Towards Learning Universal Hyperparameter Optimizers with Transformers
Abstract
Meta-learning hyperparameter optimization (HPO) algorithms from prior experiments is a promising approach to improve optimization efﬁciency over objective functions from a similar distribution.
However, existing methods are restricted to learning from experiments sharing the same set of hyperparameters.
In this paper, we introduce the O PT F ORMER , the ﬁrst text-based Transformer HPO framework that provides a universal end-to-end interface for jointly learning policy and function prediction when trained on vast tuning data from the wild, such as Google’s
Vizier database, one of the world’s largest HPO datasets.
Our extensive experiments demonstrate that the O PT F ORMER can simultaneously imitate at least 7 different
HPO algorithms, which can be further improved via its function uncertainty estimates.
Compared to a Gaussian Process, the O PT F ORMER also learns a robust prior distribution for hyperparameter response functions, and can thereby provide more accurate and better calibrated predictions.
This work paves the path to future extensions for training a Transformer-based model as a general HPO optimizer.
Introduction
The emergence of public machine learning data platforms such as OpenML [1] and hyperparameter optimization (HPO) services such as Google Vizier [2], Amazon SageMaker [3] and Microsoft
Azure [4] have made large-scale datasets containing hyperparameter evaluations accessible.
For our use-case in this paper, Google Vizier is the de-facto HPO service across Google, having optimized some of Google’s largest products and research efforts and contains a collection of valuable tuning data within the last 5 years.
While there is growing interest in leveraging such data to meta-learn hyperparameter optimization algorithms [5–8], dealing with large datasets consisting of experimental trials in the wild can be challenging, due to large variations in HPO problems and their associated text metadata (e.g. shown later in Table 1).
Thus, most meta and transfer-learning HPO methods [7–16] consider a restrictive setting where all tasks must share the same set of hyperparameters so that the input data can be represented as ﬁxed-sized vectors.
Consequently, such methods only exploit a small portion of the available data to learn priors.
This drawback is more severe for large datasets which contain signiﬁcant amounts of useful information.
To overcome these limitations, we introduce the O PT F ORMER , a general hyperparameter optimization framework based on Transformers [17].
Transformers have demonstrated excellent performance in many data tasks, ranging from natural language [18], images [19, 20], biological data [21, 22], code [23, 24], and control [25, 26].
Here, we investigate how to use a Transformer as a universal interface for modelling experimental data and learn HPO algorithms, as given a sufﬁcient amount of data, a Transformer can potentially learn a more complex prior distribution than standard Bayesian
Optimization (BO) with Gaussian Processes (GPs), especially as the Transformer possesses certain computational advantages over GPs for large datasets.
Code:
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We introduce a serialization scheme to convert a combination of any metadata and an optimization trajectory into text, represented as a sequence of tokens, and formulate the HPO task as a sequence modeling problem.
We adopt a supervised learning approach, by learning to predict parameters and hyperparameter response functions from ofﬂine tuning data (See Fig. 1).
In order to further improve optimization performance, we augment the model by utilizing its own function prediction during inference (Section 4.3).
Extensive experiments on both public and private datasets demonstrate the
O PT F ORMER ’s competitive tuning and generalization abilities.
In summary, our contributions are as follows:
• We formulate, to the best of our knowledge, the ﬁrst meta-learning HPO framework to learn both policy and function priors from data across different search spaces.
• The O PT F ORMER is capable of learning the behaviors of 7 diverse blackbox optimization algorithms relying on a broad class of methods (non-adaptive, evolutionary, and Bayesian). • Furthermore, the O PT F ORMER learns the prior over objective functions and provides both accurate and well calibrated predictions, in many cases signiﬁcantly surpassing GPs in log-predictive likelihood and expected calibration error (ECE) [27].
• Lastly, O PT F ORMER policies augmented with model-based optimization, such as the use of Expected Improvement acquisition functions, are competitive HPO algorithms.
To the best of our knowledge, this is the ﬁrst time Transformers are augmented with acquisition functions for online adaptation.
Preliminaries 2.1 Meta-learning for hyperparameter optimization
HPO aims to ﬁnd a set of hyperparameters x from search space X to maximize a model performance metric, y = f
( x ) , often referred to as a response function.
Table 1 shows an example of HPO experimental data.
Following the HPO nomenclature [2, 28], an experimental study consists of metadata ( m ) and a history of trials ( h ).
The metadata contains arbitrary unstructured information, including but not limited to descriptions of the problem, optimization algorithm, names, types and value ranges of hyperparameters.
