Trimmed L-moments and L-comoments for robust statistics.
-
Updated
Jul 6, 2024 - Python
Trimmed L-moments and L-comoments for robust statistics.
Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning
The cMIBACO implementation for lightly robust solutions in MOGenConVRP under uncertainty.
Оптимизация долгосрочного портфеля акций
Code accompanying the paper "Heuristic Methods for Mixed-Integer, Linear, and Gamma-Robust Bilevel Problems" (with Ivana Ljubic and Martin Schmidt)
Simple, yet effective, data-driven algorithm for optimization under parametric uncertainty
Pytorch Implementation of Robust Convolutional LSTM Encoder–decoder (RCLED)
Experiments code for AAMAS'24 paper on "Decentralized Federated Policy Gradient with Byzantine Fault-Tolerance and Provably Fast Convergence"
Python library that implements Robust Portfolio Optimization with ellipsoid uncertainty sets.
[NeurIPS 2023] "Combating Bilateral Edge Noise for Robust Link Prediction"
[NeurIPS 2023] Combating Bilateral Edge Noise for Robust Link Prediction
End-to-end distributionally robust optimization
Code for the experiments in the paper "Contextual Robust Optimisation with Uncertainty Quantification".
Package of robust GPR inversion using Huber norm and source separation
Implementation of Robust Imitation Learning against Variations in Environment Dynamics
Python Library for Robustness Monitoring and Adversarial Debugging of NLP models
This repo contains code and visualisation for "Robust moving target defence against false data injection attacks in power grids"
Coping with Label Shift via Distributionally Robust Optimisation
Modeling robust optimization problems in Pyomo
Geometric median (GM) is a classical method in statistics for achieving a robust estimation of the uncorrupted data; under gross corruption, it achieves the optimal breakdown point of 0.5. However, its computational complexity makes it infeasible for robustifying stochastic gradient descent (SGD) for high-dimensional optimization problems. In th…
Add a description, image, and links to the robust-optimization topic page so that developers can more easily learn about it.
To associate your repository with the robust-optimization topic, visit your repo's landing page and select "manage topics."