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cs.LG2026
Can LLMs Beat Classical Hyperparameter Optimization Algorithms? A Study on autoresearch
Fabio Ferreira, Lucca Wobbe, Arjun Krishnakumar +2
The autoresearch repository enables an LLM agent to optimize hyperparameters by editing training code directly. We use it as a testbed to compare classical HPO algorithms against L…
cs.LG2025
Weight-Entanglement Meets Gradient-Based Neural Architecture Search
Rhea Sanjay Sukthanker, Arjun Krishnakumar, Mahmoud Safari +1
Weight sharing is a fundamental concept in neural architecture search (NAS), enabling gradient-based methods to explore cell-based architectural spaces significantly faster than tr…
cs.LG2025
confopt: A Library for Implementation and Evaluation of Gradient-based One-Shot NAS Methods
Abhash Kumar Jha, Shakiba Moradian, Arjun Krishnakumar +2
Gradient-based one-shot neural architecture search (NAS) has significantly reduced the cost of exploring architectural spaces with discrete design choices, such as selecting operat…