Search Results for author:
Found 6 papers, 2 papers with code
Synthetic Design: An Optimization Approach to Experimental Design with Synthetic Controls
We investigate the optimal design of experimental studies that have pre-treatment outcome data available.
Variance Reduction in Bipartite Experiments through Correlation Clustering
This paper introduces a novel clustering objective and a corresponding algorithm that partitions a bipartite graph so as to maximize the statistical power of a bipartite experiment on that graph.
Inferring Graphs from Cascades: A Sparse Recovery Framework
In the Network Inference problem, one seeks to recover the edges of an unknown graph from the observations of cascades propagating over this graph.
Generative Adversarial Nets
We propose a new framework for estimating generative models via adversarial nets, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake.
Overcoming the Curse of Sentence Length for Neural Machine Translation using Automatic Segmentation
The authors of (Cho et al., 2014a) have shown that the recently introduced neural network translation systems suffer from a significant drop in translation quality when translating long sentences, unlike existing phrase-based translation systems.
Generative Adversarial Networks
We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake.
Super-Resolution
Time-Series Few-Shot Learning with Heterogeneous Channels
