855 citations · 2.9k across the 86 of their papers we have counts for
9 papers · 1 filter
NeuralFDR: Learning Discovery Thresholds from Hypothesis Features
Fei Xia, Martin J. Zhang, James Zou +1
As datasets grow richer, an important challenge is to leverage the full features in the data to maximize the number of useful discoveries while controlling for false positives. We…
Medoids in almost linear time via multi-armed bandits
Vivek Bagaria, Govinda M. Kamath, Vasilis Ntranos +2
Computing the medoid of a large number of points in high-dimensional space is an increasingly common operation in many data science problems. We present an algorithm Med-dit which…
Porcupine Neural Networks: (Almost) All Local Optima are Global
Soheil Feizi, Hamid Javadi, Jesse Zhang +1
Neural networks have been used prominently in several machine learning and statistics applications. In general, the underlying optimization of neural networks is non-convex which m…
Understanding GANs: the LQG Setting
Soheil Feizi, Farzan Farnia, Tony Ginart +1
Generative Adversarial Networks (GANs) have become a popular method to learn a probability model from data. In this paper, we aim to provide an understanding of some of the basic i…
Two-Way Interference Channel Capacity: How to Have the Cake and Eat it Too
Changho Suh, Jaewoong Cho, David Tse
Two-way communication is prevalent and its fundamental limits are first studied in the point-to-point setting by Shannon [1]. One natural extension is a two-way interference channe…
Polar Coding for Parallel Gaussian Channel
David Tse, Bin Li, Kai Chen
In this paper, we propose a Polar coding scheme for parallel Gaussian channels. The encoder knows the sum rate of the parallel channels but does not know the rate of any channel. B…