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Found 4 papers, 1 papers with code
Predicting on the Edge: Identifying Where a Larger Model Does Better
Much effort has been devoted to making large and more accurate models, but relatively little has been put into understanding which examples are benefiting from the added complexity.
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.
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.
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.
