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Found 10 papers, 4 papers with code
Federated Learning with Extremely Noisy Clients via Negative Distillation
The model trained on noisy labels serves as a `bad teacher' in knowledge distillation, aiming to decrease the risk of providing incorrect information.
MISNN: Multiple Imputation via Semi-parametric Neural Networks
Multiple imputation (MI) has been widely applied to missing value problems in biomedical, social and econometric research, in order to avoid improper inference in the downstream data analysis.
Label-Noise Learning with Intrinsically Long-Tailed Data
In this case, it is hard to distinguish clean samples from noisy samples on the intrinsic tail classes with the unknown intrinsic class distribution.
Assessing Fairness in the Presence of Missing Data
When the goal is to develop a fair algorithm in the complete data domain where there are no missing values, an algorithm that is fair in the complete case domain may show disproportionate bias towards some marginalized groups in the complete data domain.
Fairness in Missing Data Imputation
Missing data are ubiquitous in the era of big data and, if inadequately handled, are known to lead to biased findings and have deleterious impact on data-driven decision makings.
An Unconstrained Layer-Peeled Perspective on Neural Collapse
We prove that gradient flow on this model converges to critical points of a minimum-norm separation problem exhibiting neural collapse in its global minimizer.
How Gradient Descent Separates Data with Neural Collapse: A Layer-Peeled Perspective
In this paper, we derive a landscape analysis to the surrogate model to study the inductive bias of the neural features and parameters from neural networks with cross-entropy.
Efficient Designs of SLOPE Penalty Sequences in Finite Dimension
In this paper, we propose two efficient algorithms to design the possibly high-dimensional SLOPE penalty, in order to minimize the mean squared error.
Fairness guarantee in analysis of incomplete data
While there is a growing body of literature on fairness in analysis of fully observed data, there has been little work on investigating fairness in analysis of incomplete data when the goal is to develop a fair algorithm in the complete data domain where there are no missing values.
A general kernel boosting framework integrating pathways for predictive modeling based on genomic data
Predictive modeling based on genomic data has gained popularity in biomedical research and clinical practice by allowing researchers and clinicians to identify biomarkers and tailor treatment decisions more efficiently.
