Pith. sign in

REVIEW 4 major objections 6 minor 61 references

Filtering synthetic minority samples by realism and boundary utility tightens generalization and raises recall under class imbalance.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

T0 review · grok-4.5

2026-07-14 15:20 UTC pith:KAIYYOY2

load-bearing objection Useful stackable post-filter with an explicit λ knob, but identical multi-generator RUBRIC rows undercut the generator-dependent realism story the theory needs. the 4 major comments →

arxiv 2607.09816 v1 pith:KAIYYOY2 submitted 2026-07-10 cs.LG

RUBRIC: Realism--Utility Balanced Ranking for Imbalanced Classification

Yanxuan Yu , Dong liu , Renata Borovica-Gajic , Ying Nian Wu This is my paper
classification cs.LG
keywords class imbalancesynthetic oversamplingsample selectionrealism-utility trade-offmargin-based generalizationfraud detectiondiscriminator filteringsubmodular selection
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Severe class imbalance forces practitioners to invent minority examples, but most generators also invent noise that warps the decision boundary. RUBRIC does not invent a new generator. It scores every synthetic candidate with two frozen helpers—one that judges how real the point looks, one that judges how useful it is near the current decision boundary—then keeps only a fixed budget of the best-ranked points, optionally with diversity. Under mild regularity conditions the paper proves that this quality-over-quantity filter shrinks total-variation shift from the true minority and exponentially damps harmful near-negative margin tails, which monotonically tightens a margin-based excess-risk bound. On fraud and other imbalanced tabular benchmarks the filter lifts F1 and recall for several generators while ROC-AUC stays comparable; a single knob λ lets the user trade those detection gains for better ranking quality (AUPRC) when needed.

Core claim

Selective, budgeted filtering of already-generated synthetic minority samples by a realism–utility score (discriminator log-odds plus a concave margin shaper) monotonically tightens the generalization bound of margin-based classifiers by jointly reducing distribution shift and suppressing near-negative margin contributions, and in practice improves recall and F1 under a fixed synthetic budget.

What carries the argument

The realism–utility ranking score s(x̃)=λu(x̃)+(1−λ)r(x̃), where r is the discriminator log-odds and u is a concave margin shaper, maximized under a fixed budget K (optionally plus a monotone submodular diversity term) and then used to train the final classifier only on the selected subset.

Load-bearing premise

The discriminator’s score must stay close enough to the true density ratio between real minority points and the candidate pool; if that calibration fails, the realism term no longer reliably controls distribution shift and the bound-tightening claim collapses.

What would settle it

Train the same generator with and without RUBRIC on a held-out extreme-imbalance set; if the selected subset does not reduce estimated total-variation distance to the real minority, or if excess risk and recall do not improve under the paper’s default λ and K while the discriminator is well-calibrated, the central claim is false.

Watch this falsifier — get emailed when new claim-graph text bears on it.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. RUBRIC is a generator-agnostic post-filter for synthetic minority samples in imbalanced classification. Candidates are scored by a realism–utility trade-off: realism via a discriminator logit trained to separate real minority points from the generator’s candidate pool, and utility via a concave margin shaper applied to a boundary model trained only on real data. Selection under a fixed budget is cast as monotone submodular maximization (facility-location diversity) with a (1−1/e) greedy guarantee. Under Assumptions 4.1–4.3 the authors claim that filtering tightens a margin-based excess-risk bound by reducing total-variation shift (Pinsker + discriminator calibration) and exponentially suppressing near-negative margin tails (Theorem 4.7 / Eq. 9). Experiments on Credit Card Fraud, IEEE-CIS, and Santander report mixed but often positive paired Δ in Recall/F1 for several generators, with an explicit λ-sensitivity analysis of the AUPRC–Recall frontier.

Significance. If the method and theory hold, the contribution is practically useful: a plug-in filter that can be stacked on any oversampler without redesigning generators, with an explicit user knob (λ) for ranking vs detection metrics and a submodular selection guarantee. The framing of synthetic selection as budgeted realism–utility optimization is cleaner than generator-specific heuristics, and the paper ships public code plus λ-sweeps and paired bootstrap Δ tables. The theoretical claim is modest (standard excess-risk terms plus TV and a margin floor) rather than a new learning principle, so significance rests mainly on whether the filter reliably improves risk-sensitive metrics without harming ranking quality—and whether the reported experiments actually implement the generator-dependent pipeline the theory requires.

