153 citations · 192 across the 4 of their papers we have counts for
6 papers
Ask & Explore: Grounded Question Answering for Curiosity-Driven Exploration
Jivat Neet Kaur, Yiding Jiang, Paul Pu Liang
In many real-world scenarios where extrinsic rewards to the agent are extremely sparse, curiosity has emerged as a useful concept providing intrinsic rewards that enable the agent…
NeurIPS 2020 Competition: Predicting Generalization in Deep Learning
Yiding Jiang, Pierre Foret, Scott Yak +7
Understanding generalization in deep learning is arguably one of the most important questions in deep learning. Deep learning has been successfully adopted to a large number of pro…
Observational Overfitting in Reinforcement Learning
Xingyou Song, Yiding Jiang, Stephen Tu +2
A major component of overfitting in model-free reinforcement learning (RL) involves the case where the agent may mistakenly correlate reward with certain spurious features from the…
Fantastic Generalization Measures and Where to Find Them
Yiding Jiang, Behnam Neyshabur, Hossein Mobahi +2
Generalization of deep networks has been of great interest in recent years, resulting in a number of theoretically and empirically motivated complexity measures. However, most pape…
Language as an Abstraction for Hierarchical Deep Reinforcement Learning
Yiding Jiang, Shixiang Gu, Kevin Murphy +1
Solving complex, temporally-extended tasks is a long-standing problem in reinforcement learning (RL). We hypothesize that one critical element of solving such problems is the notio…
Predicting the Generalization Gap in Deep Networks with Margin Distributions
Yiding Jiang, Dilip Krishnan, Hossein Mobahi +1
As shown in recent research, deep neural networks can perfectly fit randomly labeled data, but with very poor accuracy on held out data. This phenomenon indicates that loss functio…