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most citedImproved Bounds on Quantum Learning Algorithms

72 citations · 262 across the 30 of their papers we have counts for

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Showing 2019Show all

6 papers · 1 filter

cs.CC2019

Testing noisy linear functions for sparsity

Xue Chen, Anindya De, Rocco A. Servedio

We consider the following basic inference problem: there is an unknown high-dimensional vector , and an algorithm is given access to labeled pairs where…

cs.CC20191 cited

Kruskal-Katona for convex sets, with applications

Anindya De, Rocco A. Servedio

The well-known Kruskal-Katona theorem in combinatorics says that (under mild conditions) every monotone Boolean function has a nontrivial "density increm…

cs.DS2019

A Lower Bound on Cycle-Finding in Sparse Digraphs

Xi Chen, Tim Randolph, Rocco A. Servedio +1

We consider the problem of finding a cycle in a sparse directed graph that is promised to be far from acyclic, meaning that the smallest feedback arc set in is large. We pr…

cs.DS20194 cited

Efficient average-case population recovery in the presence of insertions and deletions

Frank Ban, Xi Chen, Rocco A. Servedio +1

Several recent works have considered the \emph{trace reconstruction problem}, in which an unknown source string is transmitted through a probabilistic channel which…

cs.DS2019

Learning from satisfying assignments under continuous distributions

Clément L. Canonne, Anindya De, Rocco A. Servedio

What kinds of functions are learnable from their satisfying assignments? Motivated by this simple question, we extend the framework of De, Diakonikolas, and Servedio [DDS15], which…

cs.DS2019

Beyond trace reconstruction: Population recovery from the deletion channel

Frank Ban, Xi Chen, Adam Freilich +2

\emph{Population recovery} is the problem of learning an unknown distribution over an unknown set of -bit strings, given access to independent draws from the distribution that h…