A package of distributionally robust optimization (DRO) methods. Implemented via cvxpy and PyTorch
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May 31, 2025 - Jupyter Notebook
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distributionally-robust-optimization
Here are 20 public repositories matching this topic...
[NeurIPS2023] Official code of "Understanding Contrastive Learning via Distributionally Robust Optimization"
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Oct 18, 2023 - Python
The Pytorch implementation for "Topology-aware Robust Optimization for Out-of-Distribution Generalization" (ICLR 2023)
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Apr 3, 2025 - Python
"Aligning Distributionally Robust Optimization with Practical Deep Learning Needs"
optimization optimization-methods dro distributionally-robust-optimization optimization-for-deep-learning
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Oct 3, 2025 - Python
Distributionally robust machine learning with Pytorch and Scikit-learn wrappers
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Dec 19, 2025 - Python
[ICLR 2025] Official code of "Towards Robust Alignment of Language Models: Distributionally Robustifying Direct Preference Optimization"
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Jun 1, 2024 - Python
Temporally and Distributionally Robust Optimization for Cold-start Recommendation (AAAI'24)
pytorch recommender-system cold-start distribution-shift distributionally-robust-optimization aaai2024
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Mar 28, 2024 - Python
[ICDE2024] Official code of "BSL: Understanding and Improving Softmax Loss for Recommendation"
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Dec 27, 2023 - Python
Python Implementation of the Instance-wise Distributionally Robust Nonnegative Matrix Factorization (iDRNMF)
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Nov 9, 2025 - Python
This is the official repository for the ICLR 2024 paper Out-Of-Domain Unlabeled Data Improves Generalization.
semi-supervised-learning robust-optimization unsupervised-learning conference-paper iclr robustness adversarial-learning self-supervised-learning generalization-error out-of-distribution out-of-domain distribution-shift out-of-distribution-generalization distributionally-robust-optimization iclr2024
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Jan 22, 2025 - TeX
Python Implementation of DRNMF-SP
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Nov 17, 2025 - Python
Code for the experiments in the paper "Contextual Robust Optimisation with Uncertainty Quantification".
machine-learning robust-optimization uncertainty-quantification stochastic-optimization distributionally-robust-optimization
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Apr 14, 2023 - Python
An open-source Python module for portfolio optimization and backtesting
finance portfolio-optimization quantitative-finance heuristics online-learning utility-theory markowitz risk-measures distributionally-robust-optimization
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Jan 28, 2026 - Python
Code for the project DiGriFlex
optimization markov-chain forecasting power-systems bayesian-bootstrapping distributionally-robust-optimization
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Feb 14, 2024 - Python
Code for the Paper "Robust Offline Reinforcement Learning with Linearly Structured f-Divergence Regularization", International Conference on Machine Learning (ICML) 2025
offline-reinforcement-learning distributionally-robust-optimization off-dynamics-rl off-dynamics-reinforcement-learning
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Jul 17, 2025 - Jupyter Notebook
this repository hosts my master's thesis codes and dataset.
deep-reinforcement-learning portfolio-optimization cvxpy end-to-end-learning implicit-optimization distributionally-robust-optimization
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Oct 19, 2023 - Python
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.
python linear-programming jupyter-notebook scientific-computing monte-carlo-simulation portfolio-optimization robust-optimization quantitative-finance operations-research cvxpy convex-optimization risk-management financial-engineering geometric-brownian-motion stochastic-programming pension-funds scenario-generation second-order-cone-programming distributionally-robust-optimization asset-liability-management
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Feb 21, 2026 - Jupyter Notebook
Distributionally robust Cox regression with Wasserstein ambiguity sets. Includes classical baselines, evaluation metrics, and reproducible experiments.
python machine-learning survival-analysis cox-regression cvxpy convex-optimization censored-data robust-statistics wasserstein-distance distributionally-robust-optimization
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Mar 5, 2026 - Python
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.
python machine-learning random-forest reproducible-research econometrics scipy ensemble-learning quantitative-finance principal-component-analysis fixed-income risk-management time-series-analysis financial-modeling nelson-siegel-model shap-values forecast-combination treasury-bonds distributionally-robust-optimization structural-breaks yield-curve-forecasting
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Jan 11, 2026 - Jupyter Notebook
📈 Forecast U.S. Treasury yield curves with a robust machine learning approach, enhancing accuracy and decision-making in finance.
python machine-learning random-forest econometrics scipy principal-component-analysis fixed-income term-structure-models financial-modeling nelson-siegel-model vasicek-model forecast-combination short-rates heath-jarrow-morton treasury-bonds distributionally-robust-optimization structural-breaks
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Mar 8, 2026 - Assembly
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