4 papers
Improving Model Safety by Targeted Error Correction
Abolfazl Mohammadi-Seif, Ricardo Baeza-Yates
The widespread adoption of machine learning in critical applications demands techniques to mitigate high-consequence errors. Our method utilizes a dual-classifier GBDT pipeline to…
Risk-Calibrated Learning: Minimizing Fatal Errors in Medical AI
Abolfazl Mohammadi-Seif, Ricardo Baeza-Yates
Deep learning models often achieve expert-level accuracy in medical image classification but suffer from a critical flaw: semantic incoherence. These high-confidence mistakes that…
Face Density as a Proxy for Data Complexity: Quantifying the Hardness of Instance Count
Abolfazl Mohammadi-Seif, Ricardo Baeza-Yates
Machine learning progress has historically prioritized model-centric innovations, yet achievable performance is frequently capped by the intrinsic complexity of the data itself. In…
Beyond the Mean: Distribution-Aware Loss Functions for Bimodal Regression
Abolfazl Mohammadi-Seif, Carlos Soares, Rita P. Ribeiro +1
Despite the strong predictive performance achieved by machine learning models across many application domains, assessing their trustworthiness through reliable estimates of predict…