Kim et al. reply
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New research quantifies variability in AI concept explanations
A new paper published on arXiv analyzes the variability of Concept Activation Vectors (CAVs), a method used in explainable AI to provide high-level concept explanations. The research, led by Julia Wenkmann, theoreticall…
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New arXiv Paper Offers Sharp Bounds for Causal Inference with Unobserved Confounding
A new research paper published on arXiv introduces a method for bounding continuous treatment effects in the presence of unobserved confounding. The study focuses on identifying the conditional average potential outcome…
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New methods enable language models to perform "any-order inference"
Researchers have developed new methods to enable language models to perform "any-order inference," a non-causal reasoning process similar to how programmers draft code by moving between high-level concepts and specific …
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New study finds common time-series anomaly detection metrics are gameable
A new paper critically examines evaluation metrics for time-series anomaly detection (TSAD), finding that many commonly used metrics are susceptible to being gamed by simple, no-skill score generators. The research test…
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New Mean Field Approach for High-Dimensional Linear Regression
This paper introduces a novel mean field approach for empirical Bayes estimation in high-dimensional linear regression. The method utilizes a variational empirical Bayes technique to efficiently estimate the underlying …