Exact Generalisation Error Exposes Benchmarks Skew Graph Neural Networks Success (or Failure)
Fuente:
arXiv
Saved in:
| Main Authors: | Ayday, Nil, Sabanayagam, Mahalakshmi, Ghoshdastidar, Debarghya |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Different Statistical Perspectives for Understanding Generalisation in Graph Neural Networks
by: Ayday, Nil, et al.
Published: (2026)
by: Ayday, Nil, et al.
Published: (2026)
Exact Certification of (Graph) Neural Networks Against Label Poisoning
by: Sabanayagam, Mahalakshmi, et al.
Published: (2024)
by: Sabanayagam, Mahalakshmi, et al.
Published: (2024)
Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks
by: Gosch, Lukas, et al.
Published: (2024)
by: Gosch, Lukas, et al.
Published: (2024)
Exact Certification of Neural Networks and Partition Aggregation Ensembles against Label Poisoning
by: Mohgaonkar, Ajinkya, et al.
Published: (2026)
by: Mohgaonkar, Ajinkya, et al.
Published: (2026)
Robustness Certificates for Neural Networks against Adversarial Attacks
by: Taheri, Sara, et al.
Published: (2025)
by: Taheri, Sara, et al.
Published: (2025)
Gaussian Process Limit Reveals Structural Benefits of Graph Transformers
by: Ayday, Nil, et al.
Published: (2026)
by: Ayday, Nil, et al.
Published: (2026)
Robust Feature Inference: A Test-time Defense Strategy using Spectral Projections
by: Singh, Anurag, et al.
Published: (2023)
by: Singh, Anurag, et al.
Published: (2023)
Generalization Certificates for Adversarially Robust Bayesian Linear Regression
by: Sabanayagam, Mahalakshmi, et al.
Published: (2025)
by: Sabanayagam, Mahalakshmi, et al.
Published: (2025)
Non-Singularity of the Gradient Descent map for Neural Networks with Piecewise Analytic Activations
by: Crăciun, Alexandru, et al.
Published: (2025)
by: Crăciun, Alexandru, et al.
Published: (2025)
Infinite Width Limits of Self Supervised Neural Networks
by: Fleissner, Maximilian, et al.
Published: (2024)
by: Fleissner, Maximilian, et al.
Published: (2024)
Transformers Provably Learn Sparse XOR with Polylogarithmic Parameters
by: Han, Yaomengxi, et al.
Published: (2025)
by: Han, Yaomengxi, et al.
Published: (2025)
On the Convergence of Gradient Descent for Large Learning Rates
by: Crăciun, Alexandru, et al.
Published: (2024)
by: Crăciun, Alexandru, et al.
Published: (2024)
Tight PAC-Bayesian Risk Certificates for Contrastive Learning
by: Van Elst, Anna, et al.
Published: (2024)
by: Van Elst, Anna, et al.
Published: (2024)
When can we Approximate Wide Contrastive Models with Neural Tangent Kernels and Principal Component Analysis?
by: Anil, Gautham Govind, et al.
Published: (2024)
by: Anil, Gautham Govind, et al.
Published: (2024)
Cluster Specific Representation Learning
by: Sabanayagam, Mahalakshmi, et al.
Published: (2024)
by: Sabanayagam, Mahalakshmi, et al.
Published: (2024)
Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders
by: Ham, Jonghyun, et al.
Published: (2025)
by: Ham, Jonghyun, et al.
Published: (2025)
Theoretical Foundations of Representation Learning using Unlabeled Data: Statistics and Optimization
by: Esser, Pascal, et al.
Published: (2025)
by: Esser, Pascal, et al.
Published: (2025)
A Probabilistic Model for Non-Contrastive Learning
by: Fleissner, Maximilian, et al.
Published: (2025)
by: Fleissner, Maximilian, et al.
Published: (2025)
Recovering Imbalanced Clusters via Gradient-Based Projection Pursuit
by: Eppert, Martin, et al.
Published: (2025)
by: Eppert, Martin, et al.
Published: (2025)
Explainable Clustering Beyond Worst-Case Guarantees
by: Fleissner, Maximilian, et al.
Published: (2024)
by: Fleissner, Maximilian, et al.
Published: (2024)
A Theoretical Characterization of Optimal Data Augmentations in Self-Supervised Learning
by: Feigin, Shlomo Libo, et al.
Published: (2024)
by: Feigin, Shlomo Libo, et al.
