On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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48 results for “marginal fairness”

Towards Marginal Fairness Sliced Wasserstein Barycenterstat.ML

Proposes MFSWB for marginal fairness in SWB, improving efficiency and performance.

problem Achieving marginal fairness in SWB averaging.

method Defining MFSWB as a constrained SWB problem, proposing two surrogate problems and a new slicing distribution.

result Surrogate MFSWB problems effectively minimize distances to marginals and encourage marginal fairness.

sliced Wasserstein barycenter marginal fairness Wasserstein distance point-cloud averaging color harmonization

2024-05-13abs ↗pdf ↗

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clusteringstat.ML

A new algorithm COVA-FC improves subgroup-fair clustering efficiency.

problem Challenges in making cluster assignments independent of sensitive attributes in subgroups.

method Defining a subgroup-fairness gap, deriving a covariance-based surrogate, and introducing a continuous relaxation for efficient optimization.

result COVA-FC achieves competitive cost-fairness trade-offs and improves computational efficiency.

subgroup-fair clustering covariance fairness gap subgroup-marginal-fairness efficient optimization

2026-07-20abs ↗pdf ↗

Bounding and Approximating Intersectional Fairness through Marginal Fairnessstat.ML

The paper explores intersectional fairness in machine learning, proving bounds on it.

problem Intersectional fairness in machine learning, especially when multiple protected attributes are involved.

method Statistical analysis and bounds on intersectional fairness, leveraging marginal fairness.

result Theoretical bounds on intersectional fairness can be computed from marginal fairness and other statistical quantities.

intersectional fairness marginal fairness machine learning protected attributes subgroups

2022-06-12abs ↗pdf ↗

Quantifying Infra-Marginality and Its Trade-off with Group Fairnesscs.AI

In critical decision-making scenarios, optimizing accuracy can lead to a biased classifier, hence past work recommends enforcing group-based fairness metrics in addition to maximizing accuracy. However, doing so exposes the classifier to another kind of bias called infra-marginality. This refers to individual-level bia…

2019-09-03abs ↗pdf ↗

Marginal Fairness: Fair Decision-Making under Risk Measuresstat.ML

New fairness criterion for risk-sensitive decisions in regulated industries.

problem Ensuring equitable outcomes in risk-sensitive decision-making.

method Marginal fairness for generalized distortion risk measures, two-step decision-making process.

result Ensures fairness in decision-making under risk measures, regardless of protected attributes.

fairness risk measures regulated industries individual fairness cascade sensitivity

2025-05-24abs ↗pdf ↗

Statistical and Structural Approaches to Algorithmic Fairnesscs.LG

Algorithmic fairness is a field of study that addresses the systematic disadvantage of marginalized groups in machine learning systems.

problem Modern machine learning systems increasingly determine access to economic and social opportunities, leading to structural inequalities and prejudices.

method Statistical and structural approaches to algorithmic fairness.

result The field of algorithmic fairness emerged to address the systematic disadvantage of marginalized groups in machine learning systems.

algorithmic fairness machine learning fairness socio-technical structural inequalities

2026-06-24abs ↗pdf ↗

An Empirical Study of Rich Subgroup Fairness for Machine Learningcs.LG

Kearns et al. [2018] recently proposed a notion of rich subgroup fairness intended to bridge the gap between statistical and individual notions of fairness. Rich subgroup fairness picks a statistical fairness constraint (say, equalizing false positive rates across protected groups), but then asks that this constraint h…

2018-08-24abs ↗pdf ↗

Impossibility results for fair representationscs.LG

New research shows fair data representations are impossible for different tasks.

problem Achieving fairness in machine learning models trained on various tasks.

method Analyzing the limits of fair data representations.

result No representation can guarantee fairness for different tasks trained on it.

fairness machine learning representation impossibility classification

2021-07-07abs ↗pdf ↗

Stock loan with Automatic termination clause, cap and marginq-fin.PR

This paper works out fair values of stock loan model with automatic termination clause, cap and margin. This stock loan is treated as a generalized perpetual American option with possibly negative interest rate and some constraints. Since it helps a bank to control the risk, the banks charge less service fees compared …

2010-05-09abs ↗pdf ↗

Differential Privacy has Bounded Impact on Fairness in Classificationcs.LG

Privacy affects fairness in classification models, but not drastically.

problem The impact of differential privacy on fairness in classification models.

method Theoretical analysis proving Lipschitz continuity of fairness measures and a non-asymptotic bound on fairness levels.

