StCP leverages transfer learning to stabilize the size of conformal prediction sets without additional target labels.
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3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
ParamBoost improves GAMs by fitting piecewise cubic polynomials via gradient boosting and supports constraints for continuity, monotonicity, convexity, and feature interactions.
E-value sequential tests enable early stopping of MCMC sampling in Bayesian deep ensembles, often needing only a fraction of the full budget while improving over standard deep ensembles.
citing papers explorer
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Stable Localized Conformal Prediction via Transduction
StCP leverages transfer learning to stabilize the size of conformal prediction sets without additional target labels.
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ParamBoost: Gradient Boosted Piecewise Cubic Polynomials
ParamBoost improves GAMs by fitting piecewise cubic polynomials via gradient boosting and supports constraints for continuity, monotonicity, convexity, and feature interactions.
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Towards E-Value Based Stopping Rules for Bayesian Deep Ensembles
E-value sequential tests enable early stopping of MCMC sampling in Bayesian deep ensembles, often needing only a fraction of the full budget while improving over standard deep ensembles.