No Unique Minimizer, No Problem: On the Consistency of Robust Neural Classifiers
Subhabrata Majumdar, Anand Deo, Partha Pratim Saha, Abhik Ghosh
cs.LG, stat.ME, stat.ML
Submitted: 2026-08-09
Updated: 2026-08-11
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
- Provably robust learning of regression neural networks using $\beta$-divergences
- rSDNet: Unified Robust Neural Learning against Label Noise and Adversarial Attacks
- PubMedQA: A Dataset for Biomedical Research Question Answering
- Introducing Fractional Classification Loss for Robust Learning with Noisy Labels
- Attention Is All You Need
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks