Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Symbolic Distillation for Learned TCP Congestion Control
Authors: S P Sharan, Wenqing Zheng, Kuo-Feng Hsu, Jiarong Xing, Ang Chen, Zhangyang Wang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the performance of our distilled symbolic rules on both simulation and emulation environments. |
| Researcher Affiliation | Academia | S P Sharan1, Wenqing Zheng1, Kuo-Feng Hsu2, Jiarong Xing2, Ang Chen2, Zhangyang Wang1 1University of Texas at Austin 2Rice University |
| Pseudocode | No | The paper includes Figure 4, which is a diagrammatic representation of a decision tree (symbolic policy), but it does not contain textual pseudocode or a clearly labeled algorithm block. |
| Open Source Code | Yes | Our code is available at https://github.com/VITA-Group/Symbolic PCC. |
| Open Datasets | Yes | PCC-RL [7] is an open-source RL testbed for simulation of congestion control agents based on the popular Open AI Gym [43] framework. We adopt it as our main playground. |
| Dataset Splits | Yes | The return of the saved roll-outs are clustered using K-Means Clustering, and the optimal cluster number is found to be 4 using the popular elbow curve [52] and silhouette analysis [53] methods. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for experiments (e.g., CPU/GPU models, memory, or cloud provider instances). |
| Software Dependencies | No | The paper mentions software components such as PPO, Open AI Gym, Mininet, and Pantheon, but it does not specify any version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | More hyperparameter details are in our Appendix. |