no code implementations • 15 Mar 2024 • Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas
Concretely, for Gaussian robust $k$-sparse mean estimation on $\mathbb{R}^d$ with corruption rate $\epsilon>0$, our algorithm has sample complexity $(k^2/\epsilon^2)\mathrm{polylog}(d/\epsilon)$, runs in sample polynomial time, and approximates the target mean within $\ell_2$-error $O(\epsilon)$.
no code implementations • 4 Mar 2024 • Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis
We study the problem of estimating the mean of an identity covariance Gaussian in the truncated setting, in the regime when the truncation set comes from a low-complexity family $\mathcal{C}$ of sets.
no code implementations • 19 Dec 2023 • Ilias Diakonikolas, Daniel M. Kane, Jasper C. H. Lee, Thanasis Pittas
Furthermore, under a variant of the "no large sub-cluster'' condition from in prior work [BKK22], we show that our algorithm outputs an accurate clustering, not just a refinement, even for general-weight mixtures.
no code implementations • NeurIPS 2023 • Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas
We study the fundamental problems of Gaussian mean estimation and linear regression with Gaussian covariates in the presence of Huber contamination.
no code implementations • 22 Jun 2023 • Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis
In the special case where the separation is on the order of $k^{1/2}$, we additionally obtain fine-grained SQ lower bounds with the correct exponent.
no code implementations • 4 May 2023 • Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas
Our main contribution is to develop a nearly-linear time algorithm for robust PCA with near-optimal error guarantees.
no code implementations • 10 Jun 2022 • Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas
We study the problem of list-decodable sparse mean estimation.
no code implementations • 7 Jun 2022 • Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas
In this work, we develop the first efficient algorithms for robust sparse mean estimation without a priori knowledge of the covariance.
no code implementations • 26 Apr 2022 • Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas
In this work, we develop the first efficient streaming algorithms for high-dimensional robust statistics with near-optimal memory requirements (up to logarithmic factors).
no code implementations • NeurIPS 2021 • Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas, Alistair Stewart
We study the problem of list-decodable linear regression, where an adversary can corrupt a majority of the examples.
no code implementations • 8 Feb 2021 • Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis
We study the problem of agnostic learning under the Gaussian distribution.