A new rectified and renormalized Fisher-Bingham model is proposed for compositional data containing zeros, achieved via square-root mapping to the sphere and a deterministic transformation on a latent Fisher-Bingham variable.
Asymptotic statistics, volume 3
5 Pith papers cite this work. Polarity classification is still indexing.
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CMRM adds a conformal quantile regularization on prediction margins to any loss, improving noisy-label classification accuracy up to 3.39% across methods and benchmarks while preserving performance at zero noise.
A two-stage causal estimator for semi-continuous exposures that disentangles exposure status and dose via a two-part propensity score in a marginal structural model.
Active inference adapts label collection via ML uncertainty to deliver valid statistical inference with substantially fewer samples than standard non-adaptive methods across any data distribution.
Constant stepsize SA with decision-dependent Markovian noise has stationary bias O(alpha) under Poisson-Gateaux differentiability, plus finite-time moment bounds and weak convergence.
citing papers explorer
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Rectified Fisher-Bingham Model for Compositional Data with Zeros
A new rectified and renormalized Fisher-Bingham model is proposed for compositional data containing zeros, achieved via square-root mapping to the sphere and a deterministic transformation on a latent Fisher-Bingham variable.
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Conformal Margin Risk Minimization: An Envelope Framework for Robust Learning under Label Noise
CMRM adds a conformal quantile regularization on prediction margins to any loss, improving noisy-label classification accuracy up to 3.39% across methods and benchmarks while preserving performance at zero noise.
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Two-stage Estimation for Causal Inference Involving a Semi-continuous Exposure
A two-stage causal estimator for semi-continuous exposures that disentangles exposure status and dose via a two-part propensity score in a marginal structural model.
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Active Statistical Inference
Active inference adapts label collection via ML uncertainty to deliver valid statistical inference with substantially fewer samples than standard non-adaptive methods across any data distribution.
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Revisiting the Constant Stepsize Stochastic Approximation with Decision-Dependent Markovian Noise
Constant stepsize SA with decision-dependent Markovian noise has stationary bias O(alpha) under Poisson-Gateaux differentiability, plus finite-time moment bounds and weak convergence.