mailitics

Tag: bias

  • Implicit Bias and Convergence of Matrix Stochastic Mirror Descent

    Implicit Bias and Convergence of Matrix Stochastic Mirror Descent arXiv:2602.18997v1 Announce Type: new Abstract: We investigate Stochastic Mirror Descent (SMD) with matrix parameters and vector-valued predictions, a framework relevant to multi-class classification and matrix completion problems. Focusing on the overparameterized regime, where the total number of parameters exceeds the number of training samples, we prove…

  • Bias-Corrected Data Synthesis for Imbalanced Learning

    Bias-Corrected Data Synthesis for Imbalanced Learning arXiv:2510.26046v1 Announce Type: new Abstract: Imbalanced data, where the positive samples represent only a small proportion compared to the negative samples, makes it challenging for classification problems to balance the false positive and false negative rates. A common approach to addressing the challenge involves generating synthetic data for the…

  • DoubleGen: Debiased Generative Modeling of Counterfactuals

    DoubleGen: Debiased Generative Modeling of Counterfactuals arXiv:2509.16842v1 Announce Type: new Abstract: Generative models for counterfactual outcomes face two key sources of bias. Confounding bias arises when approaches fail to account for systematic differences between those who receive the intervention and those who do not. Misspecification bias arises when methods attempt to address confounding through estimation…

  • A hierarchical entropy method for the delocalization of bias in high-dimensional Langevin Monte Carlo

    A hierarchical entropy method for the delocalization of bias in high-dimensional Langevin Monte Carlo arXiv:2509.08619v1 Announce Type: new Abstract: The unadjusted Langevin algorithm is widely used for sampling from complex high-dimensional distributions. It is well known to be biased, with the bias typically scaling linearly with the dimension when measured in squared Wasserstein distance. However,…

  • Toward Digital Well-Being: Using Generative AI to Detect and Mitigate Bias in Social Networks

    Toward Digital Well-Being: Using Generative AI to Detect and Mitigate Bias in Social Networks This research answered the question: How can machine learning and artificial intelligence help us to unlearn bias? The post Toward Digital Well-Being: Using Generative AI to Detect and Mitigate Bias in Social Networks appeared first on Towards Data Science. Celia Banks…

  • Regression-Based Estimation of Causal Effects in the Presence of Selection Bias and Confounding

    Regression-Based Estimation of Causal Effects in the Presence of Selection Bias and Confounding arXiv:2503.20546v1 Announce Type: new Abstract: We consider the problem of estimating the expected causal effect $E[Y|do(X)]$ for a target variable $Y$ when treatment $X$ is set by intervention, focusing on continuous random variables. In settings without selection bias or confounding, $E[Y|do(X)] =…

  • Off-Policy Evaluation for Recommendations with Missing-Not-At-Random Rewards

    Off-Policy Evaluation for Recommendations with Missing-Not-At-Random Rewards arXiv:2502.08993v1 Announce Type: new Abstract: Unbiased recommender learning (URL) and off-policy evaluation/learning (OPE/L) techniques are effective in addressing the data bias caused by display position and logging policies, thereby consistently improving the performance of recommendations. However, when both bias exits in the logged data, these estimators may suffer…