Apr 14, 2023 · We study dynamic algorithms robust to adaptive input generated from sources with bounded capabilities, such as sparsity or limited interaction.
Feb 1, 2023 · We give algorithms robust to adaptive input from adversaries with bounded capabilities and a general framework for achieving it.
We study dynamic algorithms robust to adaptive input generated from sources with bounded capabilities, such as sparsity or limited interaction.
Apr 14, 2023 · Abstract. We study dynamic algorithms robust to adaptive input generated from sources with bounded capabilities, such as sparsity or limited ...
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We give a general reduction transforming a dynamic algorithm against an oblivious adversary to a dynamic algorithm robust against an adaptive adversary.
Abstract. Embeddings of graphs into distributions of trees that preserve distances in expectation are a cornerstone of many optimization algorithms.
Such an algorithm is useful for adaptive inputs, as the failure probability is low enough to union bound over all computation paths. Our result implies that the ...
In these settings, the adaptive adversaries can change the input distribution in response to the previous behavior of the algorithm, while oblivious adversaries ...
This motivated a growing interest in understanding the performance of algorithms when the inputs are chosen adaptively, possibly as a function of their previous ...
We introduce a topological property known as r-robustness of a graph, and show that this property provides improved bounds on tolerating malicious behavior, in ...