Performance Analysis
Keypoint-Augmented Self-Supervised Learning for Medical Image Segmentation with Limited Annotation
Pretraining CNN models (i.e., UNet) through self-supervision has become a powerful approach to facilitate medical image segmentation under low annotation regimes.
An Efficient Doubly-Robust Test for the Kernel Treatment Effect
Such assumptions allow for identification of causal target parameters. For instance, one may be interested in the average variance-weighted treatment effects (Robins et al., 2008; Li et al., 2011),
Adapting Fairness Interventions to Missing Values
Missing values in real-world data pose a significant and unique challenge to algorithmic fairness.
L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inference
However, evaluating whether or not these approximations can be trusted remains a challenge. Most approaches evaluate the posterior estimator only in expectation over the observation space.
Addressing sub-optimal adversaries in differentially private machine learning
MI can lead to the disclosure of highly sensitive information about the individual, e.g. that
Y ouTubePD: A Multimodal Benchmark for Parkinson's Disease Analysis Andy Zhou 1 Samuel Li
The healthcare and AI communities have witnessed a growing interest in the development of AI-assisted systems for automated diagnosis of Parkinson's Disease
