pruning

Accelerating Streaming Video Large Language Models via Hierarchical Token Compression CVPR 2026 An Efficient Pruner for Large Language Model with Theoretical Guarantee ICML 2025 Antidote: Post-fine-tuning Safety Alignment for Large Language Models against Harmful Fine-tuning Attack ICML 2025 Balancing Model Efficiency and Performance: Adaptive Pruner for Long-tailed Data ICML 2025 Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression ICML 2025 CLOVER: Cross-Layer Orthogonal Vectors Pruning ICML 2025 Compressing tree ensembles through Level-wise Optimization and Pruning ICML 2025 Distilling the Knowledge in Data Pruning ICML 2025 DLP: Dynamic Layerwise Pruning in Large Language Models ICML 2025 Exploring Representations and Interventions in Time Series Foundation Models ICML 2025 Federated Oriented Learning: A Practical One-Shot Personalized Federated Learning Framework ICML 2025 Improving the Scaling Laws of Synthetic Data with Deliberate Practice ICML 2025 Instruction-Following Pruning for Large Language Models ICML 2025 Let LLM Tell What to Prune and How Much to Prune ICML 2025 PROXSPARSE: REGULARIZED LEARNING OF SEMI-STRUCTURED SPARSITY MASKS FOR PRETRAINED LLMS ICML 2025 Pruning for GNNs: Lower Complexity with Comparable Expressiveness ICML 2025 SAFE: Finding Sparse and Flat Minima to Improve Pruning ICML 2025 Targeted Low-rank Refinement: Enhancing Sparse Language Models with Precision ICML 2025 APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference ICML 2024 Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications ICML 2024 COPAL: Continual Pruning in Large Language Generative Models ICML 2024 Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression ICML 2024 Exploring Intrinsic Dimension for Vision-Language Model Pruning ICML 2024 How Do Nonlinear Transformers Learn and Generalize in In-Context Learning? ICML 2024 In value-based deep reinforcement learning, a pruned network is a good network ICML 2024