Xin-Qiang Cai

According to our database1, Xin-Qiang Cai authored at least 12 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Leveraging Domain-Unlabeled Data in Offline Reinforcement Learning across Two Domains.
CoRR, 2024

Reinforcement Learning from Bagged Reward: A Transformer-based Approach for Instance-Level Reward Redistribution.
CoRR, 2024

An Animation-Based Augmentation Approach for Action Recognition from Discontinuous Video.
Proceedings of the ECAI 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain, 2024

Leveraging Multi-lingual Positive Instances in Contrastive Learning to Improve Sentence Embedding.
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics, 2024

2023
Imitation Learning from Vague Feedback.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Distributional Pareto-Optimal Multi-Objective Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Anomaly Guided Policy Learning from Imperfect Demonstrations.
Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022

2021
Seeing Differently, Acting Similarly: Imitation Learning with Heterogeneous Observations.
CoRR, 2021

Imitation Learning from Pixel-Level Demonstrations by HashReward.
Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 2021

2019
Expert-Level Atari Imitation Learning from Demonstrations Only.
CoRR, 2019

Nearest Neighbor Ensembles: An Effective Method for Difficult Problems in Streaming Classification with Emerging New Classes.
Proceedings of the 2019 IEEE International Conference on Data Mining, 2019


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