Code for the control+shift paper, where a method for generating general datasets with a controllable amount of distribution shift was proposed. Presented at the ECCV'24 "Synthetic Data for Computer Vision" workshop.

Coping with Label Shift via Distributionally Robust Optimisation

Robust and Highly Sensitive Covariate Shift Detection using XGBoost

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

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

Official implementation of the Fréchet Radiomics Distance | pip install frd-score

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

A Python 3 package for identifying distribution shifts (a.k.a feature-shifts) between datasets. Official implementation of the paper: "iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models".

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

Code accompanying our paper titled Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms

A systematic approach to class distribution mismatch in semi-supervised learning using deep dataset dissimilarity measures

Temporally and Distributionally Robust Optimization for Cold-start Recommendation (AAAI'24)

Implementation codes for NeurIPS23 paper "Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts"

Global-Local Regularization Via Distributional Robustness (AISTATS 2023)

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

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