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Found 8 papers, 3 papers with code
Uniform Convergence of Rank-weighted Learning
In this work, we study a novel notion of L-Risk based on the classical idea of rank-weighted learning.
Uniform Convergence of Rank-weighted Learning
In this work, we study a novel notion of L-Risk based on the classical idea of rank-weighted learning.
Optimal Binary Classification Beyond Accuracy
The vast majority of statistical theory on binary classification characterizes performance in terms of accuracy.
Class-Weighted Classification: Trade-offs and Robust Approaches
We define a robust risk that minimizes risk over a set of weightings and show excess risk bounds for this problem.
Multiclass Classification via Class-Weighted Nearest Neighbors
We study statistical properties of the k-nearest neighbors algorithm for multiclass classification, with a focus on settings where the number of classes may be large and/or classes may be highly imbalanced.
Adversarial Risk Bounds via Function Transformation
We derive bounds for a notion of adversarial risk, designed to characterize the robustness of linear and neural network classifiers to adversarial perturbations.
Adversarial Influence Maximization
We consider the problem of influence maximization in fixed networks for contagion models in an adversarial setting.
Confidence Sets for the Source of a Diffusion in Regular Trees
At the core of our proofs is a probabilistic analysis of P\'{o}lya urns corresponding to the number of uninfected neighbors in specific subtrees of the infection tree.