The history after t trials, h t = ( x 1 , y 1 , . . .
, x t , y t ) , contains a sequence of trials, each of which consists of a parameter suggestion x and function value y .
The goal of the meta-learning approach for HPO is to learn the shared knowledge among the objective functions f from a dataset of multiple tuning experiments represented as studies and to obtain an optimal HPO algorithm for new hyperparameter tuning tasks from a similar distribution to those in the dataset.
An HPO algorithm π maps the metadata and history to a distribution over hyperparameter suggestions, i.e. π ( x t +1 | m, h t ) .
Using the terminology of ofﬂine RL [29], we refer to the algorithm used to generate the trajectories in a dataset as the behavior policy π b .
We primarily consider search spaces X with a ﬁxed number D of hyperparameters per task, and hence x = ( x (1) , . . .
, x ( D ) ) , with each hyperparameter x ( d ) being of type DOUBLE , INTEGER ,
DISCRETE , or CATEGORICAL (see Appendix A.1 for details).
More complex search spaces can be supported as discussed in Section 7.
2.2 Transformer model
[Table]
The Transformer model is an efﬁcient attention-based neural network architecture for sequence modeling [17].
We adopt the T5 Transformer encoder-decoder architecture [30].
The encoder and decoder each consist of a stack of multi-head selfattention layers which construct pairwise interactions between positions, followed by position-wise feed-forward networks.
The encoder converts a sequence of input token representations m , to a sequence of continuous embeddings, which is fed to the decoder to generate a sequence of output tokens h one element at a time (see Fig. 1).
Related work
There has been a rich set of works in meta-learning and transfer learning by modifying speciﬁc core components of the BO pipeline, such as the acquisition function or the GP, in order to tackle BO’s myopic behavior, or obtaining more information from similar tasks.
For instance, approaches include learning new acquisition functions [31], multi-task BO [7–13] and BO for transfer learning using contextual GPs [14–16].
[32] also studies the use of meta-BO for hyperparameter tuning tasks in machine learning.
However, all of these works consider a ﬁxed search space.
A more radical meta-learning approach to non-differentiable optimization trains recurrent neural networks (RNNs) as neural optimizers from scratch.
[33] ﬁrst proposed training an RNN with gradient descent to optimize blackbox functions and hyperparameters while [34, 35] train RNNs using reinforcement learning (RL) to solve RL tasks.
Unfortunately, prior works are limited to ﬁxed search spaces and only use online generated data, constraining the training objectives to be cheaply computable.
In this work, we wish to overcome the limitations of previous works by exploiting the Transformer architecture.
Numerous works have demonstrated Transformers’ strong capabilities in ﬂexible symbolic and numerical manipulation.
On the symbolic side, Transformers have been shown able to manipulate symbolic mathematical expressions [36–38] and generate code [23, 24].
Furthermore, on the numerical side, Transformers have also been shown able to perform linear algebra computations [39], Bayesian Inference [40], and ofﬂine RL [25, 26, 41].
For AutoML speciﬁcally, [42] has demonstrated Transformers’ and analogous graph neural networks’ abilities to use dataset descriptions and metadata to generate classiﬁcation and data preprocessing pipelines.
However, to date, there has been little effort in attacking the full problem of hyperparameter tuning in the blackbox optimization setting.
In this paper, the challenging task of learning algorithms from blackbox optimization trajectories can be seen as a signiﬁcant extension of both symbolic and numerical manipulation.
Since the underlying algorithm can be composed of multiple symbolic and mathematical operations with unbounded complexity, the model must infer potentially very complex behavior over long horizons.
Universal interface and model for hyperparameter optimization
In this section, we provide a universal interface for modeling HPO studies with mixed textual and numerical information as a sequence of discrete tokens.
We train our O PT F ORMER as a generative [Table]
[Table]
model on a given dataset and explain how to use the O PT F ORMER ’s parameter and function prediction abilities to implement an HPO policy.
4.1 Study tokenization
To generalize over HPO problems of different parameter sizes
[Table]
types, and metadata
[Table]
we propose to serialize the study as a one-dimensional textual sequence, also advocated in [26].
Unfortunately, a naive serialization approach, e
[Table]
g. via JSON [43], will produce unnecessarily long sequences.
To improve scalability
[Table]
we compress the textual representation of metadata m by removing redundant phrases and punctuation (e
[Table]
g., "parameter", quotes) and encoding keywords (e.g., "name", "algorithm") and enumerating types (e.g. "DOUBLE") into single tokens.
