5 papers · 1 filter
Testable Learning of General Halfspaces under Massart Noise
Ilias Diakonikolas, Giannis Iakovidis, Daniel M. Kane +1
We study the algorithmic task of testably learning general Massart halfspaces under the Gaussian distribution. In the testable learning setting, the aim is the design of a tester-l…
PTF Testing Lower Bounds for Non-Gaussian Component Analysis
Ilias Diakonikolas, Daniel M. Kane, Sihan Liu +1
This work studies information-computation gaps for statistical problems. A common approach for providing evidence of such gaps is to show sample complexity lower bounds (that are s…
Entangled Mean Estimation in High-Dimensions
Ilias Diakonikolas, Daniel M. Kane, Sihan Liu +1
We study the task of high-dimensional entangled mean estimation in the subset-of-signals model. Specifically, given independent random points in …
Super Non-singular Decompositions of Polynomials and their Application to Robustly Learning Low-degree PTFs
Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis +2
We study the efficient learnability of low-degree polynomial threshold functions (PTFs) in the presence of a constant fraction of adversarial corruptions. Our main algorithmic resu…
Testing Closeness of Multivariate Distributions via Ramsey Theory
Ilias Diakonikolas, Daniel M. Kane, Sihan Liu
We investigate the statistical task of closeness (or equivalence) testing for multidimensional distributions. Specifically, given sample access to two unknown distributions $\mathb…