major comments (4)
  1. Tables 4–5 and the long Appendix A–C tables show many distinct generators collapsing to numerically identical RUBRIC rows (same AUROC/AUPRC/F1/Recall to three decimals, often identical std). On Credit Card, NONE/SMOTE/SMOTE-Tomek/SMOTE-ENN/SVM-SMOTE/Borderline/ADASYN + RUBRIC share 0.959±0.021 / 0.632±0.223 / 0.413±0.336 / 0.828±0.160; only KMeans-SMOTE differs. Theorem 4.7 and Lemma 4.2 make the TV term data-dependent on the candidate density q induced by Gen: different S ⇒ different D ⇒ different score orderings ⇒ different U. Identical multi-generator RUBRIC metrics are incompatible with that pipeline unless selection ignores r or a single frozen U is reused. This is load-bearing for the claim that realism filtering reduces shift per generator; the main empirical support for bound tightening is not currently credible until the pipeline is audited and tables are regenerated with genera
  2. §4 / Theorem 4.7 (Eq. 9) is stated for linear predictors in an RKHS with convex L-Lipschitz surrogate loss and a margin floor induced by score thresholding (Assumptions 4.1–4.3). The main experimental suite (Tables 3–6, Appendix A–C) is dominated by tree ensembles (LightGBM, XGBoost, RandomForest, HistGradientBoosting, etc.) for which the margin-floor and near-negative-tail analysis does not apply in the form proved. The paper should either (i) restrict primary claims and tables to margin-based models that match the theory, or (ii) clearly demote Theorem 4.7 to a special case and provide a separate justification (or empirical diagnostic) for why the same filter improves non-margin models. As written, the strongest theoretical claim is only loosely connected to the bulk of the reported results.
  3. Main results are statistically mixed in a way that undercuts the abstract’s unqualified claim that RUBRIC “improves F1-macro and recall while maintaining comparable ROC-AUC across several generators.” Paired Δ tables (Tables 11–13) show significant F1 or Recall degradations for NONE, SVM-SMOTE, and KMeans-SMOTE on Credit Card, and F1 drops for tree models on Santander; AUPRC often falls when λ prioritizes utility. The abstract and introduction should state the precision–recall / AUPRC–Recall trade-off as the primary empirical finding (as §7 does), not net uniform improvement. Without that framing, the central empirical claim overstates what Tables 4–6 and 11–13 show.
  4. Assumption 4.1 (uniform discriminator calibration error ε_est) is the weakest link in the TV argument (Lemma 4.2). The paper never reports calibration diagnostics (e.g., estimated KL or TV between selected U and real minority, or ε_est proxies) per generator. Given that many generators already target boundary regions, D may be poorly calibrated or nearly constant on S, in which case r does not control shift and the joint reduction claimed in Theorem 4.7 need not hold. A short diagnostic table (δ̂ or density-ratio histograms for each Gen) would make the theory–experiment link falsifiable rather than assumed.
minor comments (6)
  1. Appendix F contains a large amount of loosely related material (multi-stage Q-learning-style recursions, “ultra-complex concentration,” combination-lock lower bounds) that is not used by Theorem 4.7. Trim to proofs of Lemmas 4.2–4.6 and Theorem 4.7; the rest reads as padding and weakens confidence in the theoretical appendix.
  2. Notation: abstract and §1 say “F1-macro” while tables report F1 (thresholded, validation F1-max). Align terminology and state whether F1 is binary or macro throughout.
  3. Figure 1 / heatmaps: “percentage improvements” without stating the baseline metric formula and without pairing to the bootstrap Δ tables makes it hard to reconcile visual gains with significant negative Δs in Tables 11–13.
  4. Related work cites several contemporaneous Liu & Yu long-context / KV-cache papers that are orthogonal to tabular oversampling; if retained, one sentence should justify relevance, otherwise drop to reduce citation noise.
  5. Table 1 training order is clear; Algorithm 1 line numbers in the text (“lines 6–8”) do not match the printed algorithm’s line count—fix cross-references.
  6. Default λ=0.5 is used for main tables while §6 and Figure 3 show strong λ sensitivity; state the default λ explicitly in every main-table caption.

