Papers

• EnvX: Agentize Everything with Agentic AI (2025)
  Linyao Chen et al.
  1.44
• Agent Data Protocol: Unifying Datasets for Diverse, Effective Fine-tuning of LLM Agents (2025)
  Yueqi Song et al.
  1.28
• Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems (2020)
  Jaimie Drozdal et al.
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• Can AutoML outperform humans? An evaluation on popular OpenML datasets using AutoML Benchmark (2020)
  Marc Hanussek et al.
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• NNStreamer: Efficient and Agile Development of On-Device AI Systems (2021)
  MyungJoo Ham et al.
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• Towards Green Automated Machine Learning: Status Quo and Future Directions (2021)
  Tanja Tornede and Alexander Tornede and Jonas Hanselle and Marcel Wever and Felix Mohr and Eyke H\"ullermeier
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• PiML Toolbox for Interpretable Machine Learning Model Development and Diagnostics (2023)
  Agus Sudjianto et al.
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• Assembled-OpenML: Creating Efficient Benchmarks for Ensembles in AutoML with OpenML (2023)
  Lennart Purucker et al.
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• ASP: Automatic Selection of Proxy dataset for efficient AutoML (2023)
  Peng Yao et al.
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• EcoMLS: A Self-Adaptation Approach for Architecting Green ML-Enabled Systems (2024)
  Meghana Tedla et al.
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• SciCode: A Research Coding Benchmark Curated by Scientists (2024)
  Minyang Tian et al.
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• OpenDevin: An Open Platform for AI Software Developers as Generalist Agents (2024)
  Xingyao Wang et al.
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• MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering (2024)
  Jun Shern Chan et al.
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