ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data

## Abstract

Abstract In settings where only a budgeted amount of labeled data can be afforded, active learning seeks to devise query strategies for selecting the most informative data points to be labeled, aiming to enhance learning algorithms’ efficiency and performance. Numerous such query strategies have been proposed and compared in the active learning literature.
However, the community still lacks standardized benchmarks for comparing the performance of different query strategies.
This particularly holds for the combination of query strategies with different learning algorithms into active learning pipelines and examining the impact of the learning algorithm choice. To close this gap, we propose ALPBench , which facilitates the specification, execution, and performance monitoring of active learning pipelines. It has built-in measures to ensure evaluations are done reproducibly, saving exact dataset splits and hyperparameter settings of used algorithms. In total, ALPBench consists of 86 real-world tabular classification datasets and 5 active learning settings, yielding 430 active learning problems. To demonstrate its usefulness and broad compatibility with various learning algorithms and query strategies, we conduct an exemplary study evaluating 9 query strategies paired with 8 learning algorithms in 2 different settings. We provide ALPBench here: https://github.com/ValentinMargraf/ActiveLearningPipelines .

## 1 Introduction

Tabular data is one of the most commonly used data types across various sectors, including medicine [], finance, insurance [] or manufacturing []. Therefore, it is also one of the most frequently utilized data types for real-world machine learning (ML) applications []. However, generally speaking, data must first be labeled before it can be used in supervised ML tasks. Labeling data is a cumbersome and costly process, as it is time-consuming and typically accomplished by human domain experts.

For situations where only a limited budget is available for labeling datasets, the field of active learning (AL) [, ] has emerged to develop methods for actively selecting the most suitable data points from unlabeled data to be labeled by a so-called oracle.
The term ”most suitable“ here refers to data points that help achieve the best possible generalization performance for a given learning algorithm.
A diverse array of query strategies have been developed that somehow quantify the suitability of a data point being labeled.
Which QS works best heavily depends on various factors, including the dataset, the budget constraints, the learning algorithm, among other things [, , ].

However, while several empirical evaluations have already been conducted [, , , , ], the community still lacks a standardized benchmark for comparing the performance of different query strategies, providing an open-source implementation that facilitates their comparison. Moreover, the existing evaluations are often limited in the number of datasets, considering only binary classification datasets or already outdated query strategies [, ]. Further studies only consider one particular learning algorithm [, , ], which might lead to biased results, as the algorithm also influences the performance of a query strategy. Lastly, these learning algorithms often do not properly represent state-of-the-art methods. While gradient-boosted decision tree (GBDT) ensembles, such as XGBoost [] or Catboost [], as well as deep learning architectures [, ] have proven particularly successful for tabular data, they are not included in these studies.

#### Contributions.

Thus far, a comprehensive benchmark to investigate the benefits of different query strategies for active learning, especially in combination with the choice of learning algorithms, is still lacking.
In this work, we address this gap by proposing ALPBench, a comprehensive benchmark for active learning pipelines, comprising query strategies and learning algorithms, in the domain of tabular data classification tasks.
The benchmark includes a total of 86 different multi-class and binary-class tabular datasets and 5 different active learning problem settings, specifying the amount of initially labeled data points, the budget per iteration, and the number of iterations. Overall, as visualized in Figure 1, our contributions are threefold, and can be summarized as follows:

- 1.
We propose ALPBench, the first active learning benchmark for tabular data that allows for combining different learning algorithms and query strategies into active learning pipelines to execute them, monitor their performance, and benchmark against other pipelines.
- 2.
We provide an open-source implementation of ALPBench as an extensible Python package for applying and benchmarking active learning pipelines. In an experimental study we showcase its usefulness by evaluating 72 different active learning pipelines on 86 real-world classification datasets across 2 settings.
- 3.
We find that strong active learning pipelines are often combining state-of-the-art classification methods, such as TabPFN [], Catboost [] or Random Forest [], as learning algorithms and information-based query strategies. While the learning algorithm turns out to be the most crucial choice, the suitability of query strategies also varies for different datasets. This underlines the need of also considering different learning algorithms in future empirical studies in active learning.

Figure: Figure 1: The contributions of our paper are threefold: (i) the first active learning benchmark considering pipelines of query strategies and learning algorithms, (ii) an extensible Python package for applying and benchmarking active learning pipelines, and (iii) an extensive empirical evaluation of active learning pipelines.
Refer to caption: x1.png

## 2 Related Work

There exists some literature evaluating active learning pipelines on tabular data classification tasks.
An early benchmark of AL demonstrated that margin sampling (MS) often outperforms other qs [] in combination with logistic regression (LR) as a learning algorithm. The performance of combining varying learning algorithms and qs was investigated in [, , ]. However, the studies are outdated since the chosen algorithms are pretty simplistic, i.e., there are stronger ML algorithms nowadays [, ], and many of the used datasets from the UCI repository [] are rather old.

