Efficient In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Tuning

核心概念

The authors propose FT-PFN, a novel surrogate model for freeze-thaw Bayesian optimization that leverages in-context learning to efficiently and reliably extrapolate learning curves, outperforming existing deep Gaussian process and deep ensemble surrogates. When combined with their novel acquisition mechanism (MFPI-random), the resulting in-context freeze-thaw BO method (ifBO) yields new state-of-the-art performance on deep learning HPO benchmarks.

要約

The paper introduces a novel approach to hyperparameter optimization (HPO) called in-context freeze-thaw Bayesian optimization (ifBO). The key components are:

1. FT-PFN: A new surrogate model for freeze-thaw BO that uses prior-data fitted networks (PFNs) to perform Bayesian learning curve extrapolation in a single forward pass, without the need for online model updates. FT-PFN is trained exclusively on synthetic data generated from a carefully designed curve prior.

2. MFPI-random: A novel acquisition function that randomly samples the prediction horizon and performance threshold, providing a balanced exploration-exploitation trade-off.

The authors show that FT-PFN outperforms existing deep Gaussian process and deep ensemble surrogates in terms of prediction quality and speed. When combined with MFPI-random, the resulting ifBO method achieves new state-of-the-art performance on three deep learning HPO benchmark suites (LCBench, Taskset, PD1), particularly in the low-budget regime.

The key insights are:

• In-context learning with PFNs can efficiently and reliably extrapolate learning curves, eliminating the need for online model updates.
• A portfolio-based acquisition function that randomly samples the prediction horizon and threshold can better navigate the exploration-exploitation trade-off in freeze-thaw BO.
• The combination of FT-PFN and MFPI-random in ifBO outperforms existing freeze-thaw BO methods on a range of deep learning HPO tasks.

統計

The average training time for DPL is 56.576 seconds, while for DyHPO it is 112.168 seconds, and for FT-PFN it is only 1.130 seconds.
FT-PFN achieves a log-likelihood of 3.042 on the Taskset benchmark, compared to -17.760 for DPL and -0.374 for DyHPO.
On the LCBench benchmark, FT-PFN has a mean squared error of 0.003, outperforming DPL at 0.007 and DyHPO at 0.009.

引用

"FT-PFN infers the task-specific relationship between hyperparameter settings and their learning curves in a single forward pass, eliminating the need for online training during the search."
"When combined with our novel acquisition mechanism (MFPI-random), the resulting in-context freeze-thaw BO method (ifBO), yields new state-of-the-art performance in the same three families of deep learning HPO benchmarks considered in prior work."

抽出されたキーインサイト

In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization

by Herilalaina ... 場所 arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16795.pdf

深掘り質問

What other types of prior information, beyond learning curves, could be leveraged to further improve the sample efficiency of ifBO

To further improve the sample efficiency of ifBO, additional types of prior information could be leveraged. One potential source of information could be domain-specific knowledge or expert insights. Incorporating domain knowledge into the model could help guide the search process more effectively by biasing it towards hyperparameter configurations that are more likely to perform well based on prior experience or theoretical understanding. Another type of prior information that could be useful is transfer learning from related tasks. By leveraging knowledge gained from optimizing hyperparameters for similar tasks or datasets, the model could benefit from pre-existing insights and potentially reduce the search space, leading to faster convergence and better performance.

How could the current limitations of FT-PFN, such as the requirement for normalized inputs and support for only up to 10 hyperparameters, be addressed in future work

To address the current limitations of FT-PFN, several strategies could be considered for future work. One approach could involve developing a more flexible model architecture that can handle a larger number of hyperparameters and support non-normalized inputs. This could involve exploring different neural network architectures or incorporating techniques like feature scaling to handle a wider range of input data. Additionally, techniques such as automatic feature engineering or dimensionality reduction could be employed to reduce the complexity of the input space and allow for more hyperparameters to be included in the model. Furthermore, exploring ways to incorporate hierarchical or structured information about hyperparameters could help overcome the current limitations and make the model more versatile and scalable.

Could the in-context learning approach used in ifBO be extended to other areas of machine learning beyond hyperparameter optimization

The in-context learning approach used in ifBO could be extended to other areas of machine learning beyond hyperparameter optimization. One potential application could be in model selection, where the model needs to adapt to different datasets or tasks dynamically. By leveraging in-context learning, the model could quickly adapt to new data and make informed decisions about which model architecture or hyperparameters to use based on the context. Additionally, in-context learning could be applied to tasks like anomaly detection, where the model needs to continuously learn and update its understanding of what constitutes normal behavior based on new observations. This adaptive learning approach could enhance the model's ability to detect anomalies in real-time and improve overall performance.