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Found 8 papers, 2 papers with code
Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling
In this work we consider the problem of numerical integration, i. e., approximating integrals with respect to a target probability measure using only pointwise evaluations of the integrand.
Fair learning with Wasserstein barycenters for non-decomposable performance measures
In the awareness framework, akin to the classical unconstrained classification case, we show that maximizing accuracy under this fairness constraint is equivalent to solving a corresponding regression problem followed by thresholding at level $1/2$.
Nyström Kernel Mean Embeddings
Our main result is an upper bound on the approximation error of this procedure.
Classification with abstention but without disparities
Building on this result, we propose a post-processing classification algorithm, which is able to modify any off-the-shelf score-based classifier using only unlabeled sample.
An example of prediction which complies with Demographic Parity and equalizes group-wise risks in the context of regression
We provide a non-trivial example of a prediction $x \to f(x)$ which satisfies two common group-fairness notions: Demographic Parity \begin{align} (f(X) | S = 1) &\stackrel{d}{=} (f(X) | S = 2) \end{align} and Equal Group-Wise Risks \begin{align} \mathbb{E}[(f^*(X) - f(X))^2 | S = 1] = \mathbb{E}[(f^*(X) - f(X))^2 | S = 2].
Statistical guarantees for generative models without domination
In this paper, we introduce a convenient framework for studying (adversarial) generative models from a statistical perspective.
Bounding the expectation of the supremum of empirical processes indexed by Hölder classes
In this note, we provide upper bounds on the expectation of the supremum of empirical processes indexed by H\"older classes of any smoothness and for any distribution supported on a bounded set in $\mathbb R^d$.
A nonasymptotic law of iterated logarithm for general M-estimators
M-estimators are ubiquitous in machine learning and statistical learning theory.
