Research graph
References from Reliable food safety risk assessment via calibrated Mondrian conformal prediction: a machine learning framework with uncertainty quantification. Local targets link to admitted publications; unresolved targets remain external evidence.
10.1561/2200000101 · 2023 · Admitted local publication
Uncertainty sets for image classifiers using conformal prediction
2021 · External reference
The limits of distribution-free conditional predictive inference
10.1093/imaiai/iaaa017 · 2021 · External reference
Mining food safety data: text mining for risk identification in RASFF notifications
2021 · External reference
Impact of drivers of change, including climatic factors, on the occurrence of chemical food safety hazards in fruits and vegetables: A Bayesian network approach"
10.1016/j.foodcont.2018.10.021 · 2019 · External reference
Internet of things in food safety: literature review and a bibliometric analysis
10.1016/j.tifs.2019.11.002 · 2018 · External reference
SMOTE: synthetic minority over-sampling technique
10.1613/jair.953 · 2002 · External reference
XGBoost: a scalable tree boosting system
10.1145/2939672.2939785 · 2016 · External reference
The application of conformal prediction to the drug discovery process
10.1007/s10472-013-9378-2 · 2015 · External reference
SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary
10.1613/jair.1.11192 · 2018 · External reference
On calibration of modern neural networks
2017 · External reference
Learning from imbalanced data
10.1109/tkde.2008.239 · 2009 · External reference
A survey of algorithmic recourse: contrastive explanations and consequential recommendations
10.1145/3527848 · 2022 · External reference
Verified uncertainty calibration
2019 · External reference
Comparison-based inverse classification for interpretability in machine learning
2018 · External reference
Safe planning in dynamic environments using conformal prediction
10.1109/lra.2023.3292071 · 2023 · External reference
Fair conformal predictors for applications in medical imaging
10.1609/aaai.v36i11.21459 · 2022 · External reference
Big data in food safety: an overview
10.1080/10408398.2016.1257481 · 2016 · External reference
Explaining machine learning classifiers through diverse counterfactual explanations
10.1145/3351095.3372850 · 2020 · External reference
Calibrating deep neural networks using focal loss
2020 · External reference
A comparison of neural and non-neural machine learning models for food safety risk prediction with European Union RASFF data
2005 · External reference
A deep learning approach for food safety inspection using computer vision
2022 · External reference
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
1999 · External reference
FACE: feasible and actionable counterfactual explanations
10.1145/3375627.3375850 · 2020 · External reference
Conformalized quantile regression
2019 · External reference
With malice toward none: assessing uncertainty via equalized coverage
10.1162/99608f92.03f00592 · 2020 · External reference
A tutorial on conformal prediction
2008 · External reference
Learning optimal conformal classifiers
2022 · External reference
Interpretable counterfactual explanations guided by prototypes
2021 · External reference
Conditional validity of inductive conformal predictors
10.1007/s10994-013-5355-6 · 2003 · External reference
Unresolved reference
2005 · External reference
Counterfactual explanations without opening the black box: automated decisions and the GDPR
10.2139/ssrn.3063289 · 2018 · External reference
Class probability estimates are unreliable for imbalanced data (and how to fix them)
2012 · External reference
On calibrating semantic segmentation models: analyses and an algorithm
2023 · External reference
Unresolved reference
2015 · External reference
Transforming classifier scores into accurate multiclass probability estimates
External reference
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
External reference
