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Discovering Sparse Recovery Algorithms Using Neural Architecture Search

arXiv 2512.21563 ↗First posted 25 Dec 2025cs.LGcited by 0

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Abstract (arXiv)

The design of novel algorithms for solving inverse problems in signal processing is an incredibly difficult, heuristic-driven, and time-consuming task. In this short paper, we the idea of automated algorithm discovery in the signal processing context through meta-learning tools such as Neural Architecture Search (NAS). Specifically, we examine the Iterative Shrinkage Thresholding Algorithm (ISTA) and its accelerated Fast ISTA (FISTA) variant as candidates for algorithm rediscovery. We develop a meta-learning framework which is capable of rediscovering (several key elements of) the two aforementioned algorithms when given a search space of over 50,000 variables. We then show how our framework can apply to various data distributions and algorithms besides ISTA/FISTA.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 18 references that are in the map. First posted on arXiv 25 Dec 2025.

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References · 12

  1. 2018DARTS: Differentiable Architecture Searchcited by 1,782
  2. 2016Neural Architecture Search with Reinforcement Learningcited by 1,489
  3. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,097
  4. 2018Regularized Evolution for Image Classifier Architecture Searchcited by 902
  5. 2021Learning to Optimize: A Primer and A Benchmarkcited by 206
  6. 2018Neural Architecture Search with Bayesian Optimisation and Optimal Transportcited by 192
  7. 2014Phase Retrieval via Wirtinger Flow: Theory and Algorithmscited by 123
  8. 2018Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholdscited by 83
  9. 2019Alternating Phase Projected Gradient Descent with Generative Priors for Solving Compressive Phase Retrievalcited by 18
  10. 2019Learnable Embedding Space for Efficient Neural Architecture Compressioncited by 10
  11. 2019One-Shot Neural Architecture Search via Compressive Sensingcited by 4
  12. 2019Reducing The Search Space For Hyperparameter Optimization Using Group Sparsitycited by 2

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