:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Kumar, Tanishq, Luo, Kevin, Sellke, Mark
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2402.01089
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

On Size-Independent Sample Complexity of ReLU Networks
by: Sellke, Mark
Published: (2023)

An Effective Information Theoretic Framework for Channel Pruning
by: Chen, Yihao, et al.
Published: (2024)

Pruning at Initialization -- A Sketching Perspective
by: Bar, Noga, et al.
Published: (2023)

Inference-Time Chain-of-Thought Pruning with Latent Informativeness Signals
by: Li, Sophie, et al.
Published: (2025)

Incentivizing Exploration with Linear Contexts and Combinatorial Actions
by: Sellke, Mark
Published: (2023)

SwiftPrune: Hessian-Free Weight Pruning for Large Language Models
by: Kang, Yuhan, et al.
Published: (2025)

Theoretical and Empirical Advances in Forest Pruning
by: Dorador, Albert
Published: (2024)

What Scalable Second-Order Information Knows for Pruning at Initialization
by: Navarrete, Ivo Gollini, et al.
Published: (2025)

On Learning-Curve Monotonicity for Maximum Likelihood Estimators
by: Sellke, Mark, et al.
Published: (2025)

Nonparametric MLE for Gaussian Location Mixtures: Certified Computation and Generic Behavior
by: Polyanskiy, Yury, et al.
Published: (2025)

Rapid Deployment of DNNs for Edge Computing via Structured Pruning at Initialization
by: Eccles, Bailey J., et al.
Published: (2024)

Geometry Meets Incentives: Sample-Efficient Incentivized Exploration with Linear Contexts
by: Schiffer, Benjamin, et al.
Published: (2025)

To Bag is to Prune
by: Coulombe, Philippe Goulet
Published: (2020)

Mutual Information Preserving Neural Network Pruning
by: Westphal, Charles, et al.
Published: (2024)

AIMER: Calibration-Free Task-Agnostic MoE Pruning
by: Liu, Zongfang, et al.
Published: (2026)

Training-Free Dataset Pruning for Instance Segmentation
by: Dai, Yalun, et al.
Published: (2025)

Prune, Update and Trim: Robust Structured Pruning for Large Language Models
by: Mecke, Diego Coello de Portugal, et al.
Published: (2026)

Data-Free Pruning of Self-Attention Layers in LLMs
by: Saikumar, Dhananjay, et al.
Published: (2025)

Non-transferable Pruning
by: Ding, Ruyi, et al.
Published: (2024)

Sparsest Models Elude Pruning: An Exposé of Pruning's Current Capabilities
by: Zhang, Stephen, et al.
Published: (2024)

STUN: Structured-Then-Unstructured Pruning for Scalable MoE Pruning
by: Lee, Jaeseong, et al.
Published: (2024)

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs
by: Fu, Yao, et al.
Published: (2025)

Theoretically Grounded Pruning of Large Ground Sets for Constrained, Discrete Optimization
by: Nath, Ankur, et al.
Published: (2024)

FedPaI: Achieving Extreme Sparsity in Federated Learning via Pruning at Initialization
by: Wang, Haonan, et al.
Published: (2025)

Training-Free Restoration of Pruned Neural Networks
by: Lee, Keonho, et al.
Published: (2025)

Speculative Speculative Decoding
by: Kumar, Tanishq, et al.
Published: (2026)

LLM-Rank: A Graph Theoretical Approach to Pruning Large Language Models
by: Hoffmann, David, et al.
Published: (2024)

Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes
by: Kolawole, Steven, et al.
Published: (2024)

QAdaPrune: Adaptive Parameter Pruning For Training Variational Quantum Circuits
by: Kulshrestha, Ankit, et al.
Published: (2024)

Pruning Unrolled Networks (PUN) at Initialization for MRI Reconstruction Improves Generalization
by: Liang, Shijun, et al.
Published: (2024)

Quantization vs Pruning: Insights from the Strong Lottery Ticket Hypothesis
by: Kumar, Aakash, et al.
Published: (2025)

Towards Sobolev Pruning
by: Kichler, Neil, et al.
Published: (2023)

Holistic Adversarially Robust Pruning
by: Zhao, Qi, et al.
Published: (2024)

Lightweight Edge Learning via Dataset Pruning
by: Ale, Laha, et al.
Published: (2026)

Towards Efficient VLMs: Information-Theoretic Driven Compression via Adaptive Structural Pruning
by: Xu, Zhaoqi, et al.
Published: (2025)

PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs
by: Yu, Tongzhou, et al.
Published: (2025)

IPPRO: Importance-based Pruning with PRojective Offset for Magnitude-indifferent Structural Pruning
by: Jung, Jaeheun, et al.
Published: (2025)

TopoPrune: Robust Data Pruning via Unified Latent Space Topology
by: Roy, Arjun, et al.
Published: (2026)

Mosaic Pruning: A Hierarchical Framework for Generalizable Pruning of Mixture-of-Experts Models
by: Hu, Wentao, et al.
Published: (2025)

Pruning and Malicious Injection: A Retraining-Free Backdoor Attack on Transformer Models
by: Zhao, Taibiao, et al.
Published: (2025)