Search Results for author: Yiding Jiang

Found 7 papers, 3 papers with code

Assessing Generalization of SGD via Disagreement

no code implementations ICLR 2022 Yiding Jiang, Vaishnavh Nagarajan, Christina Baek, J. Zico Kolter

We empirically show that the test error of deep networks can be estimated by simply training the same architecture on the same training set but with a different run of Stochastic Gradient Descent (SGD), and measuring the disagreement rate between the two networks on unlabeled test data.

Ask & Explore: Grounded Question Answering for Curiosity-Driven Exploration

no code implementations24 Apr 2021 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 to explore its environment and acquire information to achieve its goals.

Question Answering

Observational Overfitting in Reinforcement Learning

no code implementations ICLR 2020 Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du, Behnam Neyshabur

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 observations generated by the Markov Decision Process (MDP).


Language as an Abstraction for Hierarchical Deep Reinforcement Learning

2 code implementations NeurIPS 2019 Yiding Jiang, Shixiang Gu, Kevin Murphy, Chelsea Finn

We find that, using our approach, agents can learn to solve to diverse, temporally-extended tasks such as object sorting and multi-object rearrangement, including from raw pixel observations.


Predicting the Generalization Gap in Deep Networks with Margin Distributions

1 code implementation ICLR 2019 Yiding Jiang, Dilip Krishnan, Hossein Mobahi, Samy Bengio

In this paper, we propose such a measure, and conduct extensive empirical studies on how well it can predict the generalization gap.

Cannot find the paper you are looking for? You can Submit a new open access paper.