33 citations · 48 across the 8 of their papers we have counts for
8 papers
High-dimensional estimation with missing data: Statistical and computational limits
Kabir Aladin Verchand, Ankit Pensia, Saminul Haque +1
We consider computationally-efficient estimation of population parameters when observations are subject to missing data. In particular, we consider estimation under the realizable…
A fast and slightly robust covariance estimator
John Duchi, Saminul Haque, Rohith Kuditipudi
Let from a distribution with mean zero and covariance . Given a dataset $\mathcal…
Near Exact Privacy Amplification for Matrix Mechanisms
Christopher A. Choquette-Choo, Arun Ganesh, Saminul Haque +2
We study the problem of computing the privacy parameters for DP machine learning when using privacy amplification via random batching and noise correlated across rounds via a corre…
An information-theoretic lower bound in time-uniform estimation
John C. Duchi, Saminul Haque
We present an information-theoretic lower bound for the problem of parameter estimation with time-uniform coverage guarantees. Via a new a reduction to sequential testing, we obtai…
A Fast Algorithm for Adaptive Private Mean Estimation
John Duchi, Saminul Haque, Rohith Kuditipudi
We design an -differentially private algorithm to estimate the mean of a -variate distribution, with unknown covariance , that is adaptive to . To within…
Undersampling is a Minimax Optimal Robustness Intervention in Nonparametric Classification
Niladri S. Chatterji, Saminul Haque, Tatsunori Hashimoto
While a broad range of techniques have been proposed to tackle distribution shift, the simple baseline of training on an balanced dataset often achieves clo…
Is Importance Weighting Incompatible with Interpolating Classifiers?
Ke Alexander Wang, Niladri S. Chatterji, Saminul Haque +1
Importance weighting is a classic technique to handle distribution shifts. However, prior work has presented strong empirical and theoretical evidence demonstrating that importance…
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks
Qiyang Li, Saminul Haque, Cem Anil +3
Lipschitz constraints under L2 norm on deep neural networks are useful for provable adversarial robustness bounds, stable training, and Wasserstein distance estimation. While heuri…