Trimmed L-moments and L-comoments for robust statistics.

Preliminary code for the paper "Learning Deterministic Surrogates for Robust Convex QCQPs".

Data-driven decision making under uncertainty using matrices

Оптимизация долгосрочного портфеля акций

Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning

Robust optimization module

Code and data for the article "Reliable frequency regulation through vehicle-to-grid: Encoding legislation with robust constraints" by Dirk Lauinger, François Vuille, and Daniel Kuhn available at https://pubsonline.informs.org/doi/10.1287/msom.2022.0154 and https://arxiv.org/pdf/2005.06042v4.pdf. This project was funded by the Institut VEDECOM.

Robust estimations from distribution structures: Mean.

Robust estimations from distribution structures: Central moments.

Robust estimations from distribution structures: Invariant moments.

Code accompanying the paper "Heuristic Methods for Mixed-Integer, Linear, and Gamma-Robust Bilevel Problems" (with Ivana Ljubic and Martin Schmidt)

Code and data for the article "Frequency Regulation with Storage: On Losses and Profits" by Dirk Lauinger, François Vuille, and Daniel Kuhn, available at https://doi.org/10.1016/j.ejor.2024.03.022 and https://arxiv.org/pdf/2306.02987v2.pdf. This project was funded by the Institut VEDECOM.

A development environment for robust and global optimization

Enhanced Portfolio Optimization (EPO)

code for the paper Beyond Neural scaling laws for fast proven robust certification of nearest prototype classifiers

Simple, yet effective, data-driven algorithm for optimization under parametric uncertainty

Pytorch Implementation of Robust Convolutional LSTM Encoder–decoder (RCLED)

Propagation of distributions by Monte-Carlo sampling: Real number types with uncertainty represented by samples.

Experiments code for AAMAS'24 paper on "Decentralized Federated Policy Gradient with Byzantine Fault-Tolerance and Provably Fast Convergence"

This project provides algorithms for solving robust binary optimization problems with budgeted uncertainty in the objective function.