# Data-Independent Structured Pruning of Neural Networks via Coresets

arXiv: https://arxiv.org/abs/2008.08316 · HTML: https://mlsanity.com/paper/2008.08316/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2020.

## Built on by (most cited first, 17 of 17)

- [A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations](https://mlsanity.com/paper/2308.06767/) (2023)
- [Gradient-Free Structured Pruning with Unlabeled Data](https://mlsanity.com/paper/2303.04185/) (2023)
- [Data-Efficient Structured Pruning via Submodular Optimization](https://mlsanity.com/paper/2203.04940/) (2022)
- [Pruning Neural Networks via Coresets and Convex Geometry: Towards No Assumptions](https://mlsanity.com/paper/2209.08554/) (2022)
- [A Unified Approach to Coreset Learning](https://mlsanity.com/paper/2111.03044/) (2021)
- [Neural Pruning for 3D Scene Reconstruction: Efficient NeRF Acceleration](https://mlsanity.com/paper/2504.00950/) (2025)
- [Performance-aware Approximation of Global Channel Pruning for Multitask CNNs](https://mlsanity.com/paper/2303.11923/) (2023)
- [Dataset Distillation Meets Provable Subset Selection](https://mlsanity.com/paper/2307.08086/) (2023)
- [Coresets for Data Discretization and Sine Wave Fitting](https://mlsanity.com/paper/2203.03009/) (2022)
- [Obstacle Aware Sampling for Path Planning](https://mlsanity.com/paper/2203.04075/) (2022)
- [Data-Efficient Training of CNNs and Transformers with Coresets: A Stability Perspective](https://mlsanity.com/paper/2303.02095/) (2023)
- [Efficient Test-Time Finetuning of LLMs via Convex Reconstruction and Gradient Caching](https://mlsanity.com/paper/2605.30337/) (2026)
- [FaceCoresetNet: Differentiable Coresets for Face Set Recognition](https://mlsanity.com/paper/2308.14075/) (2023)
- [Neural Network Reduction with Guided Regularizers](https://mlsanity.com/paper/2305.18448/) (2023)
- [Computing Approximate ℓp Sensitivities](https://mlsanity.com/paper/2311.04158/) (2023)
- [A Greedy Hierarchical Approach to Whole-Network Filter-Pruning in CNNs](https://mlsanity.com/paper/2409.03777/) (2024)
- [Active Learning with Low-Rank Structure for Data Selection](https://mlsanity.com/paper/2606.16045/) (2026)

## Abstract (arXiv)

Model compression is crucial for deployment of neural networks on devices with limited computational and memory resources. Many different methods show comparable accuracy of the compressed model and similar compression rates. However, the majority of the compression methods are based on heuristics and offer no worst-case guarantees on the trade-off between the compression rate and the approximation error for an arbitrarily new sample. We propose the first efficient structured pruning algorithm with a provable trade-off between its compression rate and the approximation error for any future test sample. Our method is based on the coreset framework and it approximates the output of a layer of neurons/filters by a coreset of neurons/filters in the previous layer and discards the rest. We apply this framework in a layer-by-layer fashion from the bottom to the top. Unlike previous works, our coreset is data independent, meaning that it provably guarantees the accuracy of the function for any input x∈ ℝd, including an adversarial one.