AutoML: A Survey of the State-of-the-Art

Kaiyong Zhao; Xiaowen Chu; Xin He

REVIEW 3 major objections 5 minor 1 cited by

AutoML: A Survey of the State-of-the-Art

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read This survey claims that a single document can organize the entire AutoML pipeline, from data preparation through model evaluation, and that this full-pipeline view is what earlier surveys lacked.

desk verdict A broad, readable AutoML survey whose comparative NAS tables are the main value but also the main liability: treat the tables as a rough map, not a ranking. read the letter →

arxiv 1908.00709 v6 pith:A3U7ESYA submitted 2019-08-02 cs.LG cs.CVstat.ML

Xin He , Kaiyong Zhao , Xiaowen Chu This is my paper · ORCID

classification cs.LGcs.CVstat.ML

keywords automatedmachinelearningneuralarchitecturesearchhyperparameteroptimizationdatapreparationfeatureengineeringmodelevaluationdeeppipelinesurvey

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper is a survey that tries to establish a broad, current map of automated machine learning (AutoML) organized by the stages of the deep-learning pipeline: data preparation, feature engineering, model generation, and model evaluation. Its distinctive claim is coverage: unlike prior surveys that treat only neural architecture search (NAS) or only hyperparameter optimization, it covers all four stages in one document, with the most space given to NAS as the currently dominant sub-topic. The paper also compiles the reported accuracy and GPU-day cost of representative NAS algorithms on CIFAR-10 and ImageNet, and uses that snapshot to discuss one- versus two-stage NAS, one-shot NAS, joint hyperparameter and architecture optimization, and resource-aware search. A sympathetic reader would value this as an entry point that supplies the field's vocabulary, taxonomy, and open problems in one pass. The survey's own caveat is that the compiled comparisons are not perfectly fair because methods were evaluated under different settings.

What carries the argument

The load-bearing device is the AutoML pipeline itself: a four-stage diagram (data preparation, feature engineering, model generation, model evaluation) that the survey uses as its table of contents. Within model generation, the paper's NAS taxonomy, which places every algorithm along three dimensions (search space, architecture optimization method, and model evaluation method), does the analytical work of organizing a large literature. The comparison tables use the GPU-day metric, defined as $N \times D$ where $N$ is the number of GPUs and $D$ the number of days, as the standard unit of search cost.

What would settle it

Run a representative set of the Table 3 and Table 4 methods, such as DARTS, ENAS, RandomNAS, AmoebaNet-B, and P-DARTS, on identical GPUs, epochs, batch sizes, and augmentation settings, then compare the rank order by accuracy and GPU days; if the reported order changes materially, the survey's comparative claims fail.

Watch

Extended reading notes

Core claim

The paper's central claim is that it covers a broader range of AutoML methods than earlier surveys [10, 45, 46, 9, 8], because it follows the complete pipeline rather than a single sub-topic. On its own terms, it establishes an organizational scheme: data preparation (collection, cleaning, augmentation), feature engineering (selection, construction, extraction), model generation (search space plus optimization), and model evaluation (low fidelity, weight sharing, surrogates, early stopping). Within NAS it reports that search spaces can be entire-structured, cell-based, hierarchical, or morphism-based, and that architecture optimization methods divide into evolutionary algorithms, reinforcement learning, gradient descent, surrogate-model-based optimization, grid and random search, and hybrids. It further argues that gradient-based and random-search methods now achieve results comparable to earlier RL and EA methods at a fraction of the cost, and that one-shot and weight-sharing NAS exhibit a bias that decoupled optimization tries to fix. The paper concludes by listing open problems: flexible search spaces, interpretability, reproducibility, robustness, joint hyperparameter and architecture optimization, complete pipeline systems, and lifelong learning.

Load-bearing premise

The survey's comparative snapshot of NAS methods assumes that accuracy and GPU-day figures collected from different papers, using different hardware, training budgets, and augmentation schemes, are comparable enough to support the trends it draws.

