% Key papers for Layer-wise pruning of LLMs (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2020_train_38877,
  title = {{Train Big, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6828},
}

@inproceedings{icml2023_sparsegpt_42717,
  title = {{SparseGPT: Massive Language Models Can be Accurately Pruned in One-Shot}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24014},
}

@inproceedings{aaai2023_a_3029,
  title = {{A Survey on Model Compression and Acceleration for Pretrained Language Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26255},
}

@inproceedings{neurips2023_model_66568,
  title = {{Model Sparsity Can Simplify Machine Unlearning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/73048},
}

@inproceedings{aaai2022_from_2129,
  title = {{From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model Compression}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/11547-from-dense-to-sparse-contrastive-pruning-for-better-pre-trained-language-model-compression/},
}

@inproceedings{cvpr2024_resource_24624,
  title = {{Resource-Efficient Transformer Pruning for Finetuning of Large Models}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31465},
}

@inproceedings{neurips2024_slimgpt_71820,
  title = {{SlimGPT: Layer-wise Structured Pruning for Large Language Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95477},
}

@inproceedings{neurips2024_discovering_74114,
  title = {{Discovering Sparsity Allocation for Layer-wise Pruning of Large Language Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93422},
}

@inproceedings{aaai2026_sepprune_108103,
  title = {{SepPrune: Structured Pruning for Efficient Deep Speech Separation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/40455},
}

@inproceedings{aaai2025_pruning_10399,
  title = {{Pruning Large Language Models with Semi-Structural Adaptive Sparse Training}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34592},
}

@inproceedings{neurips2023_dynamic_69820,
  title = {{Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/70129},
}

@inproceedings{iclr2024_slicegpt_35542,
  title = {{SliceGPT: Compress Large Language Models by Deleting Rows and Columns}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17531},
}

@inproceedings{icml2023_losparse_42300,
  title = {{LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/23722},
}

@inproceedings{neurips2023_ziplm_68807,
  title = {{ZipLM: Inference-Aware Structured Pruning of Language Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71044},
}

@inproceedings{iclr2025_the_83416,
  title = {{The Unreasonable Ineffectiveness of the Deeper Layers}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28400},
}

@inproceedings{iclr2024_besa_34921,
  title = {{BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18153},
}

@inproceedings{neurips2024_alps_70172,
  title = {{ALPS: Improved Optimization for Highly Sparse One-Shot Pruning for Large Language Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96911},
}

@inproceedings{neurips2024_building_72567,
  title = {{Building on Efficient Foundations: Effective Training of LLMs with Structured Feedforward Layers}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94805},
}

@inproceedings{neurips2025_mustafar_75795,
  title = {{MUSTAFAR: Promoting Unstructured Sparsity for KV Cache Pruning in LLM Inference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=C69741fMFX},
}

@inproceedings{aaai2026_d2_107580,
  title = {{D2 Prune: Sparsifying Large Language Models via Dual Taylor Expansion and Attention Distribution Awareness}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39932},
}

@inproceedings{iclr2024_the_33935,
  title = {{The LLM Surgeon}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19140},
}

@inproceedings{iclr2024_accurate_35406,
  title = {{Accurate Retraining-free Pruning for Pretrained Encoder-based Language Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17667},
}

@inproceedings{icml2024_lorap_45292,
  title = {{LoRAP: Transformer Sub-Layers Deserve Differentiated Structured Compression for Large Language Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33160},
}

@inproceedings{icml2025_la_45915,
  title = {{La RoSA: Enhancing LLM Efficiency via Layerwise Rotated Sparse Activation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46630},
}

@inproceedings{icml2025_slimllm_45987,
  title = {{SlimLLM: Accurate Structured Pruning for Large Language Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46559},
}

@inproceedings{icml2025_pivoting_46127,
  title = {{Pivoting Factorization: A Compact Meta Low-Rank Representation of Sparsity for Efficient Inference in Large Language Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46433},
}

@inproceedings{icml2025_backslash_47042,
  title = {{BackSlash: Rate Constrained Optimized Training of Large Language Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45543},
}

@inproceedings{icml2025_cabs_47755,
  title = {{CABS: Conflict-Aware and Balanced Sparsification for Enhancing Model Merging}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44845},
}