result Privacy impacts fairness, but not significantly as the number of samples increases.

differential privacy fairness classification Lipschitz continuity disparate impact

2022-10-28abs ↗pdf ↗

Learning Fair and Transferable Representationsstat.ML

Developing learning methods which do not discriminate subgroups in the population is a central goal of algorithmic fairness. One way to reach this goal is by modifying the data representation in order to meet certain fairness constraints. In this work we measure fairness according to demographic parity. This requires t…

2019-06-25abs ↗pdf ↗

Fairness in Multi-Task Learning via Wasserstein Barycentersstat.ML

Develops a method for fairness in multi-task learning using Wasserstein barycenters.

problem Extending fairness to multi-task learning with shared representations.

method Definition of Strong Demographic Parity extended to multi-task learning using multi-marginal Wasserstein barycenters. Closed form solution for optimal fair predictor.

result Empirical results show practical value of post-processing methodology in promoting fair decision-making.

Fairness Multi-task learning Wasserstein barycenters Demographic Parity Machine learning

2023-06-16abs ↗pdf ↗

Doubly-Regressing Approach for Subgroup Fairnessstat.ML

New algorithm tackles subgroup fairness in AI with multiple sensitive attributes.

problem Heavy computational burdens and data sparsity in subgroup fairness for multiple sensitive attributes.

method Doubly Regressing Adversarial learning (DRAF) for subgroup fairness, focusing on subgroups with sufficient sample sizes and marginal fairness.

result DRAF algorithm reduces a surrogate fairness gap for supIPM with less computation than directly reducing supIPM.

subgroup fairness multiple sensitive attributes subgroup-subset fairness supremum Integral Probability Metric (supIPM)Doubly Regressing Adversarial learning (DRAF)

2025-10-24abs ↗pdf ↗

README: REpresentation learning by fairness-Aware Disentangling MEthodcs.LG

New method learns fair representations by separating out protected attributes.

problem Learning fair representations invariant to protected attributes.

method FD-VAE: disentangles latent space into target, protected, and mutual attributes.

result FD-VAE outperforms previous methods in fairness metrics.

representation learning fairness disentanglement VAE downstream classification

2020-07-07abs ↗pdf ↗

Lookahead Counterfactual Fairnesscs.LG

Introduces lookahead counterfactual fairness to account for downstream effects of ML predictions.

problem Downstream effects of ML predictions on individuals not considered by counterfactual fairness.

method Introduces lookahead counterfactual fairness (LCF), a new fairness notion that considers future status. Proposes an algorithm based on theoretical conditions.

result Proposes an algorithm to achieve lookahead counterfactual fairness and validates it on synthetic and real data.

counterfactual fairness downstream effects lookahead counterfactual fairness path-dependent fairness

2024-12-02abs ↗pdf ↗

Who's the (Multi-)Fairest of Them All: Rethinking Interpolation-Based Data Augmentation Through the Lens of Multicalibrationcs.LG

Paper examines fairness of data augmentation methods, finding vanilla Mixup outperforms Fair Mixup.

problem Improving fairness in classification models with limited minority data.

method Uses multicalibration to rigorously evaluate and improve data augmentation methods for classification fairness.

result Vanilla Mixup outperforms Fair Mixup and baseline methods in fairness and accuracy, especially with small minority groups.

data augmentation classification fairness multicalibration vanilla Mixup minority groups

2024-12-13abs ↗pdf ↗

Fair Active Learning in Low-Data Regimescs.LG

New framework improves fairness in small data settings.

problem Ensuring fairness in low-data environments.

method Combines posterior sampling exploration with fair classification.

result Framework maximizes accuracy while meeting fairness constraints.

active learning fairness low data exploration classification

2023-12-13abs ↗pdf ↗

From Parity to Preference-based Notions of Fairness in Classificationstat.ML

The adoption of automated, data-driven decision making in an ever expanding range of applications has raised concerns about its potential unfairness towards certain social groups. In this context, a number of recent studies have focused on defining, detecting, and removing unfairness from data-driven decision systems. …

2017-06-30abs ↗pdf ↗

Scalable Spectral Clustering with Group Fairness Constraintscs.LG

New algorithm speeds up fair clustering by 12x.

problem Achieving fair clustering in scalable algorithms.

method s-FairSC algorithm incorporating nullspace projection and Hotelling's deflation.

result s-FairSC is 12x faster and maintains fairness.

fair clustering spectral clustering scalability group fairness nullspace projection

