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Mahalanobis-Aware Training for Out-of-Distribution Detection
While deep learning models have seen widespread success in controlled environments, there are still barriers to their adoption in open-world settings.
Fourier-Based Augmentations for Improved Robustness and Uncertainty Calibration
Diverse data augmentation strategies are a natural approach to improving robustness in computer vision models against unforeseen shifts in data distribution.
Tools and Practices for Responsible AI Engineering
Responsible Artificial Intelligence (AI) - the practice of developing, evaluating, and maintaining accurate AI systems that also exhibit essential properties such as robustness and explainability - represents a multifaceted challenge that often stretches standard machine learning tooling, frameworks, and testing methods beyond their limits.
Improving Learning-to-Defer Algorithms Through Fine-Tuning
We work to improve learning-to-defer algorithms when paired with specific individuals by incorporating two fine-tuning algorithms and testing their efficacy using both synthetic and image datasets.