More recent qs were investigated in [, , ]. Although varying the strategy for instance selection, the learning algorithm is fixed, precisely a support vector machine (SVM) [, ] or LR []. However, as the employed learner is crucial to the overall performance of AL [], such design choice raises the question of whether the findings generalize to other learners as well. Further, their scope is limited to binary or only a handful of multi-class datasets.

All mentioned papers so far only considered one specific AL setting, i.e., the size of the initially labeled pool and the budget. [] initially provided only one labeled instance for each class, compared to, e.g., [], which randomly sampled 20 instances for the labeled pool. These misalignments across different benchmarks complicate comparisons and hinder the ability to draw general conclusions. [] are the first ones addressing this issue by investigating three different AL settings. They also considered very recent qs and datasets from the OpenML-CC18 Benchmark Suite []. However, again, the authors chose one specific learner, in this case, a deep neural network.

Motivated by recent papers such as [, ], we believe that an up-to-date benchmark has to include multiple state-of-the-art (SOTA) learning algorithms for tabular data such as GBDTs (e.g., Catboost) and prior-fitted networks (e.g., TabPFN []) as well as recent qs, e.g., power margin sampling and power BALD []. To the best of our knowledge, we are the first to combine various SOTA learning algorithms with qs and evaluate their performance on a large amount of binary and multi-class real-world classification tasks for tabular data. Furthermore, we provide our implementation as a Python package with a unified API.

## 3 Pool-based Active Learning

In pool-based AL instances from the pool of unlabeled data are selected to be labeled by an oracle, which is done in an iterative procedure. Three different scenarios are commonly considered in AL, namely, the membership-query synthesis, stream-based, and pool-based scenario []. We focus on the pool-based scenario, being the preferred one in real-world applications []. We first describe this scenario in Section 3.1 before elaborating on the implemented learning algorithms and qs within ALPBench (Sections 3.2 and 3.3), and their combination into active learning pipelines (Section 3.4).

### 3.1 Problem Definition

In a classification setting, we are given a $d$-dimensional feature space $\mathcal{X}\in\mathbb{R}^{d}$ and a label set $\mathcal{Y}=\{1,...,C\}$. A dataset (DS) is denoted as $\mathcal{D}=\{(\mathbf{x}_{i},y_{i})\}_{i=1}^{n}\subset\mathcal{X}\times
\mathcal{Y}$, where each instance $\mathbf{x}_{i}=(x_{i}^{1},\ldots,x_{i}^{d})\in\mathcal{X}$ is associated with an underlying true label $y_{i}\in\mathcal{Y}$.
In AL, however, only a small DS $\mathcal{D}_{L}^{0}=\{(\mathbf{x}_{i},y_{i})\}_{i=1}^{l}$ is initially labeled, whereas a considerably larger pool of instances $\mathcal{D}_{U}=\{(\mathbf{x}_{i})\}_{i=l+1}^{n}$ is unlabeled. From this unlabeled pool, instances to be labeled by the oracle $\mathcal{O}$ are selected by a QS. More specifically, the goal is to strategically select instances such that the predictive (probabilistic) model $h:\mathcal{X}\rightarrow\mathbb{P}(\mathcal{Y})$ induced by the learning algorithm on the labeled data minimizes the generalization error (risk) with respect to a given loss function $\ell:\mathcal{Y}\times\mathbb{P}(\mathcal{Y})\rightarrow\mathbb{R}^{+}$. Here, $\mathbb{P}(\mathcal{Y})$ denotes the space of probability distributions over $\mathcal{Y}$. A given budget of $B$ can be spent for labeling, meaning that $B$ instances from $\mathcal{D}_{U}$ can be chosen and queried to $\mathcal{O}$. In the pool-based scenario, a predefined amount of $R$ instances is queried per iteration ($R\leq B$) and added to the current labeled DS $\mathcal{D}_{L}^{i}$, on which the learning algorithm is run to induce an updated model $h$.

