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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
About Me
About Me
Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 3
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Blog Post number 1
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portfolio
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publications
Timely Broadcasting in Erasure Networks: Age-Rate Tradeoffs
Published in ISIT 2021, 2021
Investigating the benefits of coding on the Age of Information (AoI) and rate in memoryless broadcast channels.
Recommended citation: Chen, Xingran, Renpu Liu, Shaochong Wang, and Shirin Saeedi Bidokhti. (2021). "Timely Broadcasting in Erasure Networks: Age-Rate Tradeoffs." ISIT 2021.
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Exploiting Feature Heterogeneity for Improved Generalization in Federated Multi-task Learning
Published in ISIT 2023, 2023
We prove that statistical heterogeneity in federated learning can enhance generalization performance under certain conditions.
Recommended citation: Renpu Liu, Jing Yang, and Cong Shen. (2023). "Exploiting Feature Heterogeneity for Improved Generalization in Federated Multi-task Learning." ISIT 2023.
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Federated Representation Learning in the Under-Parameterized Regime
Published in ICML 2024, 2024
This paper explores Federated Representation Learning (FRL) in the under-parameterized regime.
Recommended citation: Renpu Liu, Cong Shen, and Jing Yang. (2024). "Federated Representation Learning in the Under-Parameterized Regime." ICML 2024.
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A Shared Low-Rank Adaptation Approach to Personalized RLHF
Published in AISTATS 2025, 2025
A personalized RLHF framework leveraging shared LoRA modules to capture both common and user-specific features.
Recommended citation: Renpu Liu, Peng Wang, Donghao Li, Cong Shen, Jing Yang. (2025). "A Shared Low-Rank Adaptation Approach to Personalized RLHF." AISTATS 2025.
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On the Learn-to-Optimize Capabilities of Transformers in In-Context Sparse Recovery
Published in ICLR 2025, 2025
We demonstrate that Transformers can implement LISTA-type learning-to-optimize algorithms for LASSO-based sparse recovery.
Recommended citation: Renpu Liu*, Ruida Zhou*, Cong Shen, and Jing Yang. (2025). "On the Learn-to-Optimize Capabilities of Transformers in In-Context Sparse Recovery." ICLR 2025.
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In-Context Learning Based Efficient Spectrum Sensing
Published in ISIT 2025, 2025
Applying In-Context Learning techniques to efficient spectrum sensing problems.
Recommended citation: Renpu Liu, Liwen Zhong, Wooram Lee, and Jing Yang. (2025). "In-Context Learning Based Efficient Spectrum Sensing." ISIT 2025.
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Unlabeled Data Can Provably Enhance In-Context Learning of Transformers
Published in NeurIPS 2025, 2025
This paper investigates the theoretical foundations of how unlabeled data in the prompt improves In-Context Learning.
Recommended citation: Renpu Liu, Jing Yang. (2025). "Unlabeled Data Can Provably Enhance In-Context Learning of Transformers." NeurIPS 2025.
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talks
Talk 1 on Relevant Topic in Your Field
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This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
EE 351 Discrete-Time Linear System
RA, Pennsylvania State University, 2022
EE 360 Communication System
RA, Pennsylvania State University, 2022