Scalable Meta-Learning via Mixed-Mode Differentiation

• URL: http://arxiv.org/abs/2505.00793v2
• Date: Mon, 09 Jun 2025 20:37:58 GMT
• Title: Scalable Meta-Learning via Mixed-Mode Differentiation
• Authors: Iurii Kemaev, Dan A Calian, Luisa M Zintgraf, Gregory Farquhar, Hado van Hasselt,
• Abstract summary: Mixed-Flow Meta-Gradients is a practical algorithm that uses mixed-mode differentiation to construct more efficient and scalable computational graphs.<n>It yields over 10x memory and up to 25% wall-clock time improvements over standard implementations in modern meta-learning setups.
• Score: 20.117663282362944
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Gradient-based bilevel optimisation is a powerful technique with applications in hyperparameter optimisation, task adaptation, algorithm discovery, meta-learning more broadly, and beyond. It often requires differentiating through the gradient-based optimisation itself, leading to "gradient-of-a-gradient" calculations with computationally expensive second-order and mixed derivatives. While modern automatic differentiation libraries provide a convenient way to write programs for calculating these derivatives, they oftentimes cannot fully exploit the specific structure of these problems out-of-the-box, leading to suboptimal performance. In this paper, we analyse such cases and propose Mixed-Flow Meta-Gradients, or MixFlow-MG -- a practical algorithm that uses mixed-mode differentiation to construct more efficient and scalable computational graphs yielding over 10x memory and up to 25% wall-clock time improvements over standard implementations in modern meta-learning setups.

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