Editorial extensions

If this is right

A newcomer can acquire from one document an organized picture of AutoML's complete pipeline rather than stitching together separate surveys for NAS, hyperparameter optimization, and feature engineering.
The compiled tables give a rough cost-versus-accuracy landscape: early reinforcement-learning and evolutionary searches cost thousands of GPU days, while differentiable and random-search approaches complete in under a day with competitive accuracy.
The one-shot and weight-sharing distinction implies that NAS results should be evaluated for rank correlation between search and evaluation stages, not only final accuracy, and that the Kendall Tau metric is a meaningful diagnostic.
The open-problems list frames concrete research targets: flexible search spaces free of human bias, interpretable search decisions, reproducible NAS benchmarks, robustness to noisy and adversarial data, and joint optimization of hyperparameters and architectures.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the breadth claim holds, then AutoML is best understood as a pipeline-integration problem rather than a single algorithm family, which suggests that progress in one stage, such as data augmentation, can shift the value of methods in another stage.
The paper's own fairness caveat implies that Tables 3 and 4 should not be used to rank methods; a reader should treat them as existence proofs and cost anecdotes until controlled benchmarks settle the order.
A natural testable extension of the survey's logic is to build a single benchmark suite that varies training settings and augmentation across all four pipeline stages, extending the NAS-Bench idea to data preparation and feature engineering.
The competitive performance of random search reported for NAS suggests that the field's real advantage may lie in search-space design and evaluation fidelity rather than in the optimizer itself, a hypothesis the survey does not fully pursue.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The paper is a survey, and it does what a survey should: it organizes AutoML along the pipeline—data preparation, feature engineering, HPO, NAS—and covers enough ground that a newcomer could get a genuine roadmap from one document. The discussion of one/two-stage, one-shot, and joint architecture-hyperparameter search is clear, and the key equations (DARTS relaxation, GPU Days, MnasNet objective) are reproduced faithfully. The claim of broader coverage than earlier surveys holds up. That is the real value here: consolidation, not novelty.

The soft spots are concentrated in the performance comparison. Tables 3 and 4 have at least two probable transcription errors: DARTS on ImageNet listed as 73.3/81.3 top-1/top-5, while the original paper reports top-5 near 91.3, and RENASNet at 91.12 on CIFAR-10, which sits far below every neighboring row and is almost certainly 97.12. The GPU Days metric also mixes K40, P100, 1080Ti, and V100 without normalization, so a day on an 800-GPU K40 cluster is not comparable to a day on one 2080Ti. The authors do concede in Section 6.1.2 that the comparison is not quite fair, but the caveat comes after the tables have already been presented as a global comparison, and the qualitative conclusions in Section 6.1 (gradient methods reduce cost, random search is competitive) lean on those same entries. A reader using the tables to rank methods could be misled.

None of this sinks the survey. The descriptive content is generally accurate, the organization is useful, and the open-problems section is thoughtful. These are fixable problems—verify every table entry against the source, add a hardware note to the GPU Days definition, and flag known typos. But as published, the tables are the weakest link, and they happen to be the part most likely to be lifted.

For a serious referee: yes, this deserves peer review. The survey has real pedagogical value, and the flaws are emendable rather than fatal. I would ask for table verification and a more prominent disclaimer before acceptance. For your own work: I would not cite it as a source for specific accuracy numbers, but it is a reasonable survey to point a student toward for the bird's-eye view.

Referee Report

3 major / 5 minor

Summary. This manuscript is a survey of automated machine learning organized around the ML pipeline: data preparation, feature engineering, model generation, and model evaluation. The largest part is devoted to neural architecture search, covering search spaces, architecture optimization methods, model-evaluation acceleration, and a comparative summary of representative NAS algorithms on CIFAR-10 and ImageNet. It also discusses one/two-stage NAS, one-shot NAS, joint hyperparameter and architecture optimization, resource-aware NAS, and several open problems. The authors' stated contribution is breadth relative to earlier surveys, which they support with a comparison table of existing surveys.

Significance. If the factual content is corrected, the survey would be a useful single-document entry point: it compiles a wide set of references across all pipeline stages, reproduces several central equations (DARTS relaxation, Eq. 4; bilevel objective, Eq. 5; Gumbel-Softmax, Eq. 8; MnasNet objective, Eq. 12) faithfully, and organizes NAS topics in a way that is helpful for newcomers. The main added value beyond earlier NAS-focused surveys is the pipeline-level coverage and the compiled comparison tables in Tables 3 and 4. The usefulness of those tables, however, is the weakest point: several entries appear to be transcription errors, and the resource metric is hardware-incomparable. These issues are fixable, so they do not invalidate the survey's organizational contribution, but they do require substantive revision.

major comments (3)