### 3.2 Learning Algorithms

The choice of the learning algorithm is quite important for the overall success of AL []. However, existing benchmarks typically fix a single learning algorithm, such as a deep neural network (DNN) [] or an SVM [], and recommend suitable qs for this choice. To reveal insights for suitable qs based on different learning algorithms, we investigate a variety of models. In particular, we choose the following models, which cover a wide range of model types and include SOTA learning algorithms for tabular data []: SVM, k-nearest neighbor (k-NN), random forest (RF), extremely randomized trees (ETC), LR, and naïve Bayes (NB) represent the group of base learners. For each of them, we implement multiple instantiations with different parameter settings. Further, we choose two GBDTs, namely XGBoost (XGB) and Catboost. Finally, we include a multi-layer perceptron (MLP) and TabNet [] as representatives of DNNs, and TabPFN [] representing PFNs in ALPBench.

### 3.3 Query Strategies

Query strategies (qs) can be classified into information-based (Info.), representation-based (Repr.), and hybrid strategies (Hybr.) [, ]. Info.-based strategies leverage the predictions of the learning algorithm to select instances where the learner exhibits uncertainty, as from these instances we expect the most informative insights. Repr.-based strategies rely solely on the structure of the data to identify the most representative instances. Hybr. strategies combine both of the aforementioned strategies.

Formally, let $z_{i}\in\mathcal{Z}$ either be a raw input instance, or its embedding of a neural network, $p_{i}\in\mathcal{P}$ the predicted class probabilities of a learning algorithm for that instance and $\{(\mathbf{x}_{i})\}_{i=1}^{\mathcal{R}}\subseteq\mathcal{D}_{U}$ the pool of instances that is queried by the QS in each iteration. Loosely speaking, info.-based approaches select instances based on some uncertainty measure $u(\cdot)$ on the probability scores, repr.-based compute representativeness $r(\cdot)$ leveraging the structure of $\mathcal{Z}$; hybr. approaches combine both:

$$ $\begin{array}[]{c@{\hspace{1cm}}c@{\hspace{1cm}}c}\{(\mathbf{x}_{i})\}_{i=l+1} ^{\mathcal{R}}\sim u(p_{i})\hfil\hskip 28.45274pt&\{(\mathbf{x}_{i})\}_{i=l+1} ^{\mathcal{R}}\sim r(z_{i})\hfil\hskip 28.45274pt&\{(\mathbf{x}_{i})\}_{i=l+1} ^{\mathcal{R}}\sim u(p_{i})+r(z_{i})\\ \hfil\hskip 28.45274pt&\hfil\hskip 28.45274pt&\\ \text{Information-based}\hfil\hskip 28.45274pt&\text{Representation-based} \hfil\hskip 28.45274pt&\text{Hybrid}\end{array}$ $$

Information-based. Information or uncertainty-based approaches calculate the uncertainty for each instance in the unlabeled pool, leveraging probability scores of the learning algorithm to subsequently select the most uncertain instances. These approaches are quite fast, as the calculations are performed in the (lower-dimensional) space of probabilities. However, they bear the risk of leading to a strong distribution shift in the data. In this work, we implement various approaches, most of which were also considered by [].
Amongst them are the well-known margin sampling (MS) [], entropy sampling (ES) [] and least-confident sampling (LC) [], sampling instances which have the lowest margin, highest entropy, or where the learning algorithm is the least-confident about, respectively. variance reduction (VR) [] and expected error reduction (EER) [] select instances that are expected to reduce the prediction error or output variance, respectively. epistemic uncertainty sampling (EU) [] samples instances that have the highest epistemic uncertainty. Further, we also considered methods that compute uncertainty based on predicted probabilities of an ensemble such as query-by-committee (QBC) [] (disagreement of the ensemble members), maximum entropy (MaxEnt) [] (entropy of the averaged predictions) and BALD [] (difference between MaxEnt and the averaged entropy of the members’ predictions). PowMS and PowBALD [] build on MS and BALD but add a noise term to the uncertainty scores to enforce diversity within the queried instances.

Representation-based. qs compute the representativeness of each instance in the raw input space or in some feature space. Both can potentially be high-dimensional, leading to high computational costs. \Ackmeans [] performs clustering of the instances in $\mathcal{D}_{U}$ and selects those that are nearest to the cluster centers. \Actypclu [] clusters all instances in $\mathcal{D}_{L}$ and $\mathcal{D}_{U}$ and then selects instances that lie in clusters in which no instance of $\mathcal{D}_{L}$ is located. CoreSet [] queries those instances from $\mathcal{D}_{U}$ for which the closest neighbor in $\mathcal{D}_{L}$ is the most distant.

Hybrid. Hybrid approaches combine uncertainty and representativeness. \Acclums  [] selects instances by first performing clustering on $\mathcal{D}_{U}$ and then taking into account the margin scores as well. \Acclue [] performs weighted k-means clustering on $\mathcal{D}_{U}$ with the entropy of the learning algorithm as sample weight. FALCUN [] computes a relevance score per instance, consisting of the margin scores of the learning algorithm and a diversity score, which is computed from distances between the predicted probabilities.

