Conditional Invariant

Conditional invariance in machine learning focuses on learning representations that are robust to variations in data distributions while preserving relevant information for a given task. Current research emphasizes developing algorithms and model architectures that identify and leverage conditionally invariant components, often using techniques like conditional independence regularizers or discrepancy minimization between conditional distributions across domains. This work addresses challenges in domain adaptation, fair machine learning, and causal inference by mitigating the impact of spurious correlations and unwanted variability, leading to more reliable and generalizable models across diverse datasets and applications. The resulting improvements in model robustness and fairness have significant implications for various fields, including computer vision, bioinformatics, and healthcare.