1 citations · 2 across the 30 of their papers we have counts for
13 papers · 1 filter
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…
Information-Computation Tradeoffs for Noiseless Linear Regression with Oblivious Contamination
Ilias Diakonikolas, Chao Gao, Daniel M. Kane +2
We study the task of noiseless linear regression under Gaussian covariates in the presence of additive oblivious contamination. Specifically, we are given i.i.d.\ samples from a di…
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…
Linear Regression under Missing or Corrupted Coordinates
Ilias Diakonikolas, Jelena Diakonikolas, Daniel M. Kane +2
We study multivariate linear regression under Gaussian covariates in two settings, where data may be erased or corrupted by an adversary under a coordinate-wise budget. In the inco…
Replicable Distribution Testing
Ilias Diakonikolas, Jingyi Gao, Daniel Kane +2
We initiate a systematic investigation of distribution testing in the framework of algorithmic replicability. Specifically, given independent samples from a collection of probabili…
On Fine-Grained Distinct Element Estimation
Ilias Diakonikolas, Daniel M. Kane, Jasper C. H. Lee +3
We study the problem of distributed distinct element estimation, where servers each receive a subset of a universe and aim to compute a -approximation to…
Algorithms and SQ Lower Bounds for Robustly Learning Real-valued Multi-index Models
Ilias Diakonikolas, Giannis Iakovidis, Daniel M. Kane +1
We study the complexity of learning real-valued Multi-Index Models (MIMs) under the Gaussian distribution. A -MIM is a function that depends only…
On Learning Parallel Pancakes with Mostly Uniform Weights
Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar +2
We study the complexity of learning -mixtures of Gaussians (-GMMs) on . This task is known to have complexity in full generality. To circumvent this…
Faster Algorithms for Agnostically Learning Disjunctions and their Implications
Ilias Diakonikolas, Daniel M. Kane, Lisheng Ren
We study the algorithmic task of learning Boolean disjunctions in the distribution-free agnostic PAC model. The best known agnostic learner for the class of disjunctions over $\{0,…
Batch List-Decodable Linear Regression via Higher Moments
Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar +2
We study the task of list-decodable linear regression using batches. A batch is called clean if it consists of i.i.d. samples from an unknown linear regression distribution. For a…
Efficient Multivariate Robust Mean Estimation Under Mean-Shift Contamination
Ilias Diakonikolas, Giannis Iakovidis, Daniel M. Kane +1
We study the algorithmic problem of robust mean estimation of an identity covariance Gaussian in the presence of mean-shift contamination. In this contamination model, we are given…
Robust Learning of Multi-index Models via Iterative Subspace Approximation
Ilias Diakonikolas, Giannis Iakovidis, Daniel M. Kane +1
We study the task of learning Multi-Index Models (MIMs) with label noise under the Gaussian distribution. A -MIM is any function that only depends on a -dimensional subsp…
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 …