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The paper Fair and Unbiased Algorithmic Decision Making: Current State and Future Challenges argues that ensuring fairness is not a trivial task and that the current statistical formalizations of fairness lead to a long list of criteria that are each flawed (or even harmful) in different contexts, that is, there are trade-offs between the proposed ...


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When you say likelihood, you are invoking several other concepts like events, sample, parameters, model, probability density function (PDF), etc (it would be helpful if you learn more about these concepts). In essence, a likelihood function $l(x|\theta)$ is a PDF that quantifies how likely is that event $x$ happens out of a set of possible events, given the ...


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The first place I would have directed you would be Sklearn and pydiffmap. I found this paper specifically about the problem you are doing using python the reference a package called megaman It seems like an active Github . I suggest not just looking at manifold learning papers but leaning towards a search toward non linear embedding or non linear ...


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