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Lottery Tickets in Linear Models: An Analysis of Iterative Magnitude Pruning

arXiv 2007.08243 ↗First posted 16 Jul 2020cs.LG stat.MLcited by 23

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Abstract (arXiv)

We analyse the pruning procedure behind the lottery ticket hypothesis arXiv:1803.03635v5, iterative magnitude pruning (IMP), when applied to linear models trained by gradient flow. We begin by presenting sufficient conditions on the statistical structure of the features under which IMP prunes those features that have smallest projection onto the data. Following this, we explore IMP as a method for sparse estimation.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Jul 2020.

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Citing papers per year: 2021: 8; 2022: 4; 2023: 8; 2024: 1; 2025: 0; 2026: 2.

23 papers.

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  1. 2023LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuningcited by 53
  2. 2021Heavy Tails in SGD and Compressibility of Overparametrized Neural Networkscited by 44
  3. 2022The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networkscited by 41
  4. 2021GANs Can Play Lottery Tickets Toocited by 32
  5. 2021An Operator Theoretic View on Pruning Deep Neural Networkscited by 18
  6. 2021A Probabilistic Approach to Neural Network Pruningcited by 15
  7. 2021Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Pruned Neural Networkscited by 14
  8. 2022Dimensionality Reduced Training by Pruning and Freezing Parts of a Deep Neural Network, a Surveycited by 12
  9. 2021SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruningcited by 9
  10. 2022Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithmcited by 9
  11. 2021Universality of Winning Tickets: A Renormalization Group Perspectivecited by 8
  12. 2023Getting Away with More Network Pruning: From Sparsity to Geometry and Linear Regionscited by 8
11 more
  1. 2023My3DGen: A Scalable Personalized 3D Generative Modelcited by 8
  2. 2023On the Opportunities of Green Computing: A Surveycited by 7
  3. 2024MoreauPruner: Robust Pruning of Large Language Models against Weight Perturbationscited by 5
  4. 2021Probabilistic fine-tuning of pruning masks and PAC-Bayes self-bounded learningcited by 2
  5. 2022Zeroth-Order Topological Insights into Iterative Magnitude Pruningcited by 2
  6. 2023Iterative Magnitude Pruning as a Renormalisation Group: A Study in The Context of The Lottery Ticket Hypothesiscited by 1
  7. 2023Learning to Learn with Indispensable Connectionscited by 0
  8. 2023Transferability of Winning Lottery Tickets in Neural Network Differential Equation Solverscited by 0
  9. 2023Non-Convex Optimizations for Machine Learning with Theoretical Guarantee: Robust Matrix Completion and Neural Network Learningcited by 0
  10. 2026RecycleLoRA: Rank-Revealing QR-Based Dual-LoRA Subspace Adaptation for Domain Generalized Semantic Segmentationcited by 0
  11. 2026Man, Machine, and Mathematicscited by 0