Circularity Check

0 steps flagged

No significant circularity: the excess-risk bound is a conditional consequence of independent selection scores, not a tautology of its inputs.

full rationale

RUBRIC defines candidate scores independently of the excess-risk claim: utility u = g(margin_f) from a boundary model trained only on real data, realism r as discriminator log-odds, and selection as budgeted maximization of λu+(1−λ)r plus a facility-location diversity term (Eqs. 1–3, Prop. 3.1). Theorem 4.7 then states that if Assumptions 4.1–4.3 hold—so that selection induces a usable TV plug-in bound δ̂ via Pinsker (Lemma 4.2) and a margin floor m0 (Assumption 4.3)—the standard margin/Rademacher excess-risk form tightens by the additive terms C1(δ̂+ε_est)+C2 e^{−m0/τ}. That is a conditional bound under stated regularity, not a definition of success as the score itself, and λ is an explicit user trade-off rather than a parameter fitted to force the theorem. Self-citations (Liu & Yu long-context / KV-cache / multi-task papers; Yu et al. 2025 boundary-aware filtering) are labeled orthogonal or as prior line-of-work and do not supply a uniqueness theorem or ansatz that closes the imbalanced-learning derivation. Experimental anomalies (near-identical multi-generator RUBRIC rows) raise correctness questions about whether the generator-dependent D/S pipeline was actually run as described, but they do not make Eq. 9 reduce by construction to its inputs. No self-definitional loop, fitted-as-prediction, load-bearing self-citation chain, or renamed known result was found in the load-bearing theory chain.

Axiom & Free-Parameter Ledger

5 free parameters · 7 axioms · 2 invented entities

The central claim rests on standard margin/Rademacher learning theory plus three paper-specific modeling choices: a calibrated discriminator as density-ratio proxy, a selection-induced margin floor, and a concave utility shaper. Free knobs (λ, τ, γ, K, discriminator capacity) are user- or default-set and directly move the operating point. No new physical entity is postulated; the ‘invented’ objects are algorithmic scores and the filter itself.

free parameters (5)
  • lambda (realism–utility trade-off)
    User-chosen weight in s=λu+(1−λ)r; default 0.5; sweeps show it controls AUPRC vs Recall and is load-bearing for claimed metric gains.
  • tau (utility temperature)
    Temperature in g(t)=log(1+e^{t/τ}); default 1.0; sets Lipschitz constant and exponential tail suppression rate e^{−m0/τ} in Lemma 4.6.
  • gamma (diversity weight)
    Weight on facility-location Div(U); default 0.1; changes which subset is selected under budget K.
  • K (selection budget)
    Cardinality of retained synthetics; default |n1|; ablations show Recall depends strongly on K.
  • discriminator architecture / training hyperparameters
    2-layer MLP width 128, dropout 0.2, Adam lr, early stopping—determine r(˜x) and thus ranking; not derived from theory.
axioms (7)
  • standard math Pinsker’s inequality relating TV distance to KL divergence.
    Used in Lemma 4.2 to convert discriminator logits into a TV shift bound.
  • standard math Standard excess-risk / Rademacher bounds for L-Lipschitz bounded surrogate losses of linear predictors in an RKHS ball.
    Background for Theorem 4.7’s O(LB/√N) term.
  • standard math Monotone submodular maximization under cardinality admits a (1−1/e) greedy guarantee (Nemhauser et al.).
    Proposition 3.1 for diversity-aware selection.
  • domain assumption Assumption 4.1: discriminator logit approximates log(p/q) within uniform error ε_est.
    Required to treat r(˜x) as controlling distribution shift; not verified empirically beyond ranking use.
  • domain assumption Assumption 4.3: score-based selection induces a positive margin floor m0 for all retained synthetics.
    Needed for exponential near-negative tail suppression; diversity can only raise the floor in the authors’ abstraction.
  • ad hoc to paper Utility shaper g is nondecreasing, concave, and 1/τ-Lipschitz (default softplus-like form).
    Chosen to prioritize near-boundary samples; drives Lemma 4.6’s e^{−m0/τ} bound.
  • ad hoc to paper Boundary model f is trained only on real data and frozen before scoring; final classifier never trains on unfiltered candidates.
    Pipeline axiom (Table 1) that separates utility estimation from final training.
invented entities (2)
  • RUBRIC realism–utility score s(˜x)=λu(˜x)+(1−λ)r(˜x) no independent evidence
    purpose: Scalar ranking criterion combining discriminator log-odds and shaped margin utility for budgeted selection.
    Core algorithmic object; no independent existence outside the method.
  • Concave margin utility shaper g (default log(1+e^{t/τ})) no independent evidence
    purpose: Maps classifier margins to nonnegative utilities with diminishing returns away from the boundary.
    Design choice enabling the tail-suppression lemma; not an external physical quantity.

