A package of distributionally robust optimization (DRO) methods. Implemented via cvxpy and PyTorch

[NeurIPS2023] Official code of "Understanding Contrastive Learning via Distributionally Robust Optimization"

The Pytorch implementation for "Topology-aware Robust Optimization for Out-of-Distribution Generalization" (ICLR 2023)

"Aligning Distributionally Robust Optimization with Practical Deep Learning Needs"

Distributionally robust machine learning with Pytorch and Scikit-learn wrappers

[ICLR 2025] Official code of "Towards Robust Alignment of Language Models: Distributionally Robustifying Direct Preference Optimization"

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

[ICDE2024] Official code of "BSL: Understanding and Improving Softmax Loss for Recommendation"

Python Implementation of the Instance-wise Distributionally Robust Nonnegative Matrix Factorization (iDRNMF)

This is the official repository for the ICLR 2024 paper Out-Of-Domain Unlabeled Data Improves Generalization.

Python Implementation of DRNMF-SP

Code for the experiments in the paper "Contextual Robust Optimisation with Uncertainty Quantification".

An open-source Python module for portfolio optimization and backtesting

Code for the project DiGriFlex

Code for the Paper "Robust Offline Reinforcement Learning with Linearly Structured f-Divergence Regularization", International Conference on Machine Learning (ICML) 2025

End-to-end Python framework for robust pension fund management under parameter uncertainty. Implements three Distributionally Robust (DRO) Asset-Liability Management (ALM) formulations (Mixture, Box, Wasserstein) with GBM scenario generation, convex optimization (LP/SOCP), and comprehensive backtesting. Based on 2026 research by Ghahtarani et al.

this repository hosts my master's thesis codes and dataset.

Distributionally robust Cox regression with Wasserstein ambiguity sets. Includes classical baselines, evaluation metrics, and reproducible experiments.

End-to-End Python implementation of Liu & Cheng's (2026) methodology for U.S. Treasury yield curve forecasting. Combines Factor-Augmented Dynamic Nelson-Siegel models, High-Dimensional Random Forests, and Distributionally Robust Optimization (DRO) for risk-aware ensemble forecasting under ambiguity.

📈 Forecast U.S. Treasury yield curves with a robust machine learning approach, enhancing accuracy and decision-making in finance.