Published: (2024)
Explaining Kernel Clustering via Decision Trees
by: Fleissner, Maximilian, et al.
Published: (2024)
by: Fleissner, Maximilian, et al.
Published: (2024)
Interpretable Self-Supervised Learning via Representer Landmarks and Nyström Approximation
by: Zarvandi, Maedeh, et al.
Published: (2025)
by: Zarvandi, Maedeh, et al.
Published: (2025)
Nonparametric Kernel Clustering with Bandit Feedback
by: Thuot, Victor, et al.
Published: (2026)
by: Thuot, Victor, et al.
Published: (2026)
The Quantization Benefits of Residual-Free Transformers
by: Ji, Yiping, et al.
Published: (2026)
by: Ji, Yiping, et al.
Published: (2026)
Less is More: Benchmarking LLM Based Recommendation Agents
by: Chauhan, Kargi, et al.
Published: (2026)
by: Chauhan, Kargi, et al.
Published: (2026)
Learning to Execute Graph Algorithms Exactly with Graph Neural Networks
by: Qharabagh, Muhammad Fetrat, et al.
Published: (2026)
by: Qharabagh, Muhammad Fetrat, et al.
Published: (2026)
Skew-Probabilistic Neural Networks for Learning from Imbalanced Data
by: Naik, Shraddha M., et al.
Published: (2023)
by: Naik, Shraddha M., et al.
Published: (2023)
Benchmark Success, Clinical Failure: When Reinforcement Learning Optimizes for Benchmarks, Not Patients
by: Berger, Armin, et al.
Published: (2025)
by: Berger, Armin, et al.
Published: (2025)
Cross-Attention Graph Neural Networks for Inferring Gene Regulatory Networks with Skewed Degree Distribution
by: Xiong, Jiaqi, et al.
Published: (2024)
by: Xiong, Jiaqi, et al.
Published: (2024)
Understanding the Failure Modes of Transformers through the Lens of Graph Neural Networks
by: Lee, Hunjae
Published: (2025)
by: Lee, Hunjae
Published: (2025)
Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment Approach
by: Yuan, Hanyang, et al.
Published: (2024)
by: Yuan, Hanyang, et al.
Published: (2024)
Generalisable Agents for Neural Network Optimisation
by: Tessera, Kale-ab, et al.
Published: (2023)
by: Tessera, Kale-ab, et al.
Published: (2023)
Power Failure Cascade Prediction using Graph Neural Networks
by: Chadaga, Sathwik, et al.
Published: (2024)
by: Chadaga, Sathwik, et al.
Published: (2024)
Privacy-Preserving Optimal Parameter Selection for Collaborative Clustering
by: Ghasemian, Maryam, et al.
Published: (2024)
by: Ghasemian, Maryam, et al.
Published: (2024)
Graph Convolutional Neural Networks Sensitivity under Probabilistic Error Model
by: Wang, Xinjue, et al.
Published: (2022)
by: Wang, Xinjue, et al.
Published: (2022)
Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks
by: Muschalik, Maximilian, et al.
Published: (2025)
by: Muschalik, Maximilian, et al.
Published: (2025)
On Rank-Dependent Generalisation Error Bounds for Transformers
by: Truong, Lan V.
Published: (2024)
by: Truong, Lan V.
Published: (2024)
Graph Neural Networks for Heart Failure Prediction on an EHR-Based Patient Similarity Graph
by: Boll, Heloisa Oss, et al.
Published: (2024)
by: Boll, Heloisa Oss, et al.
Published: (2024)
Generalization Error of Graph Neural Networks in the Mean-field Regime
by: Aminian, Gholamali, et al.
Published: (2024)
by: Aminian, Gholamali, et al.
Published: (2024)
Similar Items
-
Different Statistical Perspectives for Understanding Generalisation in Graph Neural Networks
by: Ayday, Nil, et al.
Published: (2026) -
Exact Certification of (Graph) Neural Networks Against Label Poisoning
by: Sabanayagam, Mahalakshmi, et al.
Published: (2024) -
Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks
by: Gosch, Lukas, et al.
Published: (2024) -
Exact Certification of Neural Networks and Partition Aggregation Ensembles against Label Poisoning
by: Mohgaonkar, Ajinkya, et al.
Published: (2026) -
Robustness Certificates for Neural Networks against Adversarial Attacks
by: Taheri, Sara, et al.
Published: (2025)