A kernel-copula embedding statistic equals zero exactly when causal dependence between X and Y is stable and is strictly positive otherwise, with a near-linear estimator and convergence rates provided.
Journal of the American statistical Association , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
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An attack aligns differently shuffled intermediate activations from secure Transformer inference queries to recover model weights with low error using roughly one dollar of queries.
citing papers explorer
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Detecting Changes in Causal Dependence with Kernels and Copulas
A kernel-copula embedding statistic equals zero exactly when causal dependence between X and Y is stable and is strictly positive otherwise, with a near-linear estimator and convergence rates provided.
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On the (In-)Security of the Shuffling Defense in the Transformer Secure Inference
An attack aligns differently shuffled intermediate activations from secure Transformer inference queries to recover model weights with low error using roughly one dollar of queries.