2022-10-28abs ↗pdf ↗

A Distributionally Robust Approach to Fair Classificationcs.LG

Proposes a fair classification model using robust optimization.

problem Preventing discrimination in classification models.

method Distributionally robust logistic regression with Wasserstein ball and convex unfairness measure.

result Improves fairness with minimal loss in predictive accuracy.

fairness classification logistic regression distributionally robust optimization Wasserstein ball

2020-07-18abs ↗pdf ↗

Attack-Resistant Uniform Fairness for Linear and Smooth Contextual Banditsstat.ML

This paper tackles fair online decision-making in contextual bandits, achieving optimal performance and fairness.

problem Fairness in online decision-making systems under strategic manipulation.

method Develops algorithms for linear and smooth reward functions, maintaining fairness and optimal regret.

result Achieves nearly minimax-optimal regret with strong fairness guarantees, even in the presence of attacks.

contextual bandits fairness regret minimization signal manipulation robust algorithms

2026-02-04abs ↗pdf ↗

Fair learning with Wasserstein barycenters for non-decomposable performance measuresstat.ML

The study connects fairness constraints with optimal transport to derive new insights in classification.

problem Ensuring fairness in classification models without sacrificing performance.

method Using Wasserstein barycenters and optimal transport, the study characterizes optimal classification functions under fairness constraints.

result Maximizing fairness under demographic parity is equivalent to solving a regression problem.

fairness classification optimal transport demographic parity Wasserstein barycenter

2022-09-01abs ↗pdf ↗

Localized Adaptive Risk Controlstat.ML

L-ARC improves model fairness by localizing risk guarantees.

problem Improving model fairness in tasks like image segmentation and wireless networks.

method Localized Adaptive Risk Control (L-ARC) updates a threshold function in RKHS to target localized statistical risk guarantees.

result L-ARC produces prediction sets with improved fairness across different data subpopulations.

fairness localized risk reproducing kernel Hilbert space image segmentation beam selection

2024-05-13abs ↗pdf ↗

A Sequentially Fair Mechanism for Multiple Sensitive Attributesstat.ML

Proposes a sequential framework for fairness in multiple sensitive attributes.

problem Fairness in the presence of multiple sensitive attributes.

method Sequential framework using multi-marginal Wasserstein barycenters.

result Closed-form solution for sequentially fair predictor.

fairness sequential fairness multiple sensitive attributes Wasserstein barycenters demographic parity

2023-09-12abs ↗pdf ↗

Are demographically invariant models and representations in medical imaging fair?cs.LG

Medical imaging models may encode demographic attributes without violating fairness, depending on the approach.

problem Discrimination in medical imaging models due to encoding demographic attributes.

method Examined marginal and class-conditional representation invariance, traditional fairness notions, and counterfactual fairness.

result Demographically invariant models may not necessarily be fair, and encoding demographic attributes can be advantageous.

medical imaging fairness representation invariance demographic attributes counterfactual fairness

2023-05-02abs ↗pdf ↗

Approaching Machine Learning Fairness through Adversarial Networkcs.LG

Fairness is becoming a rising concern w.r.t. machine learning model performance. Especially for sensitive fields such as criminal justice and loan decision, eliminating the prediction discrimination towards a certain group of population (characterized by sensitive features like race and gender) is important for enhanci…

2019-09-06abs ↗pdf ↗

A BSDE approach to fair bilateral pricing under endogenous collateralizationq-fin.MF

Our previous results are extended to the case of the margin account, which may depend on the contract's value for the hedger and/or the counterparty. The present work generalizes also the papers by Bergman (1995), Mercurio (2013) and Piterbarg (2010). Using the comparison theorems for BSDEs, we derive inequalities for …

2014-12-08abs ↗pdf ↗

DQ4FairIM: Fairness-aware Influence Maximization using Deep Reinforcement Learningcs.LG

DQ4FairIM uses RL to maximize influence while ensuring fairness across all groups.

problem Fairness in influence maximization in social networks.

method Fairness-aware deep RL method using Structure2Vec network embedding.

result DQ4FairIM achieves higher fairness than fairness-agnostic and fairness-aware baselines.

