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Found 8 papers, 0 papers with code
Trading off Consistency and Dimensionality of Convex Surrogates for the Mode
We investigate ways to trade off surrogate loss dimension, the number of problem instances, and restricting the region of consistency in the simplex for multiclass classification.
Using Property Elicitation to Understand the Impacts of Fairness Regularizers
Predictive algorithms are often trained by optimizing some loss function, to which regularization functions are added to impose a penalty for violating constraints.
Consistent Polyhedral Surrogates for Top-$k$ Classification and Variants
For the proposed hinge-like surrogates that are convex (i. e., polyhedral), we apply the recent embedding framework of Finocchiaro et al. (2019; 2022) to determine the prediction problem for which the surrogate is consistent.
An Embedding Framework for the Design and Analysis of Consistent Polyhedral Surrogates
Using these results, we establish that indirect elicitation, a necessary condition for consistency, is also sufficient when working with polyhedral surrogates.
The Structured Abstain Problem and the Lovász Hinge
The Lov\'asz hinge is a convex surrogate recently proposed for structured binary classification, in which $k$ binary predictions are made simultaneously and the error is judged by a submodular set function.
Unifying Lower Bounds on Prediction Dimension of Consistent Convex Surrogates
Given a prediction task, understanding when one can and cannot design a consistent convex surrogate loss, particularly a low-dimensional one, is an important and active area of machine learning research.
Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness
Decision-making systems increasingly orchestrate our world: how to intervene on the algorithmic components to build fair and equitable systems is therefore a question of utmost importance; one that is substantially complicated by the context-dependent nature of fairness and discrimination.
An Embedding Framework for Consistent Polyhedral Surrogates
Conversely, we show how to construct a consistent polyhedral surrogate for any given discrete loss.
