AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning

Krishnateja Killamsetty · Guttu Sai Abhishek · Aakriti Lnu · Ganesh Ramakrishnan · Alexandre Evfimievski · Lucian Popa · Rishabh Iyer

Hall J #120

Keywords: [ efficiency ] [ Submodularity ] [ hyperparameter optimization ] [ Data Subset Selection ]


Deep neural networks have seen great success in recent years; however, training a deep model is often challenging as its performance heavily depends on the hyper-parameters used. In addition, finding the optimal hyper-parameter configuration, even with state-of-the-art (SOTA) hyper-parameter optimization (HPO) algorithms, can be time-consuming, requiring multiple training runs over the entire datasetfor different possible sets of hyper-parameters. Our central insight is that using an informative subset of the dataset for model training runs involved in hyper-parameter optimization, allows us to find the optimal hyper-parameter configuration significantly faster. In this work, we propose AUTOMATA, a gradient-based subset selection framework for hyper-parameter tuning. We empirically evaluate the effectiveness of AUTOMATA in hyper-parameter tuning through several experiments on real-world datasets in the text, vision, and tabular domains. Our experiments show that using gradient-based data subsets for hyper-parameter tuning achieves significantly faster turnaround times and speedups of 3×-30× while achieving comparable performance to the hyper-parameters found using the entire dataset.

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