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Found 5 papers, 1 papers with code
Fast Fair Regression via Efficient Approximations of Mutual Information
Most work in algorithmic fairness to date has focused on discrete outcomes, such as deciding whether to grant someone a loan or not.
The Crossover Process: Learnability and Data Protection from Inference Attacks
It is usual to consider data protection and learnability as conflicting objectives.
Fast Learning from Distributed Datasets without Entity Matching
Our goal is to learn a classifier in the cross product space of the two domains, in the hard case in which no shared ID is available -- e. g. due to anonymization.
(Almost) No Label No Cry
In Learning with Label Proportions (LLP), the objective is to learn a supervised classifier when, instead of labels, only label proportions for bags of observations are known.
A Hybrid Loss for Multiclass and Structured Prediction
We propose a novel hybrid loss for multiclass and structured prediction problems that is a convex combination of a log loss for Conditional Random Fields (CRFs) and a multiclass hinge loss for Support Vector Machines (SVMs).