For the historical sequence h , we convert every DOUBLE and INTEGER parameter along with every function value into a single token, by normalizing and discretizing them into integers, with an quantization level of Q = 1000 ; e.g.
¯ x = int[ x norm · Q ] , where x norm =
( x − x min ) / ( x max − x min ) .
[Equation]
The range of x is deﬁned by the search space and the range of y is obtained from observed values in h .
For other types, we use the index in their value set.
The shortened text string is then converted to a sequence of tokens via the SentencePiece tokenizer [44] (see Table 2 for an example).
Every trial is represented by text, which is represented as a sequence of normalized and quantized tokens, (cid:104) ¯ x (1) t , . .
, ¯ x ( D ) t , (cid:63), ¯ y t , "|" (cid:105) , where the token (cid:63) separates parameter and function values and "|" separates trials.
See Appendix A.2 for further details on tokenization.
4.2 Model and training loss
After tokenization, the converted historical sequence is as follows:
[Equation]
We can now apply a Transformer model to learn the conditional distribution of tokens in ¯ h using the chain rule, given the metadata ¯ m , as depicted in Fig.
The joint distribution is presented in
Appendix D.1.
Given a dataset D of hyperparameter optimization studies, we train the O PT F ORMER by maximizing the weighted log-likelihood for each study ( m, h ) ∼ D :
[Equation]
, (3) with w n =
if ¯ h ( n ) ∈ { (cid:63), "|" } and w n = 1 otherwise.
That is, we mask out the separator tokens ( (cid:63) , "|") and predict parameter ¯ x and function tokens ¯ y only.
Note that ¯ h ( n ) denotes the n -th token, that is the n -th element of the list in Equation (2), and ¯ h (1: n − 1) denotes all tokens up to the ( n − 1) -th token.
Further details and data augmentations are provided in Appendix D.2.
4.3 Inference and decoding
Parameter prediction: To decode the predicted parameter token ¯ x ( d ) t back to its original parameter range, we truncate the output distribution to the vocabulary range corresponding to valid parameter values [0 , Q ) and reverse our tokenization procedure in Section 4.1.
For a DOUBLE or INTEGER parameter x , we use a piecewise constant distribution:
[Equation]
For all other parameter types, ¯ x corresponds to the index of the set of feasible values.
Putting these together, we may now sample parameter x t from the model’s prior distribution and thus deﬁne an
HPO policy:
[Equation]
As we use a supervised learning loss, we expect π prior to approximate the behavior policy π b .
Note that traditional BO algorithms require running Bayesian inference and then conducting a global search in the hyperparameter space with an acquisition function.
Thus the runtime complexity of making one hyperparameter suggestion is cubic in t for a typical GP-based BO method that performs
ARD each iteration [45].
In contrast, generating one suggestion by the O PT F ORMER consists of decoding D parameter tokens with an input sequence of ( D + 3) t tokens, which are then parsed into the D parameter values, producing a runtime of O
( D 2 t ) linear in t , with proper caching.
Function prediction: To decode the real-valued function y t from the discrete distribution
P θ (¯ y t | ¯ m, ¯ h t − 1 , ¯ x t ) , we construct the same piecewise constant distribution as in Eq.
(4) with the range [ y min , y max ] used in tokenization.
Note that the limited support of y will not be a concern for HPO when either the range is known or we set the range large enough compared to observed values.
For more general use as a few-shot function prediction model, one could consider adopting the Riemann Distribution in [40], which supports an unbounded range.
Augmented HPO policies with function prediction: At best, the learned policy π prior can only perform as well as the original policy π b when using behavioral cloning.
However, we can take advantage of the model’s simultaneous function prediction ability to improve the policy with modelbased planning or ofﬂine RL techniques.
While a comprehensive study of policy improvements for Transformers is out of the scope of this work, we consider here a simple yet effective policy improvement operator: sampling M = 100 candidate suggestions from π prior and choosing the suggestion with the highest score deﬁned by an acquisition function u ( · ) as follows:
[Equation]
Common acquisition functions include Expected Improvement (EI), Probability of Improvement (PI), Upper Conﬁdence Bound (UCB), and Thompson Sampling, see for example [46].
At a high level, this approach to combining imitated policies with function prediction is reminiscent of the idea behind the ofﬂine RL approach of BCQ [47].
