Robust Mean Estimation

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10 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Adaptive Robust Confidence Sets in High Dimensions
Oct 11, 2026Ilias Diakonikolas, Chao Gao, Daniel M. Kane +1Robust Mean Estimation Low-Degree Polynomial Method
University of Wisconsin-Madison · University of Chicago · University of California, San Diego +1
Median-of-Means as an Extremal Convex Estimator and a Nonconvex Route to the Trimmed Oracle
Sep 1, 2026Angshul MajumdarNonconvex Optimization Robust Mean Estimation
Department of Electronics and Communications Engineering, IIIT Delhi, India
Robust Estimation of Sparse Numerical Vectors under Local Differential Privacy
Jul 30, 2026Puning Zhao, Zhikun Zhang, Shaowei Wang +5Sparse Recovery Robust Mean Estimation
Shenzhen Campus of Sun Yat-sen University · Zhejiang University · Guangzhou University +2
HOMER: Huber-of-Means for Efficient and Robust Estimation in Hilbert Spaces
Jul 29, 2026Kisung You, Boram ChoRobust Mean Estimation Heavy-Tailed Distributions
Baruch College · Yale University
RoPoLL: Robust Panel of LLM Judges
Jun 29, 2026Anish Acharya, Kris W Pan, Brian VerkhovskyLLM Evaluation LLM-as-a-Judge
Amazon Web Services
Computationally tractable robust differentially private mean estimation
Jun 10, 2026Kelly RamsayRobust Mean Estimation Differential Privacy
Department of Mathematics and Statistics York University Toronto, ON, Canada
Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications
May 24, 2026Samuel B. Hopkins, Stefan TiegelRobust Regression Robust Mean Estimation
MIT
Understanding and Mitigating Dataset Corruption in LLM Steering
Mar 3, 2026Cullen Anderson, Narmeen Oozeer, Foad Namjoo +3Robust Mean Estimation Language Model Steering
Department of Computer Science, University of Massachusetts Amherst · Martian AI · Kahlert School of Computing, University of Utah +1
Sample Complexity Bounds for Robust Mean Estimation with Mean-Shift Contamination
Feb 25, 2026Ilias Diakonikolas, Giannis Iakovidis, Daniel M. Kane +1Robust Mean Estimation Robust Estimation
UW Madison · UC San Diego
High-Dimensional Robust Mean Estimation with Untrusted Batches
Feb 24, 2026Maryam Aliakbarpour, Vladimir Braverman, Yuhan Liu +1Robust Mean Estimation Robust Estimation
Department of Computer Science, Ken Kennedy Institute, Rice University. · Johns Hopkins University. · Rice University.

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Adaptive Robust Confidence Sets in High Dimensions

Ilias Diakonikolas, Chao Gao, Daniel M. Kane, Yuetian Luo

University of Wisconsin-Madison · University of Chicago · University of California, San Diego+1

Robust Mean Estimation Low-Degree Polynomial Method Confidence Region Estimation Data Contamination Statistical Learning Theory

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We study adaptive confidence sets for mean vectors of multivariate Gaussians with identity covariance under Huber contamination when the contamination proportion is unknown. We show that an information-theoretically optimal adaptive confidence set can be constructed by simultaneously quantifying uncertainty across all one-dimensional projections. The resulting confidence set achieves the optimal diameter and reveals a substantial adaptation cost compared with the minimax estimation error. Although statistically optimal, this construction is computationally infeasible. We establish a Statistical Query (SQ) lower bound showing that achieving diameter

O(m^{-1/2})

requires either exponentially many queries or query tolerance

d^{-Ω(m)}

. Thus, in contrast to robust mean estimation, adaptation to the unknown contamination proportion incurs both a statistical cost and a computational barrier within the SQ framework. Finally, we give a low-degree moment-matching algorithm that constructs an adaptive confidence set using

d^{O(m)}

samples and time. Its diameter bound contains an additional

O(m^{-1/2})

term beyond the information-theoretic rate, matching the dependence on

m

in the SQ lower bound. Together, these results characterize the statistical limits of adaptive robust confidence sets under Huber contamination and quantify a computational barrier within the SQ framework.