### 3.4 Active Learning Pipelines

We call the combination of a learning algorithm and a QS an active learning pipeline (ALP).
Within an ALP the learning algorithm and QS are used in alternating order to (re-)fit a model for the labeled data points and determine data points to be queried to the oracle. In ALPBench, we explicitly account for this interplay and therefore allow for constructing ALPs out of every possible combination of learning algorithms and QS as long as they work with certain interfaces.

## 4 Active Learning Pipeline Benchmark

ALPBench is meant to provide an easy-to-use and easy-to-extend platform for investigating ALPs, considering different combinations of learning algorithms and qs, and evaluating new query strategies to be tested and compared against already known strategies. To this end, in ALPBench, we aim for high modularity with simple interfaces for the individual parts of an ALP, as well as for applying the composed pipelines to different datasets and experiment setups.

To facilitate the usage of ALPBench, we subsequently explain how AL problems are specified (Section 4.1), how ALPs are specified (Section 4.2), and what measures are taken for ensuring reproducibility and therewith high-quality experimental studies (Section 4.3).

### 4.1 Specification of Active Learning Problems

Setting.
A setting describes the basic parameters of an AL benchmark problem. This includes the size of the test data, the size of the initially labeled dataset, the number of AL iterations, and how many data points may be queried in one such iteration.

Scenario. A scenario combines the fundamental parameters of a setting with a concrete classification task, i.e., an OpenML dataset ID, seeds for splitting the dataset into initially labeled, unlabeled, and test data, and a seed for pseudo-random execution of the active learning pipeline.

In ALPBench, we predefine 5 different settings of varying sizes of initially labeled data and additional budget for labeling, with and without defining the budget depending on the number of classes.
Therefore, by specifying a scenario, we can describe a single task. However, to conduct broader empirical studies, we need to have suites of benchmarks, which can also be specified in ALPBench:

Benchmark Suite Benchmark Suites in ALPBench are essentially collections of datasets that can be combined with scenarios. ALPBench allows for specifying custom benchmark suites, with OpenML [] serving as the backbone for datasets. To define new benchmark suites, it suffices to either give a benchmark ID from OpenML or specify a list of OpenML dataset IDs.

In our benchmark implementation, we provide five scenarios and two benchmark suites: OpenML-CC18 [] and TabZilla []. Both benchmark suites together comprise a total of 86 datasets.

### 4.2 Specification of Active Learning Pipelines

To apply AL methods to AL problems, active learning pipelines (ALPs) are specified by a learner and a query strategy (QS), as has been outlined in Section 3.4

Active Learning Pipeline. An ALP is mainly composed of two components, precisely a QS and a learner, plus one optional component in the form of an initializer, which can be used to select an initial set of data points. It implements the main logic for the interplay between the learner and QS and takes care of the communication with the oracle.

Learner. The learner is a learning algorithm that implements the scikit-learn classifier interface and is responsible for model induction. There are no restrictions on the type of learner as long as its interface matches that of a scikit-learn classifier. It is only provided with labeled data points.

Query Strategy. The QS selects unlabeled data points to be queried by the oracle. To perform its task, it is provided with the learner, the already labeled and unlabeled data points. While some qs are wrapped and included from the scikit-activeml library [], the remaining qs are original implementations in ALPBench.

In our benchmark implementation, we include 23 qs and a broad spectrum of 23 different learners that can be combined into more than 500 ALPs.

### 4.3 Reproducibility and Experimentation

As we would like to ensure a high-quality standard for experiments conducted with ALPBench, we provide support for logging and facilitate the execution of experiments.

Benchmark Connector. The benchmark connector stores meta-information relevant for reproducibility. This includes storing the indices of data points that are labeled initially and used for testing. Furthermore, the settings of hyperparameters of learners and query strategies are stored so that the same configurations can be maintained for future studies. We provide two facades of the Benchmark Connector, one using a database as data storage and one that works locally with a filesystem.

Experimenter. Building on pyExperimenter [], ALPBench comes with some convenience functionalities to foster large-scale experimental studies. A cross-product experiment grid is specified for some default setup and can be easily extended by more alternatives. Furthermore, we provide logging facilities to observe the active learning process, recording labeling statistics and learner performances.

## 5 Experiments

To demonstrate the usefulness of ALPBench, we conduct an empirical study comparing various active learning pipelines composed of different combinations of qs and learning algorithms. Concretely, we investigate the effectiveness of 9 qs and pair them with 8 learning algorithms, thereby constituting the most extensive study on active learning pipelines. The experimental setup is explained in Section 5.1 before the evaluation methods and results are described in Sections 5.2 and 5.3, respectively.
More details regarding the experiments are given in Appendix A.4.

