Code for "Improving Stain Invariance of CNNs for Segmentation by Fusing Channel Attention and Domain-Adversarial Training"

Advanced domain adaptation techniques for robust machine learning across different data distributions

Resources for the paper titled "Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts". Accepted at NeurIPS 2022.

Implementation of paper: Equivariant Learning for Out-of-Distribution Cold-start Recommendation. (backbone model CLCRec) (MM'23)

Coping with Label Shift via Distributionally Robust Optimisation

Gated Domain Units (GDU) aim to make your deep learning models robust against distribution shifts when applied in the real-world.

CLIFT : Analysing Natural Distribution Shift on Question Answering Models in Clinical Domain

"RDA: Reciprocal Distribution Alignment for Robust Semi-supervised Learning" by Yue Duan (ECCV 2022)

Official implementation of NeurIPS 2025 paper "Visual Instruction Bottleneck Tuning"

Code for "LLM Embeddings Improve Test-time Adaptation to Tabular Y|X-Shifts"

Code for the paper "Where are we with calibration under dataset shift in image classification?"

Robust and Highly Sensitive Covariate Shift Detection using XGBoost

Code for the Conditional Mutual Information-Debiasing (CMID) method.

Code for "Adapting Large Multimodal Models to Distribution Shifts: The Role of In-Context Learning"

Training Distribution Selection for Provable OOD Performance

Quilt: Robust Data Segment Selection against Concept Drifts (AAAI 2024)