An efficient black-box reduction from PQ to TDS learning for any Boolean concept class in the distribution-free setting implies hardness for TDS learning of halfspaces, while membership queries enable efficient PQ learning of halfspaces via iterative Forster transforms.
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The multinomial minimax risk for uniformity testing against $l_p$ alternatives converges exactly to $2Phi(-u^*/2)$ in the intermediate regime, proven via a conditional central limit theorem.
Stochastic integer optimization has sample complexity that matches, undercuts, or exceeds the continuous case based on objective structure, with new tight bounds for nonconvex continuous problems.
In Arrow-Debreu economies with multiplex network externalities, competitive markets satisfy the First and Second Welfare Theorems under regularity or identical layer structures; Lindahl equilibria correct remaining inefficiencies via personalized prices.
Generalized Rank Regression extends rank methods to non-monotonic scores, derives Bahadur representation and asymptotic normality, proposes a two-stage sub-gradient algorithm, and shows variance equivalence to composite quantile regression.
citing papers explorer
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Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift
An efficient black-box reduction from PQ to TDS learning for any Boolean concept class in the distribution-free setting implies hardness for TDS learning of halfspaces, while membership queries enable efficient PQ learning of halfspaces via iterative Forster transforms.
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Sharp Lower Bound on the Minimax Risk for Multinomial Uniformity Testing via a Conditional Central Limit Theorem
The multinomial minimax risk for uniformity testing against $l_p$ alternatives converges exactly to $2Phi(-u^*/2)$ in the intermediate regime, proven via a conditional central limit theorem.
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Sample Complexity of Stochastic Optimization with Integer Variables
Stochastic integer optimization has sample complexity that matches, undercuts, or exceeds the continuous case based on objective structure, with new tight bounds for nonconvex continuous problems.
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When Do Markets Work? Multiplex Networks and Efficiency
In Arrow-Debreu economies with multiplex network externalities, competitive markets satisfy the First and Second Welfare Theorems under regularity or identical layer structures; Lindahl equilibria correct remaining inefficiencies via personalized prices.
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Generalized Rank Regression
Generalized Rank Regression extends rank methods to non-monotonic scores, derives Bahadur representation and asymptotic normality, proposes a two-stage sub-gradient algorithm, and shows variance equivalence to composite quantile regression.
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