7 papers
When is Warmstarting Effective for Scaling Language Models?
Neeratyoy Mallik, Maciej Janowski, Johannes Hog +4
Model growth from a given checkpoint aims to accelerate training of a larger model, offering potential resource savings. Despite recent interest, warmstarting has seen limited prac…
carps: A Framework for Comparing N Hyperparameter Optimizers on M Benchmarks
Carolin Benjamins, Helena Graf, Sarah Segel +14
Hyperparameter Optimization (HPO) is crucial to develop well-performing machine learning models. In order to ease prototyping and benchmarking of HPO methods, we propose carps, a b…
Frozen Layers: Memory-efficient Many-fidelity Hyperparameter Optimization
Timur Carstensen, Neeratyoy Mallik, Frank Hutter +1
As model sizes grow, finding efficient and cost-effective hyperparameter optimization (HPO) methods becomes increasingly crucial for deep learning pipelines. While multi-fidelity H…
Warmstarting for Scaling Language Models
Neeratyoy Mallik, Maciej Janowski, Johannes Hog +4
Scaling model sizes to scale performance has worked remarkably well for the current large language models paradigm. The research and empirical findings of various scaling studies l…
Fast Benchmarking of Asynchronous Multi-Fidelity Optimization on Zero-Cost Benchmarks
Shuhei Watanabe, Neeratyoy Mallik, Edward Bergman +1
While deep learning has celebrated many successes, its results often hinge on the meticulous selection of hyperparameters (HPs). However, the time-consuming nature of deep learning…
In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization
Herilalaina Rakotoarison, Steven Adriaensen, Neeratyoy Mallik +3
With the increasing computational costs associated with deep learning, automated hyperparameter optimization methods, strongly relying on black-box Bayesian optimization (BO), face…