Imagen de Portada

MOYU: A Theoretical Study on Massive Over-activation Yielded Uplifts in LLMs

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Ma, Chi, Huang, Mincong, Wang, Chao, Wang, Yujie, Yu, Lei
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Dynamic Activation Pitfalls in LLaMA Models: An Empirical Study
por: Ma, Chi, et al.
Publicado: (2024)

First Activations Matter: Training-Free Methods for Dynamic Activation in Large Language Models
por: Ma, Chi, et al.
Publicado: (2024)

Re-evaluating the Memory-balanced Pipeline Parallelism: BPipe
por: Huang, Mincong, et al.
Publicado: (2024)

QQQ: Quality Quattuor-Bit Quantization for Large Language Models
por: Zhang, Ying, et al.
Publicado: (2024)

Graph Neural Network with Two Uplift Estimators for Label-Scarcity Individual Uplift Modeling
por: Zhu, Dingyuan, et al.
Publicado: (2024)

Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization
por: Sun, Zexu, et al.
Publicado: (2023)

UTBoost: Gradient Boosted Decision Trees for Uplift Modeling
por: Gao, Junjie, et al.
Publicado: (2023)

A Comparative Study of Model Adaptation Strategies for Multi-Treatment Uplift Modeling
por: Zhang, Ruyue, et al.
Publicado: (2025)

Orthogonal Uplift Learning with Permutation-Invariant Representations for Combinatorial Treatments
por: Su, Xinyan, et al.
Publicado: (2026)

Rankability-enhanced Revenue Uplift Modeling Framework for Online Marketing
por: He, Bowei, et al.
Publicado: (2024)

Less is More: on the Over-Globalizing Problem in Graph Transformers
por: Xing, Yujie, et al.
Publicado: (2024)

Benchmarking for Deep Uplift Modeling in Online Marketing
por: Liu, Dugang, et al.
Publicado: (2024)

Hierarchical Contextual Uplift Bandits for Catalog Personalization
por: Agrawal, Anupam, et al.
Publicado: (2026)

Boosting Graph Robustness Against Backdoor Attacks: An Over-Similarity Perspective
por: Liu, Chang, et al.
Publicado: (2025)

Evaluating Uplift Modeling under Structural Biases: Insights into Metric Stability and Model Robustness
por: Yang, Yuxuan, et al.
Publicado: (2026)

Uplift modeling with continuous treatments: A predict-then-optimize approach
por: De Vos, Simon, et al.
Publicado: (2024)

A New Transformation Approach for Uplift Modeling with Binary Outcome
por: Li, Kun, et al.
Publicado: (2023)

FairUDT: Fairness-aware Uplift Decision Trees
por: Zahid, Anam, et al.
Publicado: (2025)

A Theoretical Analysis of Self-Supervised Learning for Vision Transformers
por: Huang, Yu, et al.
Publicado: (2024)

A Theoretical Analysis of Noise Geometry in Stochastic Gradient Descent
por: Wang, Mingze, et al.
Publicado: (2023)

Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent Marketing
por: Huang, Yinqiu, et al.
Publicado: (2024)

Can Slow-thinking LLMs Reason Over Time? Empirical Studies in Time Series Forecasting
por: Cheng, Mingyue, et al.
Publicado: (2025)

Uplift Modeling Under Limited Supervision
por: Panagopoulos, George, et al.
Publicado: (2024)

Theoretical Analysis of Inductive Biases in Deep Convolutional Networks
por: Wang, Zihao, et al.
Publicado: (2023)

Which Company Adjustment Matter? Insights from Uplift Modeling on Financial Health
por: Wang, Xinlin, et al.
Publicado: (2025)

Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective
por: Zhao, Lei, et al.
Publicado: (2023)

Guardrailed Uplift Targeting: A Causal Optimization Playbook for Marketing Strategy
por: Sapru, Deepit
Publicado: (2025)

A Theoretical Study of Neural Network Expressive Power via Manifold Topology
por: Yao, Jiachen, et al.
Publicado: (2024)

Learning to Think: Information-Theoretic Reinforcement Fine-Tuning for LLMs
por: Wang, Jingyao, et al.
Publicado: (2025)

Polybasic Speculative Decoding Through a Theoretical Perspective
por: Wang, Ruilin, et al.
Publicado: (2025)

Robust Uplift Modeling with Large-Scale Contexts for Real-time Marketing
por: Sun, Zexu, et al.
Publicado: (2025)

We Have It Covered: A Resampling-based Method for Uplift Model Comparison
por: Liu, Yang, et al.
Publicado: (2025)

Jointly Optimizing Debiased CTR and Uplift for Coupons Marketing: A Unified Causal Framework
por: Yang, Siyun, et al.
Publicado: (2026)

On the Convergence Analysis of Over-Parameterized Variational Autoencoders: A Neural Tangent Kernel Perspective
por: Wang, Li, et al.
Publicado: (2024)

Direct Profit Estimation Using Uplift Modeling under Clustered Network Interference
por: Akker, Bram van den
Publicado: (2025)

ORGEval: Graph-Theoretic Evaluation of LLMs in Optimization Modeling
por: Wang, Zhuohan, et al.
Publicado: (2025)

Secrets of GFlowNets' Learning Behavior: A Theoretical Study
por: Yu, Tianshu
Publicado: (2025)

Activation Compression in LLMs: Theoretical Analysis and Efficient Algorithm
por: Wei, Wen-Da, et al.
Publicado: (2026)

TSC: A Simple Two-Sided Constraint against Over-Smoothing
por: Peng, Furong, et al.
Publicado: (2024)

Dataset Watermarking for Closed LLMs with Provable Detection
por: Huang, Pengrun, et al.
Publicado: (2026)