[Table 4, DARTS row] Table 4 lists DARTS (searched on CIFAR-10) as achieving ImageNet top-1/top-5 accuracy of 73.3/81.3. The original DARTS paper (Liu et al., ICLR 2019) reports a top-5 accuracy of about 91.3% in this setting, so the 81.3 entry is almost certainly a transcription error. Because Tables 3 and 4 are presented as a global performance comparison and feed the qualitative conclusions in Section 6.1, this error should be corrected and the tables should be checked against the primary sources.
[Table 3, RENASNet row] Table 3 lists RENASNet+c/o at 91.12% top-1 accuracy on CIFAR-10, while every neighboring method is in the 96–98% range. This is almost certainly a misprint for 97.12; as printed, it would place an EA+RL method far below its actual performance and distort the method-class comparison in Section 6.1.
[Section 6.1, Eq. (9)] GPU Days defined in Eq. (9) as N×D is not comparable across rows because Tables 3 and 4 mix K40, P100, 1080Ti, Titan, Titan Xp, V100, and 2080Ti hardware with no normalization, and many entries omit the GPU type entirely. The caveat in Section 6.1.2 that 'the comparison is not quite fair' is useful, but it appears only after the tables, and the qualitative claims in Section 6.1 about gradient-descent methods reducing resource use and random search being comparable rely precisely on this resource measure. Please add per-row hardware flags, a normalization footnote, or restrict the resource-efficiency conclusions to entries with comparable hardware.

minor comments (5)

[Figure 2] The flow chart contains the typo 'exsiting' in the decision box; it should read 'existing'.
[Section 6.1.1/6.1.2 numbering] The discussion of Tables 3 and 4 and the comparison caveat appears under the heading 'NAS-Bench Dataset' (Section 6.1.2) before the actual NAS-Bench discussion begins; consider restructuring so the caveat and table caveats precede the benchmark discussion.
[Eqs. (2) and (3)] The notation 'MB' and 'ML' in Eqs. (2) and (3) should be typeset as M^B and M^L to avoid ambiguity about whether the exponent applies to the multiplication or to M.
[Section 7.7] The name 'Auto-WEAK' should be 'Auto-WEKA', matching the referenced system by Thornton et al.
[Table 5] The entry 'Federate Learning' should be 'Federated Learning'.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the survey makes no derived predictions and fits no parameters, so its descriptive claims cannot reduce to their own inputs.

full rationale

This paper is a literature survey, not a derivation. Its load-bearing claim is that it covers a broader range of AutoML methods than prior surveys, which is supported by the explicit comparison matrix in Table 1 and by the paper's organization along the AutoML pipeline (data preparation, feature engineering, hyperparameter optimization, neural architecture search, and model evaluation). No quantity is fitted to data and then re-reported as a prediction; the only numerical comparisons, in Tables 3 and 4, are transcriptions of results from external papers, explicitly flagged by the authors in Section 6.1.2 as obtained 'under different settings' and therefore 'not quite fair.' That caveat concerns comparability and accuracy, not circularity. The paper does cite prior work, including some surveys by other authors, but none of those citations supply a load-bearing premise that is equivalent to the paper's own conclusion; self-citation is essentially absent from the argument. The NAS performance tables could contain transcription or normalization issues, as the skeptical note observes, but that is a correctness risk, not a circularity defect: reproducing a number from an external source and labeling it as an external result is the opposite of renaming that number as a prediction. Accordingly, the appropriate finding is no significant circularity, with score 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The survey introduces no fitted parameters and no new entities. Its central claims of breadth and comparison rest on the reliability of cited literature, the meaningfulness of GPU Days as a proxy, and the representativeness of the selected methods.

assumptions (3)

domain assumption Cited papers accurately report their own results and settings, including accuracies, parameter counts, and GPU days.
The survey's tables compile values from original papers without re-running experiments, so it inherits the accuracy of the cited reports.
domain assumption GPU Days, defined as the number of GPUs times the number of days, is a meaningful proxy for search efficiency across papers.
Section 6 defines and uses GPU Days, while Section 6.1.2 acknowledges that different papers used different GPU types and training settings, weakening comparability.
domain assumption The chosen set of representative methods for each category reflects the state of the art at the time of writing.
The survey's breadth claim depends on the inclusion and selection of papers; an unrepresentative selection could bias the overview.