pith-pipeline@v1.1.0-grok45 · 68730 in / 4061 out tokens · 53006 ms · 2026-07-14T15:20:06.211375+00:00 · methodology

0 comments
read the original abstract

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 quantified by a learned discriminator that distinguishes real samples from synthetic samples, while utility captures proximity to the decision boundary through a concave margin-based scoring function. We show that, under mild regularity conditions, the proposed filtering strategy monotonically tightens the generalization bound for margin-based classifiers by jointly reducing distribution shift and suppressing near-negative tail contributions. Through extensive experiments on credit-card fraud detection and other imbalanced benchmarks, we demonstrate that RUBRIC improves F1-macro and recall while maintaining comparable ROC-AUC across several generators. We also provide explicit lambda-sensitivity analysis to show how users can recover AUPRC when ranking quality is prioritized.

Figures

Figures reproduced from arXiv: 2607.09816 by Dong Liu, Renata Borovica-Gajic, Yanxuan Yu, Ying Nian Wu.

Figure 1
Figure 1. Figure 1: Performance improvement heatmap (mean across seeds; error bars in supplement) showing percentage [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: IEEE-CIS: average improvement summary with mean±std over seeds (error bars). This figure illustrates variability across runs and complements the heatmap aggregation. requiring high AUPRC should set λ ≤ 0.3; detection￾oriented settings benefit from λ ≥ 0.7. Runtime. RUBRIC filtering adds a one-time post￾processing cost before final classifier training ( [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Performance comparison across three datasets [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Credit Card: SMOTE vs SMOTE+RUBRIC across metrics (mean over seeds; see main text for [PITH_FULL_IMAGE:figures/full_fig_p036_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: IEEE-CIS: SMOTE vs SMOTE+RUBRIC across metrics (mean over seeds; see main text for protocol). [PITH_FULL_IMAGE:figures/full_fig_p037_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Santander: SMOTE vs SMOTE+RUBRIC across metrics (mean over seeds; see main text for protocol). [PITH_FULL_IMAGE:figures/full_fig_p038_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Credit Card improvement heatmap (mean across seeds). [PITH_FULL_IMAGE:figures/full_fig_p038_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Santander improvement heatmap (mean across seeds). [PITH_FULL_IMAGE:figures/full_fig_p039_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Credit Card: average improvement summary (mean [PITH_FULL_IMAGE:figures/full_fig_p039_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: IEEE-CIS: average improvement summary (mean [PITH_FULL_IMAGE:figures/full_fig_p040_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Santander: average improvement summary (mean [PITH_FULL_IMAGE:figures/full_fig_p040_12.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

61 extracted references · 1 linked inside Pith

  1. [1]

    Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD) , pages=

    SMOTE: Synthetic Minority Over-sampling Technique , author=. Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD) , pages=

  2. [2]

    IEEE Transactions on Knowledge and Data Engineering , volume=

    Learning from imbalanced data , author=. IEEE Transactions on Knowledge and Data Engineering , volume=

  3. [3]

    International Conference on Intelligent Computing , pages=

    Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning , author=. International Conference on Intelligent Computing , pages=. 2005 , organization=

    2005

  4. [4]

    IEEE International Joint Conference on Neural Networks (IJCNN) , pages=

    ADASYN: Adaptive synthetic sampling approach for imbalanced learning , author=. IEEE International Joint Conference on Neural Networks (IJCNN) , pages=

  5. [5]

    International Journal of Knowledge Engineering and Soft Data Paradigms , year=

    Borderline over-sampling for imbalanced data classification , author=. International Journal of Knowledge Engineering and Soft Data Paradigms , year=

  6. [6]

    EPIA Conference on Artificial Intelligence , pages=

    SMOTE for Regression with K-Means and Kernel Functions , author=. EPIA Conference on Artificial Intelligence , pages=

  7. [7]

    Progress in Artificial Intelligence , volume=

    Learning from imbalanced data: open challenges and future directions , author=. Progress in Artificial Intelligence , volume=

  8. [8]

    Expert Systems with Applications , volume=

    Effective data generation for imbalanced learning using conditional generative adversarial networks , author=. Expert Systems with Applications , volume=

  9. [9]

    Information Sciences , volume=

    Using generative adversarial networks for improving classification effectiveness in credit card fraud detection , author=. Information Sciences , volume=

  10. [10]

    Expert Systems with Applications , volume=

    Effective Data Generation for Imbalanced Learning Using Conditional GANs , author=. Expert Systems with Applications , volume=. 2018 , publisher=

    2018

  11. [11]

