Unifies MCBoost variants and proves convergence to Bregman projection with rates, finite-sample bounds, and covariate-shift transferability.
M., & Roth, A
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A spatio-temporal GCN with bi-level chaotic fusion and volatility-aware gating produces stock price prediction intervals on NSE data, reporting superior Winkler score, PIAW, and PICP over LSTM, GRU, GCN, and HGNN baselines.
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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.
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Bi-Level Chaotic Fusion Based Graph Convolutional Network for Stock Market Prediction Interval
A spatio-temporal GCN with bi-level chaotic fusion and volatility-aware gating produces stock price prediction intervals on NSE data, reporting superior Winkler score, PIAW, and PICP over LSTM, GRU, GCN, and HGNN baselines.
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