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Found 8 papers, 1 papers with code
Optimal algorithms for group distributionally robust optimization and beyond
Distributionally robust optimization (DRO) can improve the robustness and fairness of learning methods.
Improved Algorithms for Online Submodular Maximization via First-order Regret Bounds
- For monotone submodular maximization subject to a matroid, we give an efficient algorithm which achieves a (1 − c/e − ε)-regret of O(√kT ln(n/k)) where n is the size of the ground set, k is the rank of the matroid, ε > 0 is a constant, and c is the average curvature.
Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits
We propose novel algorithms with first- and second-order regret bounds for adversarial linear bandits.
Statistical Learning with Conditional Value at Risk
For convex and Lipschitz loss functions, we show that our algorithm has $O(1/\sqrt{n})$-convergence to the optimal CVaR, where $n$ is the number of samples.
Feature Attribution As Feature Selection
In this study, we formalize the feature attribution problem as a feature selection problem.
Fast greedy algorithms for dictionary selection with generalized sparsity constraints
In dictionary selection, several atoms are selected from finite candidates that successfully approximate given data points in the sparse representation.
Maximally Invariant Data Perturbation as Explanation
In adversarial example, one seeks the smallest data perturbation that changes the model's output.
A Generalization of Submodular Cover via the Diminishing Return Property on the Integer Lattice
We show that the generalized submodular cover problem can be applied to various problems and devise a bicriteria approximation algorithm.
