A Theoretical Analysis of the Learning Dynamics under Class Imbalance
Fuente:
arXiv
Saved in:
| Main Authors: | Francazi, Emanuele, Baity-Jesi, Marco, Lucchi, Aurelien |
|---|---|
| Format: | Preprint |
| Published: |
2022
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Initial Guessing Bias: How Untrained Networks Favor Some Classes
by: Francazi, Emanuele, et al.
Published: (2023)
by: Francazi, Emanuele, et al.
Published: (2023)
Where You Place the Norm Matters: From Prejudiced to Neutral Initializations
by: Francazi, Emanuele, et al.
Published: (2025)
by: Francazi, Emanuele, et al.
Published: (2025)
When Bias Meets Trainability: Connecting Theories of Initialization
by: Bassi, Alberto, et al.
Published: (2025)
by: Bassi, Alberto, et al.
Published: (2025)
Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable Model
by: Pezzicoli, F. S., et al.
Published: (2025)
by: Pezzicoli, F. S., et al.
Published: (2025)
Producing Plankton Classifiers that are Robust to Dataset Shift
by: Chen, Cheng, et al.
Published: (2024)
by: Chen, Cheng, et al.
Published: (2024)
Why Do We Need Warm-up? A Theoretical Perspective
by: Alimisis, Foivos, et al.
Published: (2025)
by: Alimisis, Foivos, et al.
Published: (2025)
Theoretical characterisation of the Gauss-Newton conditioning in Neural Networks
by: Zhao, Jim, et al.
Published: (2024)
by: Zhao, Jim, et al.
Published: (2024)
A Comprehensive Analysis on the Learning Curve in Kernel Ridge Regression
by: Cheng, Tin Sum, et al.
Published: (2024)
by: Cheng, Tin Sum, et al.
Published: (2024)
Theoretical Analysis of Contrastive Learning under Imbalanced Data: From Training Dynamics to a Pruning Solution
by: Liao, Haixu, et al.
Published: (2026)
by: Liao, Haixu, et al.
Published: (2026)
Adaptive Methods through the Lens of SDEs: Theoretical Insights on the Role of Noise
by: Compagnoni, Enea Monzio, et al.
Published: (2024)
by: Compagnoni, Enea Monzio, et al.
Published: (2024)
Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs
by: Compagnoni, Enea Monzio, et al.
Published: (2025)
by: Compagnoni, Enea Monzio, et al.
Published: (2025)
A Closer Look at AUROC and AUPRC under Class Imbalance
by: McDermott, Matthew B. A., et al.
Published: (2024)
by: McDermott, Matthew B. A., et al.
Published: (2024)
Cubic regularized subspace Newton for non-convex optimization
by: Zhao, Jim, et al.
Published: (2024)
by: Zhao, Jim, et al.
Published: (2024)
Class-Imbalanced Graph Learning without Class Rebalancing
by: Liu, Zhining, et al.
Published: (2023)
by: Liu, Zhining, et al.
Published: (2023)
Multimodal Deep Generative Model for Semi-Supervised Learning under Class Imbalance
by: Yoon, Heegeon, et al.
Published: (2026)
by: Yoon, Heegeon, et al.
Published: (2026)
Quantum-Informed Contrastive Learning with Dynamic Mixup Augmentation for Class-Imbalanced Expert Systems
by: Jahin, Md Abrar, et al.
Published: (2025)
by: Jahin, Md Abrar, et al.
Published: (2025)
Class-Dependent Hybrid Data Augmentation for Multiclass Migraine Classification under Severe Class Imbalance
by: Somón, Elvin, et al.
Published: (2026)
by: Somón, Elvin, et al.
Published: (2026)
An Automated Machine Learning Framework for Surgical Suturing Action Detection under Class Imbalance
by: Zhang, Baobing, et al.
Published: (2025)
by: Zhang, Baobing, et al.
Published: (2025)
Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum
by: Cheng, Tin Sum, et al.
Published: (2024)
by: Cheng, Tin Sum, et al.
Published: (2024)
Optimizer choice matters for the emergence of Neural Collapse
by: Zhao, Jim, et al.
Published: (2026)
by: Zhao, Jim, et al.
Published: (2026)
An Analytical Model for Overparameterized Learning Under Class Imbalance
by: Mor, Eliav, et al.
Published: (2025)
by: Mor, Eliav, et al.