Influence Maximization Fairness Deep Reinforcement Learning Social Networks Markov Decision Process

2025-11-29abs ↗pdf ↗

Extracting Fairness Policies from Legal Documentscs.LG

Machine Learning community is recently exploring the implications of bias and fairness with respect to the AI applications. The definition of fairness for such applications varies based on their domain of application. The policies governing the use of such machine learning system in a given context are defined by the c…

2018-09-12abs ↗pdf ↗

Mind the Gap: A Causal Perspective on Bias Amplification in Prediction & Decision-Makingcs.LG

The paper addresses bias amplification in prediction and decision-making using causal analysis.

problem Bias amplification in automated systems, especially after thresholding.

method Introduces margin complement and causal decomposition of prediction disparities.

result Disparity in predictor

\widehat Y

can be decomposed into causal influences of

X

S

and

M

bias amplification causal analysis prediction disparities thresholding weak necessity

2024-05-24abs ↗pdf ↗

Minimax AUC Fairness: Efficient Algorithm with Provable Convergencecs.LG

Paper proposes a new algorithm to minimize AUC disparities in machine learning models.

problem Minimizing unfairness in AUC scores for machine learning models.

method Proposes a minimax learning and bias mitigation framework for AUC optimization.

result Proves the convergence of the proposed algorithm to minimize group-level AUC.

AUC fairness minimax ROC curve machine learning fairness

2022-08-22abs ↗pdf ↗

Predictive Representativity: Uncovering Racial Bias in AI-based Skin Cancer Detectioncs.LG

Study reveals AI skin cancer classifiers underperform for darker skin phototypes, advocating for fairness auditing.

problem AI bias in dermatology, particularly for darker skin phototypes.

method Predictive Representativity (PR) framework, evaluating classifiers on HAM10000 and BOSQUE Test sets.

result Substantial performance disparities by skin phototype, highlighting AI bias.

AI bias skin cancer detection predictive representativity fairness auditing data justice

2025-07-10abs ↗pdf ↗

Fair regression under localized demographic parity constraintsstat.ML

We relax demographic parity in regression by enforcing parity at quantile levels and score thresholds.

problem Enforcing full distributional fairness in regression can lead to substantial accuracy loss.

method Introduce (

\ell

, Z)-fair predictor, derive closed-form solutions, and develop post-processing algorithm.

result The risk gap to the continuous optimum vanishes as the grid is refined, and we enable targeted fairness corrections.

fair regression demographic parity quantile levels score thresholds post-processing

2026-03-26abs ↗pdf ↗

Malign Overfitting: Interpolation Can Provably Preclude Invariancecs.LG

Interpolation can prevent classifiers from having desired invariance properties.

problem Invariance properties in over-parameterized models are often ineffective.

method Theoretical analysis and algorithm design for non-interpolating classifiers.

result Interpolating classifiers cannot satisfy desired invariance properties.

overfitting invariance classification fairness robustness

2022-11-28abs ↗pdf ↗

Causal Multi-Level Fairnesscs.LG

The paper tackles multi-level fairness in algorithmic systems, addressing bias at both individual and structural levels.

problem Algorithmic systems can unfairly impact marginalized groups, especially when considering only individual-level bias.

method Formalizes multi-level fairness using causal inference tools, addressing effects of sensitive attributes at multiple levels.

result Illustrates the importance of accounting for macro-level sensitive attributes in fairness assessments.

algorithmic fairness causal inference multi-level structural bias income prediction

2020-10-14abs ↗pdf ↗

Fair bilateral prices in Bergman's modelq-fin.MF

Bielecki and Rutkowski (2014) introduced and studied a generic nonlinear market model, which includes several risky assets, multiple funding accounts and margin accounts. In this paper, we examine the pricing and hedging of contract both from the perspective of the hedger and the counterparty with arbitrary initial end…

2014-10-02abs ↗pdf ↗

Fair and profitable bilateral prices under funding costs and collateralizationq-fin.MF

2014-10-02abs ↗pdf ↗

Randomized Transport Plans via Hierarchical Fully Probabilistic Designeess.SY

New method designs fairer transport plans with uncertainty.

problem Designing fair and balanced mass transport plans.

method Hierarchical fully probabilistic design (HFPD) for transport plans.

result Optimal hyperprior for transport plans with uncertain marginals.

transport plans fair matching uncertainty quantification optimal transport Bayesian resetting

2024-08-04abs ↗pdf ↗

MVA Transfer Pricingq-fin.PR

This article prices OTC derivatives with either an exogenously determined initial margin profile or endogenously approximated initial margin. In the former case, margin valuation adjustment (MVA) is defined as the liability-side discounted expected margin profile, while in the latter, an extended partial differential e…

2015-12-23abs ↗pdf ↗

Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairnessstat.ML

A new method improves data representation for diverse tasks.

problem Learning meaningful representations for tasks like batch correction and counterfactual inference.

method Contrastive Mixture of Posteriors (CoMP) method using misalignment penalties.

result CoMP achieves state-of-the-art performance on challenging tasks.

