research
∙
06/25/2023
Language models are weak learners
A central notion in practical and theoretical machine learning is that o...
share
research
∙
06/08/2023
On the Importance of Exploration for Generalization in Reinforcement Learning
Existing approaches for improving generalization in deep reinforcement l...
research
∙
06/07/2023
On the Joint Interaction of Models, Data, and Features
Learning features from data is one of the defining characteristics of de...
research
∙
05/22/2023
Neural Functional Transformers
The recent success of neural networks as implicit representation of data...
research
∙
02/27/2023
Permutation Equivariant Neural Functionals
This work studies the design of neural networks that can process the wei...
research
∙
12/08/2022
Learning Options via Compression
Identifying statistical regularities in solutions to some tasks in multi...
research
∙
06/27/2022
Agreement-on-the-Line: Predicting the Performance of Neural Networks under Distribution Shift
Recently, Miller et al. showed that a model's in-distribution (ID) accur...
research
∙
06/25/2021
Assessing Generalization of SGD via Disagreement
We empirically show that the test error of deep networks can be estimate...
research
∙
04/24/2021
Ask Explore: Grounded Question Answering for Curiosity-Driven Exploration
In many real-world scenarios where extrinsic rewards to the agent are ex...
research
∙
12/14/2020
NeurIPS 2020 Competition: Predicting Generalization in Deep Learning
Understanding generalization in deep learning is arguably one of the mos...
research
∙
12/06/2019
Observational Overfitting in Reinforcement Learning
A major component of overfitting in model-free reinforcement learning (R...
research
∙
12/04/2019
Fantastic Generalization Measures and Where to Find Them
Generalization of deep networks has been of great interest in recent yea...
research
∙
06/18/2019
Language as an Abstraction for Hierarchical Deep Reinforcement Learning
Solving complex, temporally-extended tasks is a long-standing problem in...
research
∙
09/28/2018