arXiv research

A new bias score method optimizes fairness in classification.

MultiwayPAM clusters LLM-as-a-Judge scores to reveal evaluator bias.

The paper tackles sampling bias in credit scoring models and proposes methods to improve their training and evaluation.

New scoring rule predicts causal relations from data with selection bias.

This work introduces a bias-variance decomposition for proper scores, improving uncertainty estimation in predictive models.

In the last two decades several biclustering methods have been developed as new unsupervised learning techniques to simultaneously cluster rows and columns of a data matrix. These algorithms play a central role in contemporary machine learning and in many applications, e.g. to computational biology and bioinformatics. …

New method improves sampling from score-based models by correcting bias.

New characterization limits sampling with inexact scores.

The paper addresses score-mismatched diffusion models and zero-shot conditional samplers.

Study improves unbiased recommender learning by addressing missing-reward bias.

Propensity score matching improves fairness in machine learning models.

Study reveals bias in machine learning conference reviews.

Proposes SD-KDE for density estimation using debiased kernel density with score-based adjustments.

Introduces privilege scores to measure and interpret protected attribute-related privilege in machine learning models.

Proposes stabilized weights for causal inference using isotonic calibration.

Annealed Langevin dynamics improves sampling from composite scores in SBI.

Updating predictive models introduces bias, leading to miscalibration.

In classification problems, sampling bias between training data and testing data is critical to the ranking performance of classification scores. Such bias can be both unintentionally introduced by data collection and intentionally introduced by the algorithm, such as under-sampling or weighting techniques applied to i…

New models reduce discrimination in machine learning without sacrificing explanatory bias.

Kernel-smoothed scores improve diffusion models by reducing memorization.

This study analyzes bias in face generation models and proposes mitigation techniques.

BC-ACI corrects time series forecast bias, improving prediction intervals.

Develops fair feature importance scores for tree-based models to interpret fairness.

Corrects bias in LLM-as-a-judge evaluations using adaptive calibration.

Bias and heterogeneity in peer assessment can lead to the issue of unfair scoring in the educational field. To deal with this problem, we propose a reference ranking method for an online peer assessment system using HodgeRank. Such a scheme provides instructors with an objective scoring reference based on mathematics.

The paper introduces a new framework to assess generative model uncertainty.

Study proposes a new method to estimate bias-correction term for ATE estimation.

Paper tackles bias-variance trade-off in missing data, proposing a dynamic framework.

Credit scoring models support loan approval decisions in the financial services industry. Lenders train these models on data from previously granted credit applications, where the borrowers' repayment behavior has been observed. This approach creates sample bias. The scoring model (i.e., classifier) is trained on accep…

A new method reduces the bias in estimating inverse covariance matrices from sketches.

The study examines when to trust confidence thresholding in pseudo-labelling regression.

Recidivism prediction scores are used across the USA to determine sentencing and supervision for hundreds of thousands of inmates. One such generator of recidivism prediction scores is Northpointe's Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) score, used in states like California and F…

New measure corrects news bias in NLP stock return forecasting.

We model and correct bias in sequential evaluation, improving ranking accuracy.

Paper tackles overestimation bias in continuous control, improving performance by 25%.

Deconfounding scores improve causal effect estimation with weak overlap.

Noise-corrected Langevin algorithm improves sampling from noisy data.

Quantum neural networks improve causal inference in biomedical studies, especially for small samples.

Unintended bias in Machine Learning can manifest as systemic differences in performance for different demographic groups, potentially compounding existing challenges to fairness in society at large. In this paper, we introduce a suite of threshold-agnostic metrics that provide a nuanced view of this unintended bias, by…

Study evaluates if LLMs have company-specific biases in financial sentiment analysis.

Data mining and machine learning techniques such as classification and regression trees (CART) represent a promising alternative to conventional logistic regression for propensity score estimation. Whereas incomplete data preclude the fitting of a logistic regression on all subjects, CART is appealing in part because s…

Directly estimates Fisher score for likelihood maximization.

The paper introduces Relative Bias to quantify LLM bias systematically.

A new method uses LLMs to discover causal pathways that affect fairness in machine learning.

With the development of high-throughput technologies, principal component analysis (PCA) in the high-dimensional regime is of great interest. Most of the existing theoretical and methodological results for high-dimensional PCA are based on the spiked population model in which all the population eigenvalues are equal ex…

Paper addresses selection bias in online advertising auctions.

New method detects and mitigates historical bias in data.

Some of the most effective influential spreader detection algorithms are unstable to small perturbations of the network structure. Inspired by bagging in Machine Learning, we propose the first Perturb and Combine (P&C) procedure for networks. It (1) creates many perturbed versions of a given graph, (2) applies a node s…