ELCP integrates auxiliary data with a density-ratio-weighted kernel to enhance localized conformal prediction sets, maintaining marginal coverage and improving asymptotic local coverage.
Happymap: A generalized multi-calibration method
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The thesis presents a kernel method for multiaccuracy across overlooked subpopulations, information-theoretic optimal watermarking for LLMs, and a simulator showing LLM agents outperforming humans in supply chains while creating tail risks.
A new classifier derived from determinism and statistical consistency requirements achieves zero consistency error by construction and outperforms baselines on Adult, COMPAS, and Bank Marketing datasets.
Unifies MCBoost variants and proves convergence to Bregman projection with rates, finite-sample bounds, and covariate-shift transferability.
citing papers explorer
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Enhanced localized conformal prediction with imperfect auxiliary information
ELCP integrates auxiliary data with a density-ratio-weighted kernel to enhance localized conformal prediction sets, maintaining marginal coverage and improving asymptotic local coverage.
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Trustworthy AI: Ensuring Reliability and Accountability from Models to Agents
The thesis presents a kernel method for multiaccuracy across overlooked subpopulations, information-theoretic optimal watermarking for LLMs, and a simulator showing LLM agents outperforming humans in supply chains while creating tail risks.
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Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions
A new classifier derived from determinism and statistical consistency requirements achieves zero consistency error by construction and outperforms baselines on Adult, COMPAS, and Bank Marketing datasets.
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Multicalibration Boosting: Theory, Convergence, and Transferability
Unifies MCBoost variants and proves convergence to Bregman projection with rates, finite-sample bounds, and covariate-shift transferability.