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Found 15 papers, 5 papers with code
Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry
From the social sciences to machine learning, it has been well documented that metrics to be optimized are not always aligned with social welfare.
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.
Robust Distillation for Worst-class Performance
We show empirically that our robust distillation techniques not only achieve better worst-class performance, but also lead to Pareto improvement in the tradeoff between overall performance and worst-class performance compared to other baseline methods.
Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence
We thus propose a novel combination of optimization and sampling techniques for approximate Bayesian inference by constructing an IS proposal distribution through the minimization of a forward KL (FKL) divergence.
Multi-Source Causal Inference Using Control Variates
Across simulations and two case studies with real data, we show that this control variate can significantly reduce the variance of the ATE estimate.
Regularization Strategies for Quantile Regression
We show that minimizing an expected pinball loss over a continuous distribution of quantiles is a good regularizer even when only predicting a specific quantile.
Approximate Heavily-Constrained Learning with Lagrange Multiplier Models
In machine learning applications such as ranking fairness or fairness over intersectional groups, one often encounters optimization problems with an extremely large number of constraints.
Robust Optimization for Fairness with Noisy Protected Groups
Second, we introduce two new approaches using robust optimization that, unlike the naive approach of only relying on $\hat{G}$, are guaranteed to satisfy fairness criteria on the true protected groups G while minimizing a training objective.
Deontological Ethics By Monotonicity Shape Constraints
We demonstrate how easy it is for modern machine-learned systems to violate common deontological ethical principles and social norms such as "favor the less fortunate," and "do not penalize good attributes."
Pairwise Fairness for Ranking and Regression
We present pairwise fairness metrics for ranking models and regression models that form analogues of statistical fairness notions such as equal opportunity, equal accuracy, and statistical parity.
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals
This new formulation leads to an algorithm that produces a stochastic classifier by playing a two-player non-zero-sum game solving for what we call a semi-coarse correlated equilibrium, which in turn corresponds to an approximately optimal and feasible solution to the constrained optimization problem.
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints
Classifiers can be trained with data-dependent constraints to satisfy fairness goals, reduce churn, achieve a targeted false positive rate, or other policy goals.
Quit When You Can: Efficient Evaluation of Ensembles with Ordering Optimization
Given a classifier ensemble and a set of examples to be classified, many examples may be confidently and accurately classified after only a subset of the base models in the ensemble are evaluated.
Proxy Fairness
We consider the problem of improving fairness when one lacks access to a dataset labeled with protected groups, making it difficult to take advantage of strategies that can improve fairness but require protected group labels, either at training or runtime.
Interpretable Set Functions
We propose learning flexible but interpretable functions that aggregate a variable-length set of permutation-invariant feature vectors to predict a label.
