Guangyao Chen
I’m currently a Postdoctoral Associate at AI for Science Institute, Cornell University. I received my Ph.D. degree at School of Computer Science, Peking University. I received my Bachelor degree in Computer Science from Wuhan University in 2018.
My research vision is to devise an efficient, reliable, and autonomous AI system that can operate in the open world. Guided by the principles of efficiency, reliability, and autonomy, I concentrate on these crucial aspects to enhance and adapt AI models:
- Autonomous Agent: constructing efficient and robust autonomous LLM-based agent systems that leverage LLMs to accomplish automated planning, reasoning, learning, and collaboration.
- Open World Learning: boosting the robustness of efficient AI models in the open world, achieving adaptation to diverse distributions, and discovering and learning new categories.
- Efficient AI Model: designing an efficient AI model architecture and application criteria, aiming to empower AI models to run on resource-scarce devices, and facilitate efficient inference and learning.
Feel free to catch me if interested to discuss ideas or work together. 😜
news
2024.09 | 🎉🎉 One paper on Visual RL are accepted by the conference NeurIPS 2024. |
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2024.07 | 🎉🎉 Two papers on Few-shot Learning are accepted by the conference ACMMM 2024. |
2024.04 | 🎉🎉 One paper on Autonomous Agent is accepted by the conference IJCAI 2024. |
2023.12 | 🎉🎉 One paper on Incremental Novel Class Discovery is accepted by the conference AAAI 2024. |
2023.11 | Invited talk at Qingyuan Workshop (Online). |
2023.11 | Invited talk at RLChina 2023 in Suzhou. |
2023.06 | I receive my Ph.D. degree in computer science from Peking University with Outstanding Doctoral Dissertation Award. |
2022.09 | 🎉🎉 Two papers on Image-based Reinforcement Learning and Out-of-Distribution Detection are accepted by the conference NeurIPS 2022. |
selected publications
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NeurIPSSpectrum Random Masking for Generalization in Image-based Reinforcement LearningIn Advances in Neural Information Processing Systems, Oct 2022
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ACMMMMICM: Rethinking Unsupervised Pretraining for Enhanced Few-shot LearningIn ACM Multimedia, Oct 2024 (Oral Presentation, Acceptance Rate: < 3.97%)
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ACMMMLearning Unknowns from Unknowns: Diversified Negative Prototypes Generator for Few-shot Open-Set RecognitionIn ACM Multimedia, Oct 2024
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NeurIPSSeek Commonality but Preserve Differences: Dissected Dynamics Modeling for Multi-modal Visual RLIn Advances in Neural Information Processing Systems, Oct 2024