Published: (2025)
Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers
by: Anagnostidis, Sotiris, et al.
Published: (2023)
by: Anagnostidis, Sotiris, et al.
Published: (2023)
On the Impact of Class Imbalance on the Learning Dynamics of Deep Neural Networks:An Intuitive Insight
by: Mustapha, Ismail B., et al.
Published: (2026)
by: Mustapha, Ismail B., et al.
Published: (2026)
Loss Landscape Characterization of Neural Networks without Over-Parametrization
by: Islamov, Rustem, et al.
Published: (2024)
by: Islamov, Rustem, et al.
Published: (2024)
Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size
by: Islamov, Rustem, et al.
Published: (2025)
by: Islamov, Rustem, et al.
Published: (2025)
On the Interaction of Batch Noise, Adaptivity, and Compression, under $(L_0,L_1)$-Smoothness: An SDE Approach
by: Compagnoni, Enea Monzio, et al.
Published: (2025)
by: Compagnoni, Enea Monzio, et al.
Published: (2025)
A Direct Classification Approach for Reliable Wind Ramp Event Forecasting under Severe Class Imbalance
by: Morales-Hernández, Alejandro, et al.
Published: (2026)
by: Morales-Hernández, Alejandro, et al.
Published: (2026)
Boundary-Aware Adversarial Filtering for Reliable Diagnosis under Extreme Class Imbalance
by: Yu, Yanxuan, et al.
Published: (2025)
by: Yu, Yanxuan, et al.
Published: (2025)
Class-Imbalanced Complementary-Label Learning via Weighted Loss
by: Wei, Meng, et al.
Published: (2022)
by: Wei, Meng, et al.
Published: (2022)
Few-Shot Learning with Class Imbalance
by: Ochal, Mateusz, et al.
Published: (2021)
by: Ochal, Mateusz, et al.
Published: (2021)
Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance
by: Kaushik, Chiraag, et al.
Published: (2024)
by: Kaushik, Chiraag, et al.
Published: (2024)
Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions
by: Islamov, Rustem, et al.
Published: (2026)
by: Islamov, Rustem, et al.
Published: (2026)
Active Learning for Planet Habitability Classification under Extreme Class Imbalance
by: El-Kholy, R. I., et al.
Published: (2026)
by: El-Kholy, R. I., et al.
Published: (2026)
Balancing the Scales: A Theoretical and Algorithmic Framework for Learning from Imbalanced Data
by: Cortes, Corinna, et al.
Published: (2025)
by: Cortes, Corinna, et al.
Published: (2025)
Optimization Guarantees for Square-Root Natural-Gradient Variational Inference
by: Kumar, Navish, et al.
Published: (2025)
by: Kumar, Navish, et al.
Published: (2025)
Neurosymbolic Learning for Advanced Persistent Threat Detection under Extreme Class Imbalance
by: Fathima, Quhura, et al.
Published: (2026)
by: Fathima, Quhura, et al.
Published: (2026)
Gradient Scalability and Taylor Surrogation of Quantum Cost Landscapes
by: Meyer, Sabri, et al.
Published: (2025)
by: Meyer, Sabri, et al.
Published: (2025)
Orthogonal Activation with Implicit Group-Aware Bias Learning for Class Imbalance
by: Kishanthan, Sukumar, et al.
Published: (2025)
by: Kishanthan, Sukumar, et al.
Published: (2025)
Harmonized Gradient Descent for Class Imbalanced Data Stream Online Learning
by: Zhou, Han, et al.
Published: (2025)
by: Zhou, Han, et al.
Published: (2025)
FedSat: A Statistical Aggregation Approach for Class Imbalanced Clients in Federated Learning
by: Chowdhury, Sujit, et al.
Published: (2024)
by: Chowdhury, Sujit, et al.
Published: (2024)
Similar Items
-
Initial Guessing Bias: How Untrained Networks Favor Some Classes
by: Francazi, Emanuele, et al.
Published: (2023) -
Where You Place the Norm Matters: From Prejudiced to Neutral Initializations
by: Francazi, Emanuele, et al.
Published: (2025) -
When Bias Meets Trainability: Connecting Theories of Initialization
by: Bassi, Alberto, et al.
Published: (2025) -
Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable Model
by: Pezzicoli, F. S., et al.
Published: (2025) -
Producing Plankton Classifiers that are Robust to Dataset Shift
by: Chen, Cheng, et al.
Published: (2024)