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Purucker, Lennart

  1. TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models
    2025/11/11 by Léo Grinsztajn, Grinsztajn, Léo, Klemens Flöge +46 · 2 voices · 8 citations
    Computer Science · #Machine Learning and Data Classification #Explainable Artificial Intelligence (XAI) #Advanced Neural Network Applications
  2. TabArena: A Living Benchmark for Machine Learning on Tabular Data
    2025/06/20 by Nick Erickson, Erickson, Nick, Lennart Purucker +11 · 21 citations
    Computer Science · #Adversarial Robustness in Machine Learning #Artificial Intelligence (cs.AI) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Data Classification
  3. CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure
    2023/07/01 by Purucker, Lennart, Beel, Joeran · 2 citations
    #FOS: Computer and information sciences #I.2.6 #I.5.1 #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE)
  4. Transfer Learning for Finetuning Large Language Models
    2024/11/02 by Tobias Strangmann, Strangmann, Tobias, Lennart Purucker +9 · 3 citations
    Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Natural Language Processing Techniques #Speech Recognition and Synthesis #Topic Modeling
  5. Assembled-OpenML: Creating Efficient Benchmarks for Ensembles in AutoML with OpenML
    2023/07/01 by Purucker, Lennart, Beel, Joeran · 1 citation
    #E.m #FOS: Computer and information sciences #I.2.6 #Machine Learning (cs.LG)
  6. Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML
    2023/07/17 by Purucker, Lennart, Schneider, Lennart, Anastacio, Marie +3 · 1 citation
    #FOS: Computer and information sciences #I.2.6 #I.5.1 #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE)
  7. Unreflected Use of Tabular Data Repositories Can Undermine Research Quality
    2025/03/12 by Tschalzev, Andrej, Purucker, Lennart, Lüdtke, Stefan +3 · 2 citations
    #FOS: Computer and information sciences #Machine Learning (cs.LG)
  8. Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data
    2025/07/05 by Garg, Anurag, Ali, Muhammad, Hollmann, Noah +3 · 4 citations
    #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME)
  9. Don't Waste Your Time: Early Stopping Cross-Validation
    2024/05/06 by Edward M. Bergman, Bergman, Edward, Lennart Purucker +3 · 1 citation
    Computer Science · #Artificial Intelligence (cs.AI) #Data Stream Mining Techniques #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning and Data Classification
  10. Ensembling Finetuned Language Models for Text Classification
    2024/10/25 by Arango, Sebastian Pineda, Janowski, Maciej, Purucker, Lennart +3 · 1 citation
    #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG)
  11. Early Stopping Tabular In-Context Learning
    2025/06/26 by Küken, Jaris, Purucker, Lennart, Hutter, Frank · 3 citations
    #FOS: Computer and information sciences #Machine Learning (cs.LG)
  12. Towards Benchmarking Foundation Models for Tabular Data With Text
    2025/07/10 by Breenda Das, Mráz, Martin, Anshul Gupta +6 · 3 citations
    Computer Science · Decision Sciences · Social Sciences · #Computational and Text Analysis Methods #Data Quality and Management #FOS: Computer and information sciences #Machine Learning (cs.LG) #Topic Modeling
  13. HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language Models
    2024/05/16 by Rhea Sanjay Sukthanker, Sukthanker, Rhea Sanjay, Arber Zela +7 · 1 citation
    Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Natural Language Processing Techniques #Parallel Computing and Optimization Techniques #Topic Modeling