α-TCAV replaces TCAV's hard indicator with a tunable smooth function to create a unified probabilistic framework with lower variance and guidance for parameter choice or Bayes-optimal scoring.
Proceedings of the sixth Berkeley symposium on mathematical statistics and probability, volume 2: Probability theory , volume=
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TriOpt recovers topological order via Sherman-Morrison downdates on linear kernels then solves a convex program for the DAG edges, claiming exact recovery under the true order and large speedups on high-dimensional data.
Proves sharp operator-norm concentration and expectation bounds for sample cross-covariances of sub-Gaussian and Gaussian vectors, governed by effective ranks of the marginal covariances.
citing papers explorer
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$\alpha$-TCAV: A Unified Framework for Testing with Concept Activation Vectors
α-TCAV replaces TCAV's hard indicator with a tunable smooth function to create a unified probabilistic framework with lower variance and guidance for parameter choice or Bayes-optimal scoring.
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TriOpt: A Scalable Algorithm for Linear Causal Discovery
TriOpt recovers topological order via Sherman-Morrison downdates on linear kernels then solves a convex program for the DAG edges, claiming exact recovery under the true order and large speedups on high-dimensional data.
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Concentration Inequalities for Sample Cross-Covariances
Proves sharp operator-norm concentration and expectation bounds for sample cross-covariances of sub-Gaussian and Gaussian vectors, governed by effective ranks of the marginal covariances.