Exact LTI Koopman models for nonlinear control systems require affine linear dynamics under controllability and coordinate inclusion assumptions.
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5 Pith papers cite this work, alongside 190 external citations. Polarity classification is still indexing.
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A new evidence sufficiency model with four dimensions and seven proxy categories enables monitoring of ML risk systems under delayed ground truth, detecting covariate and mixed drift but not concept drift without feature changes.
DeGRe decouples offline exploration via a lookahead evaluator using beam search and cumulative regression to distill dense supervision into an online generator that approximates optimal reranking sequences with greedy decoding.
CM-DCM jointly models direct and delayed conversions in pre-promotion e-commerce via multi-task learning, personalized gating, and counterfactual transition probabilities from add-to-cart, outperforming baselines with gains in ad revenue and GMV in A/B tests.
Fairness-induced exploration in recommenders exhibits diminishing or non-monotonic returns that vary by user interaction history, with low-history users saturating sooner.
citing papers explorer
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Limitations of LTI Koopman Modeling for Nonlinear Control Systems
Exact LTI Koopman models for nonlinear control systems require affine linear dynamics under controllability and coordinate inclusion assumptions.
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Evidence Sufficiency Under Delayed Ground Truth: Proxy Monitoring for Risk Decision Systems
A new evidence sufficiency model with four dimensions and seven proxy categories enables monitoring of ML risk systems under delayed ground truth, detecting covariate and mixed drift but not concept drift without feature changes.
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DeGRe: Dense-supervised Generative Reranking for Recommendation
DeGRe decouples offline exploration via a lookahead evaluator using beam search and cumulative regression to distill dense supervision into an online generator that approximates optimal reranking sequences with greedy decoding.
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Counterfactual Multi-task Learning for Delayed Conversion Modeling in E-commerce Sales Pre-Promotion
CM-DCM jointly models direct and delayed conversions in pre-promotion e-commerce via multi-task learning, personalized gating, and counterfactual transition probabilities from add-to-cart, outperforming baselines with gains in ad revenue and GMV in A/B tests.
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Modeling User Exploration Saturation: When Recommender Systems Should Stop Pushing Novelty
Fairness-induced exploration in recommenders exhibits diminishing or non-monotonic returns that vary by user interaction history, with low-history users saturating sooner.