    PLoS ONE , volume=

    The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets , author=. PLoS ONE , volume=

  12. [12]

    ICML , pages=

    The Relationship Between Precision-Recall and ROC Curves , author=. ICML , pages=

  13. [13]

    AISTATS , year=

    Beta Calibration: a Well-Founded and Easily Implemented Improvement on Logistic Calibration for Binary Classifiers , author=. AISTATS , year=

  14. [14]

    2018 , publisher=

    Foundations of Machine Learning , author=. 2018 , publisher=

    2018

  15. [15]

    2018 , url=

    Credit Card Fraud Detection , author=. 2018 , url=

    2018

  16. [16]

    Information Sciences , volume=

    Oversampling for Imbalanced Learning Based on K-Means and SMOTE , author=. Information Sciences , volume=. 2018 , publisher=

    2018

  17. [17]

    Journal of Machine Learning Research , volume=

    Rademacher and Gaussian Complexities: Risk Bounds and Structural Results , author=. Journal of Machine Learning Research , volume=

  18. [18]

    The Annals of Statistics , volume =

    Learning Models with Uniform Performance via Distributionally Robust Optimization , author =. The Annals of Statistics , volume =. 2021 , publisher =. doi:10.1214/20-AOS2004 , url =. 1810.08750 , archivePrefix =

    Pith/arXiv arXiv doi:10.1214/20-aos2004 2021

  19. [19]

    2015 IEEE Symposium Series on Computational Intelligence (SSCI) , pages=

    Calibrating Probability with Undersampling for Unbalanced Classification , author=. 2015 IEEE Symposium Series on Computational Intelligence (SSCI) , pages=. 2015 , organization=

    2015

  20. [20]

    2019 , url=

    Santander Customer Transaction Prediction , author=. 2019 , url=

    2019

  21. [21]

    Pacific-Asia Conference on Knowledge Discovery and Data Mining , pages=

    Safe-Level-SMOTE: Safe-Level-Synthetic Minority Over-sampling Technique for Handling the Class Imbalanced Problem , author=. Pacific-Asia Conference on Knowledge Discovery and Data Mining , pages=. 2009 , organization=

    2009

  22. [22]

    IEEE Transactions on Knowledge and Data Engineering , volume=

    MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning , author=. IEEE Transactions on Knowledge and Data Engineering , volume=. 2014 , publisher=

    2014

  23. [23]

    Journal of Artificial Intelligence Research , volume=

    SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary , author=. Journal of Artificial Intelligence Research , volume=

  24. [24]

    2018 , note=

    BAGAN: Data Augmentation with Balancing GAN , author=. 2018 , note=

    2018

  25. [25]

    IEEE Access , volume=

    Handling Imbalanced Data in GAN-based Image Generation via Adaptive Weighted Discriminator , author=. IEEE Access , volume=. 2020 , publisher=

    2020

  26. [26]

    Advances in Neural Information Processing Systems , volume=

    Generative Adversarial Nets , author=. Advances in Neural Information Processing Systems , volume=

  27. [27]

    2014 , note=

    Conditional Generative Adversarial Nets , author=. 2014 , note=

    2014

  28. [28]

    2015 , note=

    Explaining and Harnessing Adversarial Examples , author=. 2015 , note=

    2015

  29. [29]

    2012 , publisher=

    Density Ratio Estimation in Machine Learning , author=. 2012 , publisher=

    2012

  30. [30]

    Annals of Statistics , volume=

    Empirical margin distributions and bounding the generalization error of combined classifiers , author=. Annals of Statistics , volume=. 2002 , publisher=

    2002

  31. [31]

    IEEE Transactions on Information Theory , volume=

    The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network , author=. IEEE Transactions on Information Theory , volume=. 1998 , publisher=

    1998

  32. [32]

    Annals of Statistics , volume=

    Boosting the margin: A new explanation for the effectiveness of voting methods , author=. Annals of Statistics , volume=. 1998 , publisher=

    1998

  33. [33]

    2009 , publisher=

    Introduction to Nonparametric Estimation , author=. 2009 , publisher=

    2009

  34. [34]

    1964 , publisher=

    Information and Information Stability of Random Variables and Processes , author=. 1964 , publisher=

    1964

  35. [35]

    2012 , publisher=

    Elements of Information Theory , author=. 2012 , publisher=

    2012

  36. [36]

    Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI) , pages=

    The Foundations of Cost-Sensitive Learning , author=. Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI) , pages=

  37. [37]

