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Adaptive Robust Confidence Sets in High Dimensions
Ilias Diakonikolas, Chao Gao, Daniel M. Kane +1
We study adaptive confidence sets for mean vectors of multivariate Gaussians with identity covariance under Huber contamination when the contamination proportion is unknown. We sho…
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…
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…
High-Dimensional Gaussian Mean Estimation under Realizable Contamination
Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas
We study mean estimation for a Gaussian distribution with identity covariance in under a missing data scheme termed realizable -contamination model. In this model…
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…
Sample Complexity Bounds for Robust Mean Estimation with Mean-Shift Contamination
Ilias Diakonikolas, Giannis Iakovidis, Daniel M. Kane +1
We study the basic task of mean estimation in the presence of mean-shift contamination. In the mean-shift contamination model, an adversary is allowed to replace a small constant f…
Statistical Query Lower Bounds for Smoothed Agnostic Learning
Ilias Diakonikolas, Daniel M. Kane
We study the complexity of smoothed agnostic learning, recently introduced by~\cite{CKKMS24}, in which the learner competes with the best classifier in a target class under slight…