isl-org/MultiObjectiveOptimization

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1070 stars · 174 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Source code for the NeurIPS 2018 paper 'Multi-Task Learning as Multi-Objective Optimization', implementing the MGDA_UB algorithm for multi-task learning. It includes PyTorch and pure NumPy implementations of Frank-Wolfe and projected gradient descent solvers, plus the MultiMNIST dataset.

Use cases

• implement multi-task learning as multi-objective optimization
• balance gradients across multiple loss functions in deep learning
• reproduce the MGDA_UB algorithm from the NeurIPS 2018 paper
• train models on the MultiMNIST dataset
• use Frank-Wolfe or projected gradient descent for Pareto optimization
• port gradient surgery techniques to other deep learning frameworks

When to choose

• you need the reference implementation of MGDA_UB for research or citation
• you want a framework-agnostic NumPy multi-objective gradient solver
• you are studying multi-task learning optimization methods

When to avoid

• you need maintained software with bug fixes or updates - Intel has discontinued the project
• you need production-ready multi-task learning tooling with active community support
• you require support for recent PyTorch versions without your own patches

Facets

library · maturity abandoned

machine-learning deep-learning machine-learning deep-learning python cross-platform multi-task-learning multi-objective-optimization mgda gradient-descent pytorch neurips-2018 research-code numpy optimization research

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