Compressed Sensing

Phase retrieval in high dimensions: Statistical and computational phase transitions NeurIPS 2020 Robust Low-Rank Discovery of Data-Driven Partial Differential Equations AAAI 2020 Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors ICML 2020 Decentralized sketching of low rank matrices NeurIPS 2019 Deep Compressed Sensing ICML 2019 Generalization Error Analysis of Quantized Compressive Learning NeurIPS 2019 Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling ICML 2019 Random Projections with Asymmetric Quantization NeurIPS 2019 Superset Technique for Approximate Recovery in One-Bit Compressed Sensing NeurIPS 2019 The All-or-Nothing Phenomenon in Sparse Linear Regression COLT 2019 Universality in Learning from Linear Measurements NeurIPS 2019 How Much Restricted Isometry is Needed In Nonconvex Matrix Recovery? NeurIPS 2018 Learning without the Phase: Regularized PhaseMax Achieves Optimal Sample Complexity NeurIPS 2018 Phase Retrieval Under a Generative Prior NeurIPS 2018 Plug-in Estimation in High-Dimensional Linear Inverse Problems: A Rigorous Analysis NeurIPS 2018 Thwarting Adversarial Examples: An L_0-Robust Sparse Fourier Transform NeurIPS 2018 Convolutional Phase Retrieval NeurIPS 2017 Fitting Low-Rank Tensors in Constant Time NeurIPS 2017 Learned D-AMP: Principled Neural Network based Compressive Image Recovery NeurIPS 2017 Near Optimal Sketching of Low-Rank Tensor Regression NeurIPS 2017 On Blackbox Backpropagation and Jacobian Sensing NeurIPS 2017 Simple strategies for recovering inner products from coarsely quantized random projections NeurIPS 2017 Solving Most Systems of Random Quadratic Equations NeurIPS 2017 Fast recovery from a union of subspaces NeurIPS 2016 Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices AISTATS 2016