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Tag: discovery

  • Membership Inference Attacks with False Discovery Rate Control

    Membership Inference Attacks with False Discovery Rate Control arXiv:2508.07066v1 Announce Type: new Abstract: Recent studies have shown that deep learning models are vulnerable to membership inference attacks (MIAs), which aim to infer whether a data record was used to train a target model or not. To analyze and study these vulnerabilities, various MIA methods have…

  • Probably Approximately Correct Causal Discovery

    Probably Approximately Correct Causal Discovery arXiv:2507.18903v1 Announce Type: new Abstract: The discovery of causal relationships is a foundational problem in artificial intelligence, statistics, epidemiology, economics, and beyond. While elegant theories exist for accurate causal discovery given infinite data, real-world applications are inherently resource-constrained. Effective methods for inferring causal relationships from observational data must perform well…

  • Getting AI Discovery Right

    Getting AI Discovery Right A guide to ideating, validating, and prioritizing your AI use cases The post Getting AI Discovery Right appeared first on Towards Data Science. Dr. Janna Lipenkova Go to original source

  • Lie Group Symmetry Discovery and Enforcement Using Vector Fields

    Lie Group Symmetry Discovery and Enforcement Using Vector Fields arXiv:2505.08219v1 Announce Type: new Abstract: Symmetry-informed machine learning can exhibit advantages over machine learning which fails to account for symmetry. Additionally, recent attention has been given to continuous symmetry discovery using vector fields which serve as infinitesimal generators for Lie group symmetries. In this paper, we…

  • Hypothesis-free discovery from epidemiological data by automatic detection and local inference for tree-based nonlinearities and interactions

    Hypothesis-free discovery from epidemiological data by automatic detection and local inference for tree-based nonlinearities and interactions arXiv:2505.00571v1 Announce Type: new Abstract: In epidemiological settings, Machine Learning (ML) is gaining popularity for hypothesis-free discovery of risk (or protective) factors. Although ML is strong at discovering non-linearities and interactions, this power is currently compromised by a lack…

  • SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph

    SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph arXiv:2502.07857v1 Announce Type: new Abstract: Causal discovery can be computationally demanding for large numbers of variables. If we only wish to estimate the causal effects on a small subset of target variables, we might not need to learn the causal graph for…