Cover Image

A Theory for Compressibility of Graph Transformers for Transductive Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Shirzad, Hamed, Lin, Honghao, Velingker, Ameya, Venkatachalam, Balaji, Woodruff, David, Sutherland, Danica
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Even Sparser Graph Transformers
by: Shirzad, Hamed, et al.
Published: (2024)

SeedER: Seed-and-Expand Retrieval from Knowledge Graphs
by: Shirzad, Hamed, et al.
Published: (2026)

Locality-Aware Graph-Rewiring in GNNs
by: Barbero, Federico, et al.
Published: (2023)

Optimal Sketching for Residual Error Estimation for Matrix and Vector Norms
by: Li, Yi, et al.
Published: (2024)

Weisfeiler-Leman at the margin: When more expressivity matters
by: Franks, Billy J., et al.
Published: (2024)

HyperGraphX: Graph Transductive Learning with Hyperdimensional Computing and Message Passing
by: Cong, Guojing, et al.
Published: (2025)

Learning the Positions in CountSketch
by: Li, Yi, et al.
Published: (2023)

Understanding Simplicity Bias towards Compositional Mappings via Learning Dynamics
by: Ren, Yi, et al.
Published: (2024)

Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling
by: Bae, Wonho, et al.
Published: (2023)

Learning Representations for Independence Testing
by: Xu, Nathaniel, et al.
Published: (2024)

Learning Dynamics of LLM Finetuning
by: Ren, Yi, et al.
Published: (2024)

Generalized Coverage for More Robust Low-Budget Active Learning
by: Bae, Wonho, et al.
Published: (2024)

Transductive Active Learning: Theory and Applications
by: Hübotter, Jonas, et al.
Published: (2024)

Uncertainty Herding: One Active Learning Method for All Label Budgets
by: Bae, Wonho, et al.
Published: (2024)

In Transformer We Trust? A Perspective on Transformer Architecture Failure Modes
by: Mondal, Trishit, et al.
Published: (2026)

Efficient kernelized bandit algorithms via exploration distributions
by: Hu, Bingshan, et al.
Published: (2025)

Transductive Reward Inference on Graph
by: Qu, Bohao, et al.
Published: (2024)

An Approximation Theory Perspective on Machine Learning
by: Mhaskar, Hrushikesh N., et al.
Published: (2025)

Conformal Load Prediction with Transductive Graph Autoencoders
by: Luo, Rui, et al.
Published: (2024)

Multiclass Transductive Online Learning
by: Hanneke, Steve, et al.
Published: (2024)

The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models
by: Stein, Adam, et al.
Published: (2025)

Universal Multiclass Transductive Online Learning
by: Hanneke, Steve, et al.
Published: (2026)

Why Do You Grok? A Theoretical Analysis of Grokking Modular Addition
by: Mohamadi, Mohamad Amin, et al.
Published: (2024)

Efficient Attention via Pre-Scoring: Prioritizing Informative Keys in Transformers
by: Li, Zhexiang, et al.
Published: (2025)

Integrating AI and Ensemble Forecasting: Explainable Materials Planning with Scorecards and Trend Insights for a Large-Scale Manufacturer
by: Venkatachalam, Saravanan
Published: (2025)

Maximum Mean Discrepancy with Unequal Sample Sizes via Generalized U-Statistics
by: Wei, Aaron, et al.
Published: (2025)

Graph Transductive Sharpening: Leveraging Unlabeled Predictions in Node Classification
by: Zaz, Brown, et al.
Published: (2026)

Shocks Under Control: Taming Transonic Compressible Flow over an RAE2822 Airfoil with Deep Reinforcement Learning
by: Mondal, Trishit, et al.
Published: (2025)

Conformal Prediction: A Theoretical Note and Benchmarking Transductive Node Classification in Graphs
by: Maneriker, Pranav, et al.
Published: (2024)

Improved Generalization Bounds for Transductive Learning by Transductive Local Complexity and Its Applications
by: Yang, Yingzhen
Published: (2023)

Optimal Mistake Bounds for Transductive Online Learning
by: Chase, Zachary, et al.
Published: (2025)

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs
by: Lell, Nicolas, et al.
Published: (2025)

Transductive Generalization via Optimal Transport and Its Application to Graph Node Classification
by: Park, MoonJeong, et al.
Published: (2026)

Practical Kernel Tests of Conditional Independence
by: Pogodin, Roman, et al.
Published: (2024)

Transductive Spiking Graph Neural Networks for Loihi
by: Snyder, Shay, et al.
Published: (2024)

Transductive and Learning-Augmented Online Regression
by: Raman, Vinod, et al.
Published: (2025)

Bias Amplification in Language Model Evolution: An Iterated Learning Perspective
by: Ren, Yi, et al.
Published: (2024)

DUAL: Learning Diverse Kernels for Aggregated Two-sample and Independence Testing
by: Zhou, Zhijian, et al.
Published: (2025)

Deep Transductive Outlier Detection
by: Klüttermann, Simon, et al.
Published: (2024)

Local Regularizers Are Not Transductive Learners
by: Jafar, Sky, et al.
Published: (2025)