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Found 11 papers, 4 papers with code
On the Actionability of Outcome Prediction
Through a simple model encompassing actions, latent states, and measurements, we demonstrate that pure outcome prediction rarely results in the most effective policy for taking actions, even when combined with other measurements.
Lost in Translation: Reimagining the Machine Learning Life Cycle in Education
We find that a cross-disciplinary gap exists and is particularly salient in two parts of the ML life cycle: the formulation of an ML problem from education goals and the translation of predictions to interventions.
Strategic Ranking
Strategic classification studies the design of a classifier robust to the manipulation of input by strategic individuals.
Bandit Learning in Decentralized Matching Markets
We study two-sided matching markets in which one side of the market (the players) does not have a priori knowledge about its preferences for the other side (the arms) and is required to learn its preferences from experience.
Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning
Our theoretical results characterize the optimal strategies in this class, bound the Pareto errors due to inaccuracies in the scores, and show an equivalence between optimal strategies and a rich class of fairness-constrained profit-maximizing policies.
Competing Bandits in Matching Markets
Stable matching, a classical model for two-sided markets, has long been studied with little consideration for how each side's preferences are learned.
Steerable $e$PCA: Rotationally Invariant Exponential Family PCA
The second is steerable PCA, a fast and accurate procedure for including all planar rotations for PCA.
The implicit fairness criterion of unconstrained learning
We show that under reasonable conditions, the deviation from satisfying group calibration is upper bounded by the excess risk of the learned score relative to the Bayes optimal score function.
On the Local Minima of the Empirical Risk
Our objective is to find the $\epsilon$-approximate local minima of the underlying function $F$ while avoiding the shallow local minima---arising because of the tolerance $\nu$---which exist only in $f$.
Delayed Impact of Fair Machine Learning
Fairness in machine learning has predominantly been studied in static classification settings without concern for how decisions change the underlying population over time.
$e$PCA: High Dimensional Exponential Family PCA
We develop $e$PCA (exponential family PCA), a new methodology for PCA on exponential family distributions.
Methodology