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Cite this review

Pith. "Pith review of AutoML: A Survey of the State-of-the-Art." pith.science (2026). https://pith.science/paper/A3U7ESYA

@misc{pith2026190800709,
  author       = {Pith},
  title        = {Pith review of: AutoML: A Survey of the State-of-the-Art},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/A3U7ESYA}},
  note         = {Machine review of arXiv:1908.00709}
}

read the original abstract

Deep learning (DL) techniques have penetrated all aspects of our lives and brought us great convenience. However, building a high-quality DL system for a specific task highly relies on human expertise, hindering the applications of DL to more areas. Automated machine learning (AutoML) becomes a promising solution to build a DL system without human assistance, and a growing number of researchers focus on AutoML. In this paper, we provide a comprehensive and up-to-date review of the state-of-the-art (SOTA) in AutoML. First, we introduce AutoML methods according to the pipeline, covering data preparation, feature engineering, hyperparameter optimization, and neural architecture search (NAS). We focus more on NAS, as it is currently very hot sub-topic of AutoML. We summarize the performance of the representative NAS algorithms on the CIFAR-10 and ImageNet datasets and further discuss several worthy studying directions of NAS methods: one/two-stage NAS, one-shot NAS, and joint hyperparameter and architecture optimization. Finally, we discuss some open problems of the existing AutoML methods for future research.

Figures

Figures reproduced from arXiv: 1908.00709 by the authors.

**Figure 1.** An overview of AutoML pipeline covering data preparation (Section 2), feature engineering (Section 3), model generation (Section 4) [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

**Figure 2.** The flow chart for data preparation. 2.1. Data Collection ML’s deepening study has led to a consensus that highquality datasets are of critical importance for ML; as a result, numerous open datasets have emerged. In the early stages of ML study, a handwritten digital dataset, i.e., MNIST [48], was developed. After that, several larger datasets like CIFAR-10 and CIFAR-100 [49] and ImageNet [50] were developed. A var… view at source ↗

**Figure 3.** A classification of data augmentation techniques. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (16 more)

**Figure 4.** Figure 4: The iterative process of feature selection. A subset of [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 5.** Figure 5: An overview of neural architecture search pipeline. [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

**Figure 6.** Figure 6: Two simplified examples of entire-structured neural archi [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]

**Figure 7.** Figure 7: (Left) Example of a cell-based model comprising three [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]

**Figure 8.** Figure 8: Difference between DARTS [17] and P-DARTS [128]. Both methods search and evaluate networks on the CIFAR-10 dataset. As the number of cell structures increases from 5 to 11 and then 17, the number of candidate operations is gradually reduced accordingly. search phase fi…

**Figure 10.** Figure 10: Example of a three-level hierarchical architecture rep [PITH_FULL_IMAGE:figures/full_fig_p009_10.png]

**Figure 9.** Figure 9: Network-level search space proposed by [ [PITH_FULL_IMAGE:figures/full_fig_p009_9.png]

**Figure 12.** Figure 12: Net2DeeperNet and Net2WiderNet transformations in [PITH_FULL_IMAGE:figures/full_fig_p010_12.png]

**Figure 13.** Figure 13: Overview of the evolutionary algorithm. each network can be modified using function-preserving network morphism operators. Hence, the child network has increased capacity and is guaranteed to perform at least as well as the parent networks. Four Steps. A typical EA co…

**Figure 14.** Figure 14: Overview of neural architecture search using reinforcement [PITH_FULL_IMAGE:figures/full_fig_p012_14.png]

**Figure 15.** Figure 15: Example of a controller generating a cell structure. Each [PITH_FULL_IMAGE:figures/full_fig_p012_15.png]

**Figure 16.** Figure 16: Overview of DARTS. (a) The data can only flow from lower-level nodes to higher-level nodes, and the operations on edges are [PITH_FULL_IMAGE:figures/full_fig_p014_16.png]

**Figure 17.** Figure 17: Examples of grid search (left) and random search (right) in [PITH_FULL_IMAGE:figures/full_fig_p015_17.png]

**Figure 18.** Figure 18: Illustration of two- and one-stage neural architecture [PITH_FULL_IMAGE:figures/full_fig_p020_18.png]

**Figure 19.** Figure 19: (Left) One-shot models. (Right) Non-one-shot models. [PITH_FULL_IMAGE:figures/full_fig_p021_19.png]

**Figure 20.** Figure 20: State-of-the-art models on the PTB dataset. The lower the [PITH_FULL_IMAGE:figures/full_fig_p024_20.png]

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Forward citations

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