability densities defined over distinct metric spaces, has emerged as an important tool for the analysis of data with complex structure, such as ensembles of point clouds or networks.…

  • Efficient Metric Collection in PyTorch: Avoiding the Performance Pitfalls of TorchMetrics

    Efficient Metric Collection in PyTorch: Avoiding the Performance Pitfalls of TorchMetrics Metric collection is an essential part of every machine learning project, enabling us to track model performance and monitor training progress. Ideally, Metrics should be collected and computed without introducing any additional overhead to the training process. However, just like other components of the…

  • Stop the Count! Why Putting A Time Limit on Metrics is Critical for Fast and Accurate Experiments

    Stop the Count! Why Putting A Time Limit on Metrics is Critical for Fast and Accurate Experiments Why your experiments might never reach significance Photo by Andrik Langfield on Unsplash Introduction Experiments usually compare the frequency of an event (or some other sum metric) after either exposure (treatment) or non-exposure (control) to some intervention. For example:…

  • How to Choose a Threshold for an Evaluation Metric for Large Language Models

    How to Choose a Threshold for an Evaluation Metric for Large Language Models arXiv:2412.12148v1 Announce Type: new Abstract: To ensure and monitor large language models (LLMs) reliably, various evaluation metrics have been proposed in the literature. However, there is little research on prescribing a methodology to identify a robust threshold on these metrics even though…