16 citations · 66 across the 9 of their papers we have counts for
12 papers
Fairness for Image Generation with Uncertain Sensitive Attributes
Ajil Jalal, Sushrut Karmalkar, Jessica Hoffmann +2
This work tackles the issue of fairness in the context of generative procedures, such as image super-resolution, which entail different definitions from the standard classification…
Instance-Optimal Compressed Sensing via Posterior Sampling
Ajil Jalal, Sushrut Karmalkar, Alexandros G. Dimakis +1
We characterize the measurement complexity of compressed sensing of signals drawn from a known prior distribution, even when the support of the prior is the entire space (rather th…
Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent
Surbhi Goel, Aravind Gollakota, Zhihan Jin +2
We prove the first superpolynomial lower bounds for learning one-layer neural networks with respect to the Gaussian distribution using gradient descent. We show that any classifier…
Robustly Learning any Clusterable Mixture of Gaussians
Ilias Diakonikolas, Samuel B. Hopkins, Daniel Kane +1
We study the efficient learnability of high-dimensional Gaussian mixtures in the outlier-robust setting, where a small constant fraction of the data is adversarially corrupted. We…
Approximation Schemes for ReLU Regression
Ilias Diakonikolas, Surbhi Goel, Sushrut Karmalkar +2
We consider the fundamental problem of ReLU regression, where the goal is to output the best fitting ReLU with respect to square loss given access to draws from some unknown distri…
Lower Bounds for Compressed Sensing with Generative Models
Akshay Kamath, Sushrut Karmalkar, Eric Price
The goal of compressed sensing is to learn a structured signal from a limited number of noisy linear measurements . In traditional compressed sensing, "structure"…