3k citations · 24.2k across the 299 of their papers we have counts for
5 papers · 2 filters
Optimal Mean Estimation without a Variance
Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett +1
We study the problem of heavy-tailed mean estimation in settings where the variance of the data-generating distribution does not exist. Concretely, given a sample $\mathbf{X} = \{X…
Post-Selection Inference via Algorithmic Stability
Tijana Zrnic, Michael I. Jordan
When the target of statistical inference is chosen in a data-driven manner, the guarantees provided by classical theories vanish. We propose a solution to the problem of inference…
Covariance estimation with nonnegative partial correlations
Jake A. Soloff, Adityanand Guntuboyina, Michael I. Jordan
We study the problem of high-dimensional covariance estimation under the constraint that the partial correlations are nonnegative. The sign constraints dramatically simplify estima…
On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification
Tianyi Lin, Zeyu Zheng, Elynn Y. Chen +2
Optimal transport (OT) distances are increasingly used as loss functions for statistical inference, notably in the learning of generative models or supervised learning. Yet, the be…
Lower bounds in multiple testing: A framework based on derandomized proxies
Max Rabinovich, Michael I. Jordan, Martin J. Wainwright
The large bulk of work in multiple testing has focused on specifying procedures that control the false discovery rate (FDR), with relatively less attention being paid to the corres…