# Hyper-parameter Tuning under a Budget Constraint

arXiv: https://arxiv.org/abs/1902.00532 · HTML: https://mlsanity.com/paper/1902.00532/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2019.

## Built on by (most cited first, 2 of 2)

- [Bayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approach](https://mlsanity.com/paper/2206.12708/) (2022)
- [eTOP: Early Termination of Pipelines for Faster Training of AutoML Systems](https://mlsanity.com/paper/2304.08597/) (2023)

## Abstract (arXiv)

We study a budgeted hyper-parameter tuning problem, where we optimize the tuning result under a hard resource constraint. We propose to solve it as a sequential decision making problem, such that we can use the partial training progress of configurations to dynamically allocate the remaining budget. Our algorithm combines a Bayesian belief model which estimates the future performance of configurations, with an action-value function which balances exploration-exploitation tradeoff, to optimize the final output. It automatically adapts the tuning behaviors to different constraints, which is useful in practice. Experiment results demonstrate superior performance over existing algorithms, including the-state-of-the-art one, on real-world tuning tasks across a range of different budgets.