Search Results for author:
Found 11 papers, 0 papers with code
Robust Sparse Estimation for Gaussians with Optimal Error under Huber Contamination
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 $O(\epsilon)$.
Statistical Query Lower Bounds for Learning Truncated Gaussians
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.
Clustering Mixtures of Bounded Covariance Distributions Under Optimal Separation
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.
Near-Optimal Algorithms for Gaussians with Huber Contamination: Mean Estimation and Linear Regression
We study the fundamental problems of Gaussian mean estimation and linear regression with Gaussian covariates in the presence of Huber contamination.
SQ Lower Bounds for Learning Bounded Covariance GMMs
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.
Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA
Our main contribution is to develop a nearly-linear time algorithm for robust PCA with near-optimal error guarantees.
List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering
We study the problem of list-decodable sparse mean estimation.
Robust Sparse Mean Estimation via Sum of Squares
In this work, we develop the first efficient algorithms for robust sparse mean estimation without a priori knowledge of the covariance.
Streaming Algorithms for High-Dimensional Robust Statistics
In this work, we develop the first efficient streaming algorithms for high-dimensional robust statistics with near-optimal memory requirements (up to logarithmic factors).
Statistical Query Lower Bounds for List-Decodable Linear Regression
We study the problem of list-decodable linear regression, where an adversary can corrupt a majority of the examples.
The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals
We study the problem of agnostic learning under the Gaussian distribution.
