Jake Grigsby

According to our database, Jake Grigsby authored at least 11 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

AMAGO: Scalable In-Context Reinforcement Learning for Adaptive Agents.

Jake Grigsby, Linxi Fan, Yuke Zhu

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Cross-Episodic Curriculum for Transformer Agents.

Lucy Xiaoyang Shi, Yunfan Jiang, Jake Grigsby, Linxi Fan, Yuke Zhu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

PGrad: Learning Principal Gradients For Domain Generalization.

Zhe Wang, Jake Grigsby, Yanjun Qi

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Launchpad: Learning to Schedule Using Offline and Online RL Methods.

Vanamala Venkataswamy, Jake Grigsby, Andrew Grimshaw, Yanjun Qi

CoRR, 2022

ST-MAML : A stochastic-task based method for task-heterogeneous meta-learning.

Zhe Wang, Jake Grigsby, Arshdeep Sekhon, Yanjun Qi

Proceedings of the Uncertainty in Artificial Intelligence, 2022

RARE: Renewable Energy Aware Resource Management in Datacenters.

Vanamala Venkataswamy, Jake Grigsby, Andrew Grimshaw, Yanjun Qi

Proceedings of the Job Scheduling Strategies for Parallel Processing, 2022

2021

A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise Datasets.

Jake Grigsby, Yanjun Qi

CoRR, 2021

Long-Range Transformers for Dynamic Spatiotemporal Forecasting.

Jake Grigsby, Zhe Wang, Yanjun Qi

CoRR, 2021

Towards Automatic Actor-Critic Solutions to Continuous Control.

Jake Grigsby, Jin Yong Yoo, Yanjun Qi

CoRR, 2021

2020

Measuring Visual Generalization in Continuous Control from Pixels.

Jake Grigsby, Yanjun Qi

CoRR, 2020

TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP.

John X. Morris, Eli Lifland, Jin Yong Yoo, Jake Grigsby, Di Jin, Yanjun Qi

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 2020