Conditional VAE Latent Space Counterfactual Inference Fair Representation Learning Single-Cell RNA Sequencing

2021-06-15abs ↗pdf ↗

Mortality Heterogeneity and Actuarial Fairness in China's Notional Defined Contribution Pension Systemq-fin.RM

Study finds actuarial unfairness in China's pension system, proposing income-dependent annuitization rules.

problem Actuarial fairness in China's NDC pension system when mortality differs across income groups.

method Developed a mortality-differentiated Lee-Carter framework with group-specific baseline mortality schedules and a common period effect, estimated using national and subgroup data.

result Substantial actuarial unfairness in the current age-only divisor, with a reverse transfer from poorer to richer retirees.

China pension system actuarial fairness mortality heterogeneity Lee-Carter model income-dependent annuitization

2026-05-18abs ↗pdf ↗

Accelerometry-Derived Digital Biomarkers for Cardiometabolic Risk: A Population-Representative Tabular Benchmark with Uncertainty Quantificationcs.LG

New benchmark predicts cardiometabolic risk from accelerometer data, with varying accuracy.

problem Lack of accurate tabular benchmarks for cardiometabolic risk from accelerometer data.

method Tabular learning methods (ridge regression, XGBoost, TabPFN v2) applied to NHANES data.

result TabPFN v2 achieves best performance, but triglycerides remain largely unpredictable.

accelerometry cardiometabolic risk benchmark tabular learning NHANES

2026-06-29abs ↗pdf ↗

Separation-Utility Pareto Frontier: An Information-Theoretic Characterizationcs.LG

Study optimizes fairness in predictive models by balancing utility and separation.

problem Balancing fairness and utility in predictive models.

method Information-theoretic approach using conditional mutual information (CMI).

result Reduces separation violations while maintaining or improving utility.

fairness deep learning conditional mutual information Pareto frontier separation

2026-02-04abs ↗pdf ↗

Ultra-marginal Feature Importance: Learning from Data with Causal Guaranteesstat.ML

UMFI improves feature importance methods by reducing runtime and enhancing performance.

problem Improving feature importance methods to better explain causal and associative relationships in data.

method Introducing UMFI, which uses dependence removal techniques from AI fairness literature.

result UMFI outperforms MCI, especially in complex data scenarios, and reduces runtime from exponential to super-linear.

feature importance causal inference dependence removal machine learning data analysis

2022-04-21abs ↗pdf ↗

A Model for Counterparty Risk with Geometric Attenuation Effect and the Valuation of CDSq-fin.RM

In this paper, a geometric function is introduced to reflect the attenuation speed of impact of one firm's default to its partner. If two firms are competitions (copartners), the default intensity of one firm will decrease (increase) abruptly when the other firm defaults. As time goes on, the impact will decrease gradu…

2007-06-22abs ↗pdf ↗

Conformal Prediction Sets Can Cause Disparate Impactcs.LG

Conformal prediction sets can lead to unfair outcomes.

problem Disparate impact in decision-making with conformal prediction sets.

method Experiments with human participants to demonstrate disparate impact and propose equalizing set sizes across groups.

result Providing prediction sets that satisfy Equalized Coverage increases disparate impact compared to marginal coverage.

conformal prediction disparate impact Equalized Coverage set sizes human decision-making

2024-10-02abs ↗pdf ↗

Towards More General Control of Diffusion Models Using Jeffrey Guidancecs.LG

Jeffrey guidance extends diffusion-model control to more complex applications.

problem Controlling diffusion models beyond simple cases like conditional sampling.

method Leveraging Jeffrey's rule of conditioning to update marginal distributions towards a target distribution.

result Significant reductions in FID on CIFAR-10 and FFHQ with Inception embeddings as the target.

Jeffrey guidance diffusion models FID CIFAR-10 FFHQ

2026-06-11abs ↗pdf ↗

The Crossover Process: Learnability and Data Protection from Inference Attackscs.LG

It is usual to consider data protection and learnability as conflicting objectives. This is not always the case: we show how to jointly control inference --- seen as the attack --- and learnability by a noise-free process that mixes training examples, the Crossover Process (cp). One key point is that the cp~is typicall…

2016-06-13abs ↗pdf ↗