From the 2 of 21 linked papers with an AI index.
21 papers
High-Dimensional Gaussian Mean Estimation under Realizable Contamination
Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas
The paper investigates estimating the mean of a high‑dimensional Gaussian when each sample may be missing with a bounded, data‑dependent probability (realizable ε‑contamination), p…
Linear Regression under Missing or Corrupted Coordinates
Ilias Diakonikolas, Jelena Diakonikolas, Daniel M. Kane +2
The paper analyzes multivariate linear regression with Gaussian features when an adversary can delete or corrupt a fraction of entries per coordinate, providing tight error bounds…
Robust Regression of General ReLUs with Queries
Ilias Diakonikolas, Daniel M. Kane, Mingchen Ma
We study the task of agnostically learning general (as opposed to homogeneous) ReLUs under the Gaussian distribution with respect to the squared loss. In the passive learning setti…
Agnostic Product Mixed State Tomography via Robust Statistics
Alvan Arulandu, Ilias Diakonikolas, Daniel Kane +1
We study the complexity of two closely related learning problems, one quantum and one classical. In the quantum setting, we consider agnostic tomography for the natural class of pr…
Robust Regression with Adaptive Contamination in Response: Optimal Rates and Computational Barriers
Ilias Diakonikolas, Chao Gao, Daniel M. Kane +2
We study robust regression under a contamination model in which covariates are clean while the responses may be corrupted in an adaptive manner. Unlike the classical Huber's contam…
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…