# Class-Weighted Classification: Trade-offs and Robust Approaches

arXiv: https://arxiv.org/abs/2005.12914 · HTML: https://mlsanity.com/paper/2005.12914/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2020.

## Built on by (most cited first, 30 of 30)

- [Test-Time Adaptation via Conjugate Pseudo-labels](https://mlsanity.com/paper/2207.09640/) (2022)
- [DORO: Distributional and Outlier Robust Optimization](https://mlsanity.com/paper/2106.06142/) (2021)
- [TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification](https://mlsanity.com/paper/2206.12917/) (2022)
- [Minimax Regret Optimization for Robust Machine Learning under Distribution Shift](https://mlsanity.com/paper/2202.05436/) (2022)
- [Improving Task-free Continual Learning by Distributionally Robust Memory Evolution](https://mlsanity.com/paper/2207.07256/) (2022)
- [Understanding Why Generalized Reweighting Does Not Improve Over ERM](https://mlsanity.com/paper/2201.12293/) (2022)
- [Distributionally Robust Post-hoc Classifiers under Prior Shifts](https://mlsanity.com/paper/2309.08825/) (2023)
- [Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition](https://mlsanity.com/paper/2310.18765/) (2023)
- [Boosted CVaR Classification](https://mlsanity.com/paper/2110.13948/) (2021)
- [Robust Distillation for Worst-class Performance](https://mlsanity.com/paper/2206.06479/) (2022)
- [Training Class-Imbalanced Diffusion Model Via Overlap Optimization](https://mlsanity.com/paper/2402.10821/) (2024)
- [Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants](https://mlsanity.com/paper/2402.03819/) (2024)
- [Revisiting adversarial training for the worst-performing class](https://mlsanity.com/paper/2302.08872/) (2023)
- [Stochastic Approximation Approaches to Group Distributionally Robust Optimization and Beyond](https://mlsanity.com/paper/2302.09267/) (2023)
- [Sharp error bounds for imbalanced classification: how many examples in the minority class?](https://mlsanity.com/paper/2310.14826/) (2023)
- [Concentration and excess risk bounds for imbalanced classification with synthetic oversampling](https://mlsanity.com/paper/2510.20472/) (2025)
- [Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise](https://mlsanity.com/paper/2411.06697/) (2024)
- [Generalizing to Unseen Domains with Wasserstein Distributional Robustness under Limited Source Knowledge](https://mlsanity.com/paper/2207.04913/) (2022)
- [Resilience in Knowledge Graph Embeddings](https://mlsanity.com/paper/2410.21163/) (2024)
- [Distributionally Robust Optimization with Adversarial Data Contamination](https://mlsanity.com/paper/2507.10718/) (2025)
- [DeepArrhythmia: Segment-Contextualized ECG Arrhythmia Classification via Selective Evidence Acquisition](https://mlsanity.com/paper/2605.16441/) (2026)
- [Nonparametric Trace Regression in High Dimensions via Sign Series Representation](https://mlsanity.com/paper/2105.01783/) (2021)
- [Deterministic Online Classification: Non-iteratively Reweighted Recursive Least-Squares for Binary Class Rebalancing](https://mlsanity.com/paper/2301.09230/) (2023)
- [Distributionally Robust Cross Subject EEG Decoding](https://mlsanity.com/paper/2308.11651/) (2023)
- [Robust Long-Tailed Learning via Label-Aware Bounded CVaR](https://mlsanity.com/paper/2308.15405/) (2023)
- [Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels](https://mlsanity.com/paper/2508.21424/) (2025)
- [Robust Learning of a Group DRO Neuron](https://mlsanity.com/paper/2601.18115/) (2026)
- [From Detection to Response: A Deep Learning and Retrieval-Augmented Generation Framework for Network Intrusion Mitigation](https://mlsanity.com/paper/2605.17960/) (2026)
- [NodeImport: Imbalanced Node Classification with Node Importance Assessment](https://mlsanity.com/paper/2607.13837/) (2026)
- [WildFin: An In-the-Wild Dataset for Fish Behavioral Recognition](https://mlsanity.com/paper/2608.21281/) (2026)

## Abstract (arXiv)

We address imbalanced classification, the problem in which a label may have low marginal probability relative to other labels, by weighting losses according to the correct class. First, we examine the convergence rates of the expected excess weighted risk of plug-in classifiers where the weighting for the plug-in classifier and the risk may be different. This leads to irreducible errors that do not converge to the weighted Bayes risk, which motivates our consideration of robust risks. We define a robust risk that minimizes risk over a set of weightings and show excess risk bounds for this problem. Finally, we show that particular choices of the weighting set leads to a special instance of conditional value at risk (CVaR) from stochastic programming, which we call label conditional value at risk (LCVaR). Additionally, we generalize this weighting to derive a new robust risk problem that we call label heterogeneous conditional value at risk (LHCVaR). Finally, we empirically demonstrate the efficacy of LCVaR and LHCVaR on improving class conditional risks.