Theoretical Guarantees for One-Shot Magnitude Pruning and Compute-Adaptive Early Exit

arXiv:2609.12337 · cs.LG · Submitted 2026-09-11 · Read on arXiv

cs.LG

Submitted: 2026-09-11

Updated: 2026-09-11

Comments: Published in ICML 2026

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Pruning — 4× in this paper · explained in 4 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Generalization — 1× in this paper · explained in 13 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks