Proves an impossibility theorem that no feature attribution ranking can be faithful, stable, and complete under collinearity, characterizes the design space as two families, introduces the DASH ensemble method, and formally verifies all claims in Lean 4.
Advances in Neural Information Processing Systems , volume=
3 Pith papers cite this work. Polarity classification is still indexing.
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LLM filtering of embedding-based stock networks raises long-short Sharpe ratio from 0.742 to 0.820 and cuts max drawdown from -10.47% to -7.85% in 2011-2019 S&P 500 backtests.
Data equity, prediction equity, and decision equity are distinct statistical requirements that need separate evaluations to address how racial biases in pulse oximetry measurements lead to treatment disparities.
citing papers explorer
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The Attribution Impossibility: No Feature Ranking Is Faithful, Stable, and Complete Under Collinearity
Proves an impossibility theorem that no feature attribution ranking can be faithful, stable, and complete under collinearity, characterizes the design space as two families, introduces the DASH ensemble method, and formally verifies all claims in Lean 4.
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Cross-Stock Predictability via LLM-Augmented Semantic Networks
LLM filtering of embedding-based stock networks raises long-short Sharpe ratio from 0.742 to 0.820 and cuts max drawdown from -10.47% to -7.85% in 2011-2019 S&P 500 backtests.
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Data (in)equities in data science: Dissecting systemic and systematic biases in pulse oximetry
Data equity, prediction equity, and decision equity are distinct statistical requirements that need separate evaluations to address how racial biases in pulse oximetry measurements lead to treatment disparities.