289 citations · 1.2k across the 72 of their papers we have counts for
6 papers · 1 filter
Adaptive Differentially Private Empirical Risk Minimization
Xiaoxia Wu, Lingxiao Wang, Irina Cristali +2
We propose an adaptive (stochastic) gradient perturbation method for differentially private empirical risk minimization. At each iteration, the random noise added to the gradient i…
Auto-differentiable Ensemble Kalman Filters
Yuming Chen, Daniel Sanz-Alonso, Rebecca Willett
Data assimilation is concerned with sequentially estimating a temporally-evolving state. This task, which arises in a wide range of scientific and engineering applications, is part…
Pure Exploration in Kernel and Neural Bandits
Yinglun Zhu, Dongruo Zhou, Ruoxi Jiang +3
We study pure exploration in bandits, where the dimension of the feature representation can be much larger than the number of arms. To overcome the curse of dimensionality, we prop…
Prediction in the presence of response-dependent missing labels
Hyebin Song, Garvesh Raskutti, Rebecca Willett
In a variety of settings, limitations of sensing technologies or other sampling mechanisms result in missing labels, where the likelihood of a missing label in the training set is…
Data-driven Cloud Clustering via a Rotationally Invariant Autoencoder
Takuya Kurihana, Elisabeth Moyer, Rebecca Willett +2
Advanced satellite-born remote sensing instruments produce high-resolution multi-spectral data for much of the globe at a daily cadence. These datasets open up the possibility of i…
Deep Equilibrium Architectures for Inverse Problems in Imaging
Davis Gilton, Gregory Ongie, Rebecca Willett
Recent efforts on solving inverse problems in imaging via deep neural networks use architectures inspired by a fixed number of iterations of an optimization method. The number of i…