Robust Deep Learning Under Distribution Shift

Robust Deep Learning Under Distribution Shift

Explore the challenges and solutions in deep learning under distribution shift in this 50-minute lecture by Zack Lipton from Carnegie Mellon University. Delve into topics such as adversarial misspellings, feedback loops, and label shift detection. Learn about black box shift estimation and correction techniques, and examine their applications in pneumonia prediction and image classification. Investigate the impact of implicit bias in SGD, weight-invariance, and regularization methods on deep learning models. Gain insights into domain adaptation strategies and synthetic experiments that address distribution shift problems in real-world scenarios.