We explore the fundamental problem of sorting through the lens of learning-augmented
algorithms, where algorithms can leverage possibly erroneous predictions to improve their …

We consider the problem of computing a function of n variables using noisy queries, where
each query is incorrect with some fixed and known probability. Specifically, we consider the …

Sorting is the task of ordering n elements using pairwise comparisons. It is well known that
comparisons are both necessary and sufficient when the outcomes of the comparisons are …

Typical black-box optimization approaches in robotics focus on learning from metric scores.
However, that is not always possible, as not all developers have ground truth available …

Let $\mathsf {TH} _k $ denote the $ k $-out-of-$ n $ threshold function: given $ n $ input
Boolean variables, the output is $1 $ if and only if at least $ k $ of the inputs are $1 $. We …

We revisit the noisy binary search model of Karp and Kleinberg, in which we have $ n $
coins with unknown probabilities $ p_i $ that we can flip. The coins are sorted by increasing …

In this work, we study the problem of sorting n elements with pairwise comparisons under
the presence of observation noise. We consider variable-length algorithms with a random …

This work considers the problem of the noisy binary search in a sorted array. The noise is
modeled by a parameter $ p $ that dictates that a comparison can be incorrect with …

We revisit the problem of computing with noisy information considered in Feige et al.[1],
which includes computing the OR function from noisy queries, and computing the MAX …

We study the problem of detecting or recovering a planted ranked subgraph from a directed
graph, an analog for directed graphs of the well-studied planted dense subgraph model. We …