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

Sorting is a fundamental problem in computer science. In the classical setting, it is well-
known that (1±o (1)) n log2 n comparisons are both necessary and sufficient to sort a list of n …

With the explosion of massive, widely available unlabeled data in the past years, finding
label and time efficient, robust learning algorithms has become ever more important in …

Metric based comparison operations such as finding maximum, nearest and farthest
neighbor are fundamental to studying various clustering techniques such as $ k $-center …

We present comparison-based parallel algorithms for sorting n comparable items subject to
comparison errors. We consider errors to occur according to a well-studied framework …

The task of finding a user's utility function (representing the user's preference) by asking
them to compare pairs of points through a series of questions, each requiring him/her to …

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 …

We provide and study several algorithms for sorting an array of n comparable distinct
elements subject to probabilistic comparison errors. In this model, the comparison of two …

We provide several algorithms for sorting an array of n comparable distinct elements subject
to probabilistic comparison errors in external memory. In this model, which has been …

We consider the problem of inferring an unknown ranking of $ n $ items from a random
tournament on $ n $ vertices whose edge directions are correlated with the ranking. We …