Neural tangent kernel from neural reparameterization modulates sensitivity and wave tangent kernels to produce spectral filtering, wavenumber modulation, and frequency bias that improve NeurFWI convergence.
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The Sinkhorn treatment effect is a new entropic optimal transport measure of divergence between counterfactual distributions that admits first- and second-order pathwise differentiability, debiased estimators, and asymptotically valid tests for distributional treatment effects.
AOI approximately inverts the likelihood mapping from fixed effects to outcomes to produce an estimator whose bias vanishes exponentially in T with double robustness.
The authors propose an S-MILP framework that optimizes group sequential testing boundaries to achieve faster rejection of the null hypothesis compared to traditional methods while controlling type I and type II errors.
The authors create e-processes for monotonicity and unimodality testing that achieve power one and consistent mode estimation under i.i.d. sampling.
A replica exchange MCMC algorithm couples constrained and relaxed chains to sample from disconnected implicit manifolds defined by nonlinear constraints.
citing papers explorer
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Deciphering Neural Reparameterized Full-Waveform Inversion with Neural Sensitivity Kernel and Wave Tangent Kernel
Neural tangent kernel from neural reparameterization modulates sensitivity and wave tangent kernels to produce spectral filtering, wavenumber modulation, and frequency bias that improve NeurFWI convergence.
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Sinkhorn Treatment Effects: A Causal Optimal Transport Measure
The Sinkhorn treatment effect is a new entropic optimal transport measure of divergence between counterfactual distributions that admits first- and second-order pathwise differentiability, debiased estimators, and asymptotically valid tests for distributional treatment effects.
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Approximate Operator Inversion for Average Effects in Nonlinear Panel Models
AOI approximately inverts the likelihood mapping from fixed effects to outcomes to produce an estimator whose bias vanishes exponentially in T with double robustness.
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A General Framework for Optimal Group Sequential Testing via Mixed-Integer Linear Programming
The authors propose an S-MILP framework that optimizes group sequential testing boundaries to achieve faster rejection of the null hypothesis compared to traditional methods while controlling type I and type II errors.
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E-values and sequential power-one tests for monotonicity and unimodality
The authors create e-processes for monotonicity and unimodality testing that achieve power one and consistent mode estimation under i.i.d. sampling.
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A Replica Exchange Markov Chain Monte Carlo Method for Disconnected Implicit Manifolds via Tubular Relaxation
A replica exchange MCMC algorithm couples constrained and relaxed chains to sample from disconnected implicit manifolds defined by nonlinear constraints.
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