Because we apply a linear mapping from the original y value to the quantized value ¯ y before discretization, we can simply deﬁne the acquisition functions on the discrete distribution P θ (¯ y | ¯ m, ¯ h t − 1 , ¯ x t ) as follows,
[Equation]
where ¯ y ∗ = max τ ≤ t − 1 ¯ y τ in EI and PI is the threshold to measure improvement. We deﬁne the UCB acquisition function with a quantile parameter α .
Our TS acquisition is deﬁned as a sampled function value at a given location from the marginal predictive distribution. It is inspired by the traditional Thompson Sampling method [45] but different in that the correlation between different locations is ignored.
Data
Training the O PT F ORMER requires HPO studies with optimization trajectories.
The most natural dataset we possess is the entire Google Vizier [2] database, one of the world’s largest collections of real world hyperparameter tuning studies, which we denote as RealWorldData . There are around 750K studies, each with on average 300 trials, covering a vast class of production and machine learning applications at Google, ranging from vision, speech, NLP and robotics, and representing one of the most representative distributions of HPO tasks for machine learning models in practice.
These studies were generated with a mixture of non-adaptive, evolutionary, and BO algorithms.
However, as the dataset does not contain sufﬁcient algorithm information, we have to treat the corresponding behavior policy as a randomly mixed algorithm π b .
In addition, we create two new datasets based on public benchmarks.
HPO-B is the largest public benchmark for HPO containing about 1.9K tuning tasks, most of which use one of 16 shared search spaces.
In the continuous evaluation setting, it ﬁts an XGBoost model to the trial data of every tuning task as the objective function.
For further control over speciﬁc function dimensions and properties, we use the blackbox optimization benchmark BBOB [48], consisting of 24 types of synthetic functions with customizable properties (dimension sizes, rotations, shifts, discretizations, noise types) we randomize over.
For each of the two public benchmarks (HPO-B and BBOB), we apply a ﬁxed set of 7 HPO algorithms to generate a dataset of optimization trajectories.
In contrast to RealWorldData, we specify the algorithm name in the metadata m as part of the conditioning input for our model.
The controlled algorithms used are: (1) Grid Search, (2) Shufﬂed Grid Search, (3) Random Search, (4) Regularized
Evolution [49], (5) Hill-Climbing, (6) Eagle Strategy [50], and (7) Vizier’s GP-UCB [2].
Appendix B contains detailed explanations of the algorithms.
("R") RealWorldData ("H")
HPO-B ("B") BBOB #Studies 750K 10M 10M #Trials / study 300 (on average) Study source Google’s database Generated
Generated π b
Mixed
Controlled
Controlled
Obj.
Functions
HPO tasks
HPO tasks
Synthetic
Search space
Different per task 16 shared search spaces
Randomized
Experiments
We train a single Transformer model with 250M parameters on the union of the three datasets described above, RealWorldData, HPO-B, and BBOB (hyperparameter details in Appendix D.2).
Each dataset contains a corresponding “test” set of functions, either using synthetic functions (BBOB) or ﬁtting a machine learning model to obtain the objective (RealWorldData, HPO-B).
We evaluate mainly on the two natural HPO benchmarks, RealWorldData and
HPO-B.
The train/test subsets of
RealWorldData are split temporally to avoid information leak (see Appendix C for details).
To aggregate results across functions with different output scaling, we normalize all the test functions.
This is standard practice in the literature [2, 5, 51–54].
We deﬁne our performance metric at trial t as the best-so-far normalized function value max i ∈{ 1: t } ( y i − y rand ) /
( y max − y rand ) , where y rand is the median of function values randomly sampled in the search space to be robust to outliers, and y max is the maximum, if known, or best value found by any algorithm.
For the HPO-B benchmark, we use the recommended bounds provided in [5].
We also consider other metrics when comparing different algorithms in Appendix E.3, including the performance proﬁle and average ranking.
We ﬁnd our results are consistent over different metrics.
Because the O PT F ORMER is trained to predict the conditional distributions of parameter and function values, we would like to answer the following questions when evaluating on unseen test problems: [Figure] Evolution
[Figure] Can the O PT F ORMER learn to imitate multiple HPO algorithms with one model?
(Section 6.1) 2.
Can the O PT F ORMER learn a good prior over hyperparameter response functions?
(Section 6.2) 3.
Is the O PT F ORMER a competitive approach for HPO?
(Section 6.3) 6.1 Imitating HPO policies
We ﬁrst evaluate how well the O PT F ORMER can learn the conditional distribution of parameter suggestions given by the behavior policies in the dataset, and how well it can imitate multiple algorithms.
