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Found 9 papers, 2 papers with code
Group-wise oracle-efficient algorithms for online multi-group learning
We study the problem of online multi-group learning, a learning model in which an online learner must simultaneously achieve small prediction regret on a large collection of (possibly overlapping) subsequences corresponding to a family of groups.
Multi-group Learning for Hierarchical Groups
The multi-group learning model formalizes the learning scenario in which a single predictor must generalize well on multiple, possibly overlapping subgroups of interest.
Group conditional validity via multi-group learning
We consider the problem of distribution-free conformal prediction and the criterion of group conditional validity.
Learning Tensor Representations for Meta-Learning
Prior works on learning linear representations for meta-learning assume that there is a common shared representation across different tasks, and do not consider the additional task-specific observable side information.
Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics
We find that biased predictions are mostly caused by biased training data.
Is Private Learning Possible with Instance Encoding?
A private machine learning algorithm hides as much as possible about its training data while still preserving accuracy.
Ensuring Fairness Beyond the Training Data
In this work, we develop classifiers that are fair not only with respect to the training distribution, but also for a class of distributions that are weighted perturbations of the training samples.
A Separation Result Between Data-oblivious and Data-aware Poisoning Attacks
Some of the stronger poisoning attacks require the full knowledge of the training data.
Methodological Blind Spots in Machine Learning Fairness: Lessons from the Philosophy of Science and Computer Science
In the ML fairness literature, there have been few investigations through the viewpoint of philosophy, a lens that encourages the critical evaluation of basic assumptions.
