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Found 6 papers, 3 papers with code
Performative Prediction with Bandit Feedback: Learning through Reparameterization
Specifically, we develop a {\em reparameterization} framework that reparametrizes the performative prediction objective as a function of the induced data distribution.
Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors
The pursuit of long-term fairness involves the interplay between decision-making and the underlying data generating process.
Metric-Fair Classifier Derandomization
We study the problem of classifier derandomization in machine learning: given a stochastic binary classifier $f: X \to [0, 1]$, sample a deterministic classifier $\hat{f}: X \to \{0, 1\}$ that approximates the output of $f$ in aggregate over any data distribution.
Fairness Transferability Subject to Bounded Distribution Shift
Given an algorithmic predictor that is "fair" on some source distribution, will it still be fair on an unknown target distribution that differs from the source within some bound?
Model Transferability With Responsive Decision Subjects
We provide both upper bounds for the performance gap due to the induced domain shift, as well as lower bounds for the trade-offs that a classifier has to suffer on either the source training distribution or the induced target distribution.
Linear Classifiers that Encourage Constructive Adaptation
Machine learning systems are often used in settings where individuals adapt their features to obtain a desired outcome.