As the algorithm’s name is contained in the metadata m , we can modify the behaviour of the policy π prior ( x t +1 | m, h t ) simply by altering this variable.
Fig.
2a compares two different policies to the O PT F ORMER , when it is conditioned on the corresponding policy name.
We observe a good match between the imitated algorithms and the O PT F ORMER (additional algorithms are shown in
Appendix E.1).
In Fig.
2b we run target policies on the BBOB dataset’s test functions and compare the optimization trajectories of the algorithms and the O PT F ORMER .
In Fig.
2c we compare the average and standard deviation of the best normalized function values at trial 100.
Our model imitates most algorithms very accurately in both the mean and variance except for the most complicated algorithm, Vizier, where π prior is slightly worse in the LUNACEK benchmark.
We expand on this in Appendix E.1.
Because Vizier is the best performing HPO algorithm among all considered, the O PT F ORMER will imitate Vizier faithfully, although not perfectly, in the following experiments.
6.2 Learning priors for hyperparameter response functions
In this section, we assess the O PT F ORMER ’s ability to learn the conditional distribution of the function value as a few-shot function regressor.
Speciﬁcally, for every function in each test dataset, we repeatedly sample up to 200 random trials ( x 1 , y 1 , . . .
x t , y t ) , t ≤ 200 , and predict the conditional distribution p ( y t | x 1 , y 1 , . . .
, x t ) .
We compare with a GP model with output warping — details provided in Appendix B.
We report the log-predictive likelihood log p ( y t | x t , . . . )
in Table 4.
As uncertainty estimation is important for HPO, we also evaluate how well the function predictive distribution is calibrated.
When a predictive distribution p θ ( y | . . . ) matches the true distribution, the estimated CDF F ( y )
= (cid:82) y
−∞ p θ ( y (cid:48) | . . . ) dy (cid:48) will be uniformly distributed.
In Fig.
3, we plot the cumulative histogram of F ( y ) on RealWorldData test set and check the deviation from the diagonal line to assess goodness-of-ﬁt as proposed by Rosenblatt [55].
The O PT F ORMER has a smaller [Table] [Figure] [Table]
[Figure]
[Table]
[Figure] w [Table] [Figure]
h
[Table]
[Figure]
[Table]
[Figure]
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OptFormer
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deviation than the GP almost across the entire range.
We also compare calibration performance using the expected calibration error (ECE) [27].
Readers are referred to [27] and Appendix E. for a detailed explanation of ECE.
We observe from Table 4 that the O PT F ORMER achieves better predictive likelihood and ECE than the GP on both datasets.
6.3 Augmenting a prior policy with function prediction
We evaluate the O PT F ORMER as a hyperparameter optimization algorithm on two benchmarks,
RealWorldData and HPO-B.
We compare our prior policy, the O PT F ORMER , and an augmented policy with Expected Improvement, the O PT F ORMER (EI), against standard HPO baselines, including
Random Search, our implementation of GP-UCB, and the well-tuned Vizier service.
For HPO-B, we also include the GP (not to be confused with our GP-UCB) and DGP (GP with deep kernel) baseline results provided by the original paper [5].
Additionally, we include three recent transferlearning methods based on multi-task GP models: ABLR [12, 56], FSBO [7], and HyperBO [57, 58] (implementation details in Appendix B).
Please note that all of these transfer learning methods require learning GPs on multiple tasks sharing the same search space.
Therefore, none of them apply to the
RealWorldData benchmark where every study has its own search space.
We show the trajectory of the best normalized function value averaged over all functions from each benchmark in Fig.
While the prior policy returned by the O PT F ORMER does not perform as well as Vizier, it is comparable or slightly better than our GP-UCB baseline and ABLR. [Figure]
(c) The prior policy improves performance with or without the Expected Improvement acquisition function.
(d) All acquisition functions provide a signiﬁcant improvement.
The most signiﬁcant improvement is achieved when we augment our prior policy with the Expected
Improvement acquisition function.
The resulting O PT F ORMER (EI) outperforms all baselines across the board on both benchmarks.
This illustrates that the O PT F ORMER is able to learn the distribution of functions in the meta-training split and transfers to the meta-testing split.
It is worth noting that to run 100 trials for about half of the test functions, the required history token sequence is longer than the 1024-token length used in training, with the maximum length about twice the training horizon.
The superior performance of the O PT F ORMER (EI) thus demonstrates its good generalization performance beyond the optimization horizon it is trained for.
6.4 Ablations
We provide further ablations on three important components for our policy:
Training dataset.
