7 citations · 17 across the 5 of their papers we have counts for
5 papers
-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis
Zishun Yu, Yunzhe Tao, Liyu Chen +2
Program synthesis aims to create accurate, executable programs from problem specifications, specifically from natural language descriptions in our context. Recent studies have leve…
Three-quarter Sibling Regression for Denoising Observational Data
Shiv Shankar, Daniel Sheldon, Tao Sun +2
Many ecological studies and conservation policies are based on field observations of species, which can be affected by systematic variability introduced by the observation process.…
REPAINT: Knowledge Transfer in Deep Reinforcement Learning
Yunzhe Tao, Sahika Genc, Jonathan Chung +2
Accelerating learning processes for complex tasks by leveraging previously learned tasks has been one of the most challenging problems in reinforcement learning, especially when th…
Zero-Shot Reinforcement Learning with Deep Attention Convolutional Neural Networks
Sahika Genc, Sunil Mallya, Sravan Bodapati +2
Simulation-to-simulation and simulation-to-real world transfer of neural network models have been a difficult problem. To close the reality gap, prior methods to simulation-to-real…
Differentially Private Learning of Undirected Graphical Models using Collective Graphical Models
Garrett Bernstein, Ryan McKenna, Tao Sun +3
We investigate the problem of learning discrete, undirected graphical models in a differentially private way. We show that the approach of releasing noisy sufficient statistics usi…