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Found 12 papers, 2 papers with code
Two Simple Ways to Learn Individual Fairness Metric from Data
Individual fairness was proposed to address some of the shortcomings of group fairness.
Trade-off Between Dependence and Complexity for Nonparametric Learning -- an Empirical Process Approach
Our main result shows that a non-trivial trade-off between the complexity of the underlying function class and the dependence among the observations characterizes the learning rate in a large class of nonparametric problems.
UTOPIA: Universally Trainable Optimal Prediction Intervals Aggregation
Uncertainty quantification for prediction is an intriguing problem with significant applications in various fields, such as biomedical science, economic studies, and weather forecasts.
Deep Neural Networks for Nonparametric Interaction Models with Diverging Dimension
We show that under certain standard assumptions, debiased deep neural networks achieve a minimax optimal rate both in terms of $(n, d)$.
Predictor-corrector algorithms for stochastic optimization under gradual distribution shift
Time-varying stochastic optimization problems frequently arise in machine learning practice (e. g. gradual domain shift, object tracking, strategic classification).
Domain Adaptation meets Individual Fairness. And they get along
In this paper, we leverage this connection between algorithmic fairness and distribution shifts to show that algorithmic fairness interventions can help ML models overcome distribution shifts, and that domain adaptation methods (for overcoming distribution shifts) can mitigate algorithmic biases.
Post-processing for Individual Fairness
In this work, we propose general post-processing algorithms for individual fairness (IF).
Estimation of a score-explained non-randomized treatment effect in fixed and high dimensions
In this paper, we present a new model coined SCENTS: Score Explained Non-Randomized Treatment Systems, and a corresponding method that allows estimation of the treatment effect at $\sqrt{n}$ rate in the presence of fairly general forms of confoundedness, when the `score' variable on whose basis treatment is assigned can be explained via certain feature measurements of the individuals under study.
Methodology Statistics Theory Statistics Theory
Outlier Robust Optimal Transport
In light of recent advances in solving the OT problem, OT distances are widely used as loss functions in minimum distance estimation.
Does enforcing fairness mitigate biases caused by subpopulation shift?
Many instances of algorithmic bias are caused by subpopulation shifts.
There is no trade-off: enforcing fairness can improve accuracy
If the algorithmic biases in an ML model are due to sampling biases in the training data, then enforcing algorithmic fairness may improve the performance of the ML model on unbiased test data.
Two Simple Ways to Learn Individual Fairness Metrics from Data
Individual fairness is an intuitive definition of algorithmic fairness that addresses some of the drawbacks of group fairness.
