Dongsub Shim

According to our database, Dongsub Shim authored at least 11 papers between 2020 and 2024.

Collaborative distances:

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

2024

MASSW: A New Dataset and Benchmark Tasks for AI-Assisted Scientific Workflows.

Xingjian Zhang, Yutong Xie, Jin Huang, Jinge Ma, Zhaoying Pan, Qijia Liu, Ziyang Xiong, Tolga Ergen, Dongsub Shim, Honglak Lee, Qiaozhu Mei

CoRR, 2024

Code Models are Zero-shot Precondition Reasoners.

Lajanugen Logeswaran, Sungryull Sohn, Yiwei Lyu, Anthony Z. Liu, Dong-Ki Kim, Dongsub Shim, Moontae Lee, Honglak Lee

Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2024

2023

MultiPrompter: Cooperative Prompt Optimization with Multi-Agent Reinforcement Learning.

Dong-Ki Kim, Sungryull Sohn, Lajanugen Logeswaran, Dongsub Shim, Honglak Lee

CoRR, 2023

TOD-Flow: Modeling the Structure of Task-Oriented Dialogues.

Sungryull Sohn, Yiwei Lyu, Anthony Z. Liu, Lajanugen Logeswaran, Dong-Ki Kim, Dongsub Shim, Honglak Lee

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Preserving Linear Separability in Continual Learning by Backward Feature Projection.

Qiao Gu, Dongsub Shim, Florian Shkurti

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Is Continual Learning Truly Learning Representations Continually?

Sungmin Cha, Dongsub Shim, Hyunwoo Kim, Moontae Lee, Honglak Lee, Taesup Moon

CoRR, 2022

Multi-policy Grounding and Ensemble Policy Learning for Transfer Learning with Dynamics Mismatch.

Hyun-Rok Lee, Ram Ananth Sreenivasan, Yeonjeong Jeong, Jongseong Jang, Dongsub Shim, Chi-Guhn Lee

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

2021

ExCon: Explanation-driven Supervised Contrastive Learning for Image Classification.

Zhibo Zhang, Jongseong Jang, Chiheb Trabelsi, Ruiwen Li, Scott Sanner, Yeonjeong Jeong, Dongsub Shim

CoRR, 2021

EDDA: Explanation-driven Data Augmentation to Improve Model and Explanation Alignment.

Ruiwen Li, Zhibo Zhang, Jiani Li, Scott Sanner, Jongseong Jang, Yeonjeong Jeong, Dongsub Shim

CoRR, 2021

Online Class-Incremental Continual Learning with Adversarial Shapley Value.

Dongsub Shim, Zheda Mai, Jihwan Jeong, Scott Sanner, Hyunwoo Kim, Jongseong Jang

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Adversarial Shapley Value Experience Replay for Task-Free Continual Learning.

Zheda Mai, Dongsub Shim, Jihwan Jeong, Scott Sanner, Hyunwoo Kim, Jongseong Jang

CoRR, 2020