Full Professor, Georgia Institute of Technology
2 papers at NeurIPS 2025
We propose a general fine-tuning approach to address the performance drops on the imbalanced text-to-image generation tasks.
We propose a kernel-based equalized statistic to quantify the accuracy-fairness trade-off among independence-, separation-, and calibration-based constraints, identifying the best suited criterion to preserve predictive accuracy.
