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Found 8 papers, 1 papers with code
Induced Model Matching: How Restricted Models Can Help Larger Ones
We consider scenarios where a very accurate predictive model using restricted features is available at the time of training of a larger, full-featured, model.
Fair Learning with Private Demographic Data
Sensitive attributes such as race are rarely available to learners in real world settings as their collection is often restricted by laws and regulations.
From Fair Decision Making to Social Equality
In this paper, we propose a simple yet revealing model that encompasses (1) a selection process where an institution chooses from multiple groups according to their qualifications so as to maximize an institutional utility and (2) dynamics that govern the evolution of the groups' qualifications according to the imposed policies.
The power of absolute discounting: all-dimensional distribution estimation
Minimax optimality is too pessimistic to remedy this issue.
Learning Non-Discriminatory Predictors
We consider learning a predictor which is non-discriminatory with respect to a "protected attribute" according to the notion of "equalized odds" proposed by Hardt et al. [2016].
Near-Optimal Smoothing of Structured Conditional Probability Matrices
Utilizing the structure of a probabilistic model can significantly increase its learning speed.
Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning
Using k-means clustering as a prototypical unsupervised learning problem, we show how we can strategically summarize the data (control space) in order to trade off risk and time when data is generated by a probabilistic model.
On the Impossibility of Learning the Missing Mass
The probability of this event is referred to as the "missing mass".