To understand the impact of the training datasets on the O PT F ORMER , we train three variants on individual datasets (O PT F ORMER -"R","H","B" respectively for RealWorldData,
HPO-B, BBOB) and study their transfer learning performances on HPO-B. Fig.
5a veriﬁes that training with in-domain data ("H") gives better performance than training over the more diverse across-domain RealWorldData HPO dataset ("R"), which is better than training over the synthetic BBOB data ("B").
Nonetheless, training on RealWorldData is enough to give comparable performance to the best transfer learning baseline at the end of 100 trials.
Lastly, training on all of the datasets (O PT F ORMER ) gives a further advantage over O PT F ORMER -H. This suggests that more data does not hurt the model’s performance but rather may improve it, even if the extra data is out-of-domain.
Meta-data m .
We have demonstrated how the O PT F ORMER ’s behavior can be controlled by the algorithm name in metadata m in Section 6.1. Here we study whether the O PT F ORMER learns to depend on other meta information.
At inference time, we provide minimum information in m (O PT F ORMER -min) by excluding all textual information and parameter value ranges.
We only keep necessary information such as parameter types and algorithm names.
Fig.
5b shows that the prior policy of O PT F ORMER -min performs comparably with the O PT F ORMER , partly due to the use of data augmentation (see Appendix D.2).
The augmented policy O PT F ORMER -min (EI) (dashed orange) improves upon the prior policy but is signiﬁcantly worse than the full model, suggesting that the missing metadata impacts the model’s predictions on function values.
Prior policy.
Section 6.3 demonstrated the beneﬁt of adding an acquisition function to the prior policy.
A natural question is whether a good prior policy is needed at all.
In Fig.
5c, we replace the prior policy in the O PT F ORMER (EI) with random search (Random Search (EI), dashed blue line).
While adding Expected Improvement still improves this random search policy’s performance, the best method requires both a good prior policy and the acquisition function. Choice of acquisition function.
In Fig.
5d, we compare the Expected Improvement (EI) with
Thompson Sampling (TS), Probability of Improvement (PI), and Upper Conﬁdence Bound (UCB) with a conﬁdence level of 0.9.
We observe that the prior policy is improved by all the acquisition functions.
Particularly, O PT F ORMER (EI) is the best among all the choices though the difference is relatively small compared to the advantage over other baselines and O PT F ORMER prior policy.
We provide additional analysis with results on both the RealWorldData and HPO-B datasets, as well as other evaluation metrics in Appendix E.4.
Limitations and future extensions
We list a few limitations of this work and discuss some potential extensions.
(1) We did not consider parameters that do not always apply or are subject to dynamic constraints depending on other parameter values.
Such parameters are common in AutoML [59] and NAS applications [60].
Our work can be extended to support these applications, by providing the conditional speciﬁcations as text in metadata m .
(2) We also considered only sequential optimization with a batch size of 1.
To support parallel suggestions, one could apply random masking to input function value observations to simulate scenarios with parallel pending trials [33].
(3) While we trained the Transformer to clone the behavior policy ofﬂine, there are extensive literature on ofﬂine RL [29] that could be applied here [25, 47, 61–64].
One could also consider meta-training acquisition functions as in [31] within the same model and online ﬁne-tuning as in [7, 41].
(4) We considered a single objective function, though multiple objectives can be easily included by outputting multiple function tokens in a trial.
The maximum sequence length is limited by the quadratic memory size requirement of a Transformer, which could be mitigated with more scalable architecture variants such as Performer [65].
Conclusion
We presented ﬁrst step to learning a universal Transformer model for hyperparameter optimization from large scale datasets containing tuning experiments with vastly different search spaces and experiment descriptions.
By training on a diverse set of synthetic and real-world tuning trajectories, we demonstrated the capacity of a single Transformer model to imitate 7 fundamentally different
HPO policies, learn to make well calibrated few-shot function predictions, and provide competitive optimization performance on unseen test functions comparable with the existing, long-tried GP-based baselines.
Many extensions are readily conceivable for future exploration. Acknowledgments
We would like to thank Chris Dyer, Luke Metz, Kevin Murphy, Yannis Assael, and Esteban Real for providing valuable feedback during their reviews of this paper.
We further thank Sebastian Pineda
Arango for technical discussions on the HPO-B benchmark and Christof Angermueller on biological benchmarks.
In addition, we thank Daniel Golovin, Daiyi Peng, Yingjie Miao, Jack Parker-Holder,
Jie Tan, Lucio Dery, and Aleksandra Faust for multiple useful conversations.