activity
20222024
most citedLearning Task-relevant Representations for Generalization via Characteristic Functions of Reward Sequence Distributions

14 citations · 16 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2024

Learning to Cut via Hierarchical Sequence/Set Model for Efficient Mixed-Integer Programming

Jie Wang, Zhihai Wang, Xijun Li +7

Cutting planes (cuts) play an important role in solving mixed-integer linear programs (MILPs), which formulate many important real-world applications. Cut selection heavily depends…

cs.CV2023

DocMAE: Document Image Rectification via Self-supervised Representation Learning

Shaokai Liu, Hao Feng, Wengang Zhou +3

Tremendous efforts have been made on document image rectification, but how to learn effective representation of such distorted images is still under-explored. In this paper, we pre…

eess.SP2023

Joint Optimization of Base Station Clustering and Service Caching in User-Centric MEC

Langtian Qin, Hancheng Lu, Yao Lu +2

Edge service caching can effectively reduce the delay or bandwidth overhead for acquiring and initializing applications. To address single-base station (BS) transmission limitation…

cs.LG20222 cited

Automatic Reward Design via Learning Motivation-Consistent Intrinsic Rewards

Yixiang Wang, Yujing Hu, Feng Wu +1

Reward design is a critical part of the application of reinforcement learning, the performance of which strongly depends on how well the reward signal frames the goal of the design…

cs.LG202214 cited

Learning Task-relevant Representations for Generalization via Characteristic Functions of Reward Sequence Distributions

Rui Yang, Jie Wang, Zijie Geng +4

Generalization across different environments with the same tasks is critical for successful applications of visual reinforcement learning (RL) in real scenarios. However, visual di…