    IEEE Transactions on Knowledge and Data Engineering , volume =

    Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem , author =. IEEE Transactions on Knowledge and Data Engineering , volume =. 2006 , month = jan, doi =

    2006

  38. [38]

    IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) , volume=

    A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches , author=. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) , volume=. 2012 , publisher=

    2012

  39. [39]

    IEEE Transactions on Systems, Man, and Cybernetics---Part A: Systems and Humans , volume =

    RUSBoost: A Hybrid Approach to Alleviating Class Imbalance , author =. IEEE Transactions on Systems, Man, and Cybernetics---Part A: Systems and Humans , volume =. 2010 , doi =

    2010

  40. [40]

    Expert Systems with Applications , volume=

    Learning from class-imbalanced data: Review of methods and applications , author=. Expert Systems with Applications , volume=. 2017 , publisher=

    2017

  41. [41]

    Journal of Big Data , volume=

    Survey on deep learning with class imbalance , author=. Journal of Big Data , volume=. 2019 , publisher=

    2019

  42. [42]

    Imbalanced Learning: Foundations, Algorithms, and Applications , pages=

    Foundations of Imbalanced Learning , author=. Imbalanced Learning: Foundations, Algorithms, and Applications , pages=. 2013 , publisher=

    2013

  43. [43]

    ACM Computing Surveys (CSUR) , volume=

    A survey of predictive modeling on imbalanced domains , author=. ACM Computing Surveys (CSUR) , volume=. 2017 , publisher=

    2017

  44. [44]

    Advances in Large Margin Classifiers , pages=

    Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods , author=. Advances in Large Margin Classifiers , pages=. 1999 , publisher=

    1999

  45. [45]

    Proceedings of the 34th International Conference on Machine Learning (ICML) , pages=

    On Calibration of Modern Neural Networks , author=. Proceedings of the 34th International Conference on Machine Learning (ICML) , pages=. 2017 , organization=

    2017

  46. [46]

    Proceedings of the IEEE International Conference on Computer Vision , pages=

    Focal Loss for Dense Object Detection , author=. Proceedings of the IEEE International Conference on Computer Vision , pages=

  47. [47]

    Advances in Neural Information Processing Systems , volume=

    On the relationship between binary classification, bipartite ranking, and binary class probability estimation , author=. Advances in Neural Information Processing Systems , volume=

  48. [48]

    Machine Learning , volume=

    A theory of learning from different domains , author=. Machine Learning , volume=. 2010 , publisher=

    2010

  49. [49]

    Advances in Neural Information Processing Systems , volume=

    Domain Adaptation with Multiple Sources , author=. Advances in Neural Information Processing Systems , volume=

  50. [50]

    2002 , publisher=

    Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , author=. 2002 , publisher=

    2002

  51. [51]

    Journal of Machine Learning Research , volume=

    Stability and generalization , author=. Journal of Machine Learning Research , volume=

  52. [52]

    2014 , publisher=

    Understanding Machine Learning: From Theory to Algorithms , author=. 2014 , publisher=

    2014

  53. [53]

    2025 , eprint =

    Boundary-Aware Adversarial Filtering for Reliable Diagnosis under Extreme Class Imbalance , author =. 2025 , eprint =

    2025

  54. [54]

    Machine Learning , volume =

    A survey on semi-supervised learning , author =. Machine Learning , volume =. 2020 , doi =

    2020

  55. [55]

    Mathematical Programming , volume =

    An analysis of approximations for maximizing submodular set functions---I , author =. Mathematical Programming , volume =. 1978 , doi =

    1978

  56. [56]

    Tractability: Practical Approaches to Hard Problems , editor =

    Submodular Function Maximization , author =. Tractability: Practical Approaches to Hard Problems , editor =

  57. [57]

    2025 , address =

    Liu, Dong and Yu, Yanxuan , booktitle =. 2025 , address =

    2025

  58. [58]

    2026 , address =

    Liu, Dong and Yu, Yanxuan , booktitle =. 2026 , address =

    2026

  59. [59]

    2025 , publisher =

    Liu, Dong and Yu, Yanxuan , booktitle =. 2025 , publisher =

    2025

  60. [60]

    Liu, Dong and Yu, Yanxuan and Lengerich, Ben and Wu, Ying Nian , booktitle =

  61. [61]

    2026 , address =

    Liu, Dong and Yu, Yanxuan , booktitle =. 2026 , address =. doi:10.1145/3748173.3779188 , url =

    doi:10.1145/3748173.3779188 2026