# RUBRIC: Realism--Utility Balanced Ranking for Imbalanced Classification

arXiv: https://arxiv.org/abs/2607.09816 · HTML: https://mlsanity.com/paper/2607.09816/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 38 references that are in the map.
- First posted on arXiv 10 Jul 2026.

## Builds on (most cited first, 8 of 8)

- [Learning Models with Uniform Performance via Distributionally Robust Optimization](https://mlsanity.com/paper/1810.08750/) (2018)
- [BAGAN: Data Augmentation with Balancing GAN](https://mlsanity.com/paper/1803.09655/) (2018)
- [Oversampling for Imbalanced Learning Based on K-Means and SMOTE](https://mlsanity.com/paper/1711.00837/) (2017)
- [MT2ST: Adaptive Multi-Task to Single-Task Learning](https://mlsanity.com/paper/2406.18038/) (2024)
- [MKA: Memory-Keyed Attention for Efficient Long-Context Reasoning](https://mlsanity.com/paper/2603.20586/) (2026)
- [HSGM: Hierarchical Segment-Graph Memory for Scalable Long-Text Semantics](https://mlsanity.com/paper/2509.18168/) (2025)
- [SemToken: Semantic-Aware Tokenization for Efficient Long-Context Language Modeling](https://mlsanity.com/paper/2508.15190/) (2025)
- [Boundary-Aware Adversarial Filtering for Reliable Diagnosis under Extreme Class Imbalance](https://mlsanity.com/paper/2511.17629/) (2025)

## Built on by (most cited first, 1 of 1)

- [Se-DPO: Self-Evolving Token Credit for Direct Preference Optimization](https://mlsanity.com/paper/2608.09568/) (2026)

## Abstract (arXiv)

Class imbalance poses a fundamental challenge in risk-sensitive applications such as fraud detection and medical diagnosis, where minority-class samples are scarce yet critical for accurate classification. Existing oversampling methods generate synthetic samples to rebalance class distributions; however, they often produce large numbers of low-quality candidates that distort decision boundaries or introduce artifacts, leading to overfitting and degraded generalization. In this work, we introduce RUBRIC, a generator-agnostic filtering framework that formulates synthetic sample selection as a quality-over-quantity optimization problem. RUBRIC ranks candidates using a realism-utility trade-off: realism is estimated via a neural density-ratio discriminator from each candidate's resemblance to real minority samples, while utility captures proximity to the decision boundary through a concave, margin-based scoring function g_-(t)=-log(1+e-t/τ). The discriminator uses the same architecture and training protocol on every benchmark and is fit independently to that dataset's real minority class versus its synthetic pool. We show that, under mild regularity conditions, the proposed filtering framework monotonically tightens the generalization bound for margin-based classifiers by jointly reducing distribution shift and suppressing near-negative tail contributions. Through extensive experiments on standard public imbalanced-classification benchmarks, we demonstrate that RUBRIC boosts minority-class recall while preserving overall discriminative ability across multiple data generators. Sensitivity analyses in λ and the selection budget K further characterize performance trade-offs oriented toward ranking quality.