:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Franssen, Christian, Jiang, Jinyang, Peng, Yijie, Heidergott, Bernd
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2502.00744
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Stochastic Approximation Methods for Distortion Risk Measure Optimization
by: Jiang, Jinyang, et al.
Published: (2025)

A Practical Guide to Interpretable Role-Based Clustering in Multi-Layer Financial Networks
by: Franssen, Christian, et al.
Published: (2025)

Feature-Based Network Construction: From Sampling to What-if Analysis
by: Franssen, Christian, et al.
Published: (2024)

A First-Order Gradient Approach for the Connectivity Optimization of Markov Chains
by: Franssen, Christian P. C., et al.
Published: (2024)

Two-Stage Regularization-Based Structured Pruning for LLMs
by: Feng, Mingkuan, et al.
Published: (2025)

Closing the Loop: Coordinating Inventory and Recommendation via Deep Reinforcement Learning on Multiple Timescales
by: Jiang, Jinyang, et al.
Published: (2025)

Forward Learning with Differential Privacy
by: Feng, Mingqian, et al.
Published: (2025)

Omni-Masked Gradient Descent: Memory-Efficient Optimization via Mask Traversal with Improved Convergence
by: Yang, Hui, et al.
Published: (2026)

Approximated Likelihood Ratio: A Forward-Only and Parallel Framework for Boosting Neural Network Training
by: Zhang, Zeliang, et al.
Published: (2024)

Optimizing Federated Graph Learning with Inherent Structural Knowledge and Dual-Densely Connected GNNs
by: Wang, Longwen, et al.
Published: (2024)

FLOPS: Forward Learning with OPtimal Sampling
by: Ren, Tao, et al.
Published: (2024)

Deep Reinforcement Learning for Solving Management Problems: Towards A Large Management Mode
by: Jiang, Jinyang, et al.
Published: (2024)

BMRS: Bayesian Model Reduction for Structured Pruning
by: Wright, Dustin, et al.
Published: (2024)

Catalyst: a Novel Regularizer for Structured Pruning with Auxiliary Extension of Parameter Space
by: Jung, Jaeheun, et al.
Published: (2025)

A New Stochastic Approximation Method for Gradient-based Simulated Parameter Estimation
by: Li, Zehao, et al.
Published: (2025)

On the Dynamic Regret of Following the Regularized Leader: Optimism with History Pruning
by: Mhaisen, Naram, et al.
Published: (2025)

Dual-Agent Deep Reinforcement Learning for Dynamic Pricing and Replenishment
by: Zheng, Yi, et al.
Published: (2024)

DReSS: Data-driven Regularized Structured Streamlining for Large Language Models
by: Feng, Mingkuan, et al.
Published: (2025)

Sample-Efficient "Clustering and Conquer" Procedures for Parallel Large-Scale Ranking and Selection
by: Zhang, Zishi, et al.
Published: (2024)

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning
by: Xie, Hui, et al.
Published: (2025)

AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models
by: Lu, Haiquan, et al.
Published: (2024)

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

Sparse Additive Model Pruning for Order-Based Causal Structure Learning
by: Kanamori, Kentaro, et al.
Published: (2026)

Synaptic Pruning: A Biological Inspiration for Deep Learning Regularization
by: Vos, Gideon, et al.
Published: (2025)

Stochastic Subnetwork Annealing: A Regularization Technique for Fine Tuning Pruned Subnetworks
by: Whitaker, Tim, et al.
Published: (2024)

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

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

Smoothing the Edges: Smooth Optimization for Sparse Regularization using Hadamard Overparametrization
by: Kolb, Chris, et al.
Published: (2023)

Nonparametric Bayesian Optimization for General Rewards
by: Zhang, Zishi, et al.
Published: (2026)

Weight Concentration Regularization for Improving Pruning Robustness Under High Sparsity
by: Yun, Vincent-Daniel, et al.
Published: (2025)

Pruning Deep Neural Networks via a Combination of the Marchenko-Pastur Distribution and Regularization
by: Berlyand, Leonid, et al.
Published: (2025)

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

Progressive Binarization with Semi-Structured Pruning for LLMs
by: Yan, Xianglong, et al.
Published: (2025)

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

A New Branch-and-Bound Pruning Framework for $\ell_0$-Regularized Problems
by: Guyard, Theo, et al.
Published: (2024)

On How Iterative Magnitude Pruning Discovers Local Receptive Fields in Fully Connected Neural Networks
by: Redman, William T., et al.
Published: (2024)

On Cold Posteriors of Probabilistic Neural Networks: Understanding the Cold Posterior Effect and A New Way to Learn Cold Posteriors with Tight Generalization Guarantees
by: Zhang, Yijie
Published: (2024)

High-Layer Attention Pruning with Rescaling
by: Liu, Songtao, et al.
Published: (2025)

Connectivity Shapes Implicit Regularization in Matrix Factorization Models for Matrix Completion
by: Bai, Zhiwei, et al.
Published: (2024)

BWTA: Accurate and Efficient Binarized Transformer by Algorithm-Hardware Co-design
by: Ding, Yifu, et al.
Published: (2026)