From the 2 of 25 linked papers with an AI index.
25 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…
Efficiently Learning Drifting Halfspaces with Massart Noise
Mingchen Ma, Guyang Cao, Jelena Diakonikolas +1
We study the problem of learning a drifting concept in the presence of Massart noise. In this framework, an online learner has access to a history of independent samples whose labe…
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…
Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals
Ilias Diakonikolas, Giannis Iakovidis, Mingchen Ma
We study the task of agnostic learning of multiclass linear classifiers under the Gaussian distribution. Given labeled examples from a distribution over $\mathbb{R}^d \tim…
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…