Proves oracle Bernstein-von Mises theorem for fractional posterior under supportwise likelihood assumptions in sparse GLMs with spike-and-slab priors.
The benefit of group sparsity.The Annals of Statistics, 38(4):1978 – 2004
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Overprovisioned KANs with sparsification, deep supervision, and depth selection under differentiable MDL yield smaller models with competitive accuracy on benchmarks.
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Bernstein-von Mises Theorem for Sparse Generalized Linear Model
Proves oracle Bernstein-von Mises theorem for fractional posterior under supportwise likelihood assumptions in sparse GLMs with spike-and-slab priors.
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Optimized Architectures for Kolmogorov-Arnold Networks
Overprovisioned KANs with sparsification, deep supervision, and depth selection under differentiable MDL yield smaller models with competitive accuracy on benchmarks.