AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale. - Nicholas Roberts; Samuel Guo; Cong Xu; Ameet Talwalkar; David Lander; Lvfang Tao; Linhang Cai; Shuaicheng Niu; Jianyu Heng; Hongyang Qin; Minwen Deng; Johannes Hog; et al. (NeurIPS (Competition and Demos), 2021)

NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy. - Yash Mehta; Colin White; Arber Zela; Arjun Krishnakumar; Guri Zabergja; Shakiba Moradian; Mahmoud Safari; Kaicheng Yu; Frank Hutter (ICLR, 2022)

NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies. - Arjun Krishnakumar; Colin White; Arber Zela; Renbo Tu; Mahmoud Safari; Frank Hutter (NeurIPS, 2022)

Weight-Entanglement Meets Gradient-Based Neural Architecture Search. - Rhea Sanjay Sukthanker; Arjun Krishnakumar; Mahmoud Safari; Frank Hutter (AutoML, 2024)

TabPFN. - Noah Hollmann; Frank Hutter; Samuel Gabriel Müller; Lennart Purucker; Arjun Krishnakumar; Max Körfer; Robin Tibor Schirrmeister (Zenodo, 2024)

Accurate predictions on small data with a tabular foundation model. - Noah Hollmann; Samuel Müller 005; Lennart Purucker; Arjun Krishnakumar; Max Körfer; Shi Bin Hoo; Robin Tibor Schirrmeister; Frank Hutter (Nat., 2025)

confopt: A Library for Implementation and Evaluation of Gradient-based One-Shot NAS Methods. - Abhash Kumar Jha; Shakiba Moradian; Arjun Krishnakumar; Martin Rapp; Frank Hutter (CoRR, 2025)