Papers with ML algorithms
RedCoast: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs (2024.naacl-demo)
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| Challenge: | Recent advances in machine learning (ML) are attributed to large language models (LLMs), but their escalating memory requirements require developers to partition a large model to distribute it across multiple GPUs or TPUs. |
| Approach: | They propose a lightweight and user-friendly tool to automate distributed training and inference for LLMs and to simplify ML pipeline development. |
| Outcome: | The proposed tool automates distributed training and inference for LLMs, and simplifies ML pipeline development. |
CodeScientist: End-to-End Semi-Automated Scientific Discovery with Code-based Experimentation (2025.findings-acl)
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Peter Jansen, Oyvind Tafjord, Marissa Radensky, Pao Siangliulue, Tom Hope, Bhavana Dalvi Mishra, Bodhisattwa Prasad Majumder, Daniel S Weld, Peter Clark
| Challenge: | Automated scientific discovery (ASD) systems are limited in their evaluation of software artifacts and large volumes of research artifs are typically evaluated using conference-style paper review with limited evaluation of code. |
| Approach: | They propose a novel ASD system that frames ideation and experiment construction as a form of genetic search jointly over combinations of research articles and codeblocks defining common actions in a domain. |
| Outcome: | The proposed system returns 19 discoveries on machine-generated ideas in the domain of agents and virtual environments. |
MLAlgo-Bench: Can Machines Implement Machine Learning Algorithms? (2025.findings-emnlp)
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| Challenge: | Currently, the top-performing models achieve a 48.8% task completion rate on realizing machine learning algorithms . |
| Approach: | They propose a benchmark to test machine learning's ability to generate ML code for humans . they propose an automatic evaluation framework with metrics such as task pass rate and time overhead . |
| Outcome: | The proposed benchmark is unique in its focus on interpreting complex human instructions and producing multi-step, high-complexity code. |