The Ticino road construction cartel mimicked competitive bidding using cost-based allocation, evading econometric detection and generating at least 45% overcharges.
and Guyon, Isabelle M
11 Pith papers cite this work. Polarity classification is still indexing.
11
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
LG-CoTrain, an LLM-guided co-training method, outperforms classical semi-supervised baselines for crisis tweet classification in low-resource settings with 5-25 labeled examples per class.
TabKDE generates synthetic tabular data using copula transformations followed by kernel density estimation, matching prior accuracy with negligible training time and reduced storage via coresets.
Meta-learning with 24 classical complexity metrics predicts the optimal quantum encoding circuit among 9 candidates with up to 85.7% top-3 accuracy.
MLS is a new large-scale multilingual speech corpus derived from LibriVox with 44.5k hours of English and 6k hours across seven other languages, plus baseline ASR and LM models.
Per-class regularization hyperparameters in Gabriel graph classifiers create flexible thresholds that eliminate outliers and address class imbalance, improving performance per Friedman test.
A new framework grades levels of inference capability in data-driven systems to assess compliance with the EU AI Act definition of AI, illustrated via credit scoring workflows.
Kernel ridge regression combined with mRMR feature selection improves prediction of full benchmark scores from question subsets over existing efficient benchmarking techniques.
NLP-derived attributes from construction incident reports remain strongly predictive of independently labeled safety outcomes even after removing potential label leakage, with injury severity now well predicted on a dataset of more than 90,000 reports.
Standard NLP classifiers can surface valid injury precursors from raw construction safety reports.
Established mathematical bottlenecks in representation, optimization, complexity, and high-dimensional learning aligned with the central disappointments of early AI research periods.
citing papers explorer
-
AI-based Prediction of Independent Construction Safety Outcomes from Universal Attributes
NLP-derived attributes from construction incident reports remain strongly predictive of independently labeled safety outcomes even after removing potential label leakage, with injury severity now well predicted on a dataset of more than 90,000 reports.
-
Automatically Learning Construction Injury Precursors from Text
Standard NLP classifiers can surface valid injury precursors from raw construction safety reports.