### 5.1 Experimental Setup

In our experimental study, we consider a total of 86 real-world datasets from the OpenML-CC18 [] and the TabZilla [] benchmark suites. Furthermore, of the 23 qs ALPBench provides, we select a set of 9 representatives covering the different types of query strategies, as well as a subset of 8 learning algorithms from different ends of the bias-variance spectrum. Our selection of learning algorithms ranges from linear models to highly non-linear models, including various decision tree ensembles and SOTA deep learning methods for tabular data. More precisely, we include ES [], MS [], PowMS and PowBALD [], CoreSet [], FALCUN [], cluster margin (CluMS) [] and typical clustering (TypClu) [], and random sampling (Rand) as qs and SVM, k-NN, MLP, RF, XGB, Catboost, TabNet, and TabPFN as learning algorithms.

In Table 1, we compare the scope of our experimental evaluation to previous studies on active learning for tabular data [, , , ].
We would like to note that despite considering only a subset of methods provided in ALPBench, our work provides the most comprehensive empirical comparison so far.
To further contain computational costs, we limit the training time of learning algorithms to 180 seconds per iteration.
This of course may decrease the performance of deep learning algorithms, such as TabNet, and poses a limitation to the generalizability of our empirical study.

Active Learning Setting. Acknowledging the effect of different sizes of the initially labeled pool $\mathcal{D}_{L}$ and the budget $\mathcal{B}$, we consider – similarly to [] – different AL settings. However, we firstly choose a small and a large setting, excluding a medium setting, for which [] found quite similar results as for the small one. Secondly, we do not choose a task-independent size of $|\mathcal{D}_{L}^{0}|$ and the per-iteration budget $R$, but rather a task-dependent size dependent on the difficulty of the task.

**Table 1: Comparison of the scopes of our evaluation study and previous ones.**
|  |  | Yang et al., [] | Zhan et al., [] | Lu et al., [] | Bahri et al., 2022a [] | Ours |
| --- | --- | --- | --- | --- | --- | --- |
| QS | Info. | [, , , , , , , ] | [, , , , , , ] | [, , , , , ] | [, , , , , , , ] | [, , ] |
| Repr. | - | [] | [, ] | [] | [, ] |  |
| Hybr. | [] | [, , , ] | [, , , ] | [, ] | [, , ] |  |
| Learner | Base | LR | SVM | SVM | - | k-NN, SVM, RF |
| GBDT | - | - | - | - | CatBoost, XGB |  |
| DNN | - | - | - | MLP | MLP, TabNet [] |  |
| PFN | - | - | - | - | TabPFN [] |  |
| ALP | $\sum$ | 10 | 14 | 13 | 13 | 72 |
| DS | Binary | 44 | 35 | 26 | 35 | 48 |
| Multi | - | 9 | - | 34 | 38 |  |
| $\sum$ | 44 | 44 | 26 | 69 | 86 |  |
| AL Setting | 1 | 1 | 1 | 3 (s,m,l) | 2 (s,l) |  |

Datasets.
We evaluate each ALP on all DSs from the OpenML-CC18 [] and the TabZilla Benchmark Suite [], except for 4 quite large datasets, with OpenML IDs 1567, 1169, 41147, and 1493, leaving us with 48 binary and 38 multi-class datasets.

### 5.2 Evaluation Methods

We first want to investigate whether strong learning algorithms for tabular data found by [] still perform well in the low-label regime, especially when combined with qs into ALPs.

Heatmaps. Assuming $N$ learning algorithms and $M$ qs, we compute so-called heatmaps $H$ of size $N\times M$. Let learner $i$ and QS $j$ form the combined ALP(i,j), ALPd be the winning ALP for the dataset $d$ and $D$ the number of available datasets. Then, the entry of $H$ at position $(i,j)$ is defined as

$$ $H_{(i,j)}=\sum_{d=1}^{D}\mathds{1}[\text{\acs{alp}${}_{(i,j)}$ is not statistically significant from \acs{alp}${}_{d}$ on dataset d}]$ $$

To determine statistical significance, we use Welch’s t-test with $p=0.05$. The indicator function hence evaluates to one for ALPd and all ALPs for which the null hypothesis cannot be rejected.

We further aim to investigate the interplay of the QS with different learners, to reveal which qs are particularly effective for each learner. In agreement with common evaluation procedures for comparing qs [, , ], we also computed budget curves and win-matrices.

Figure: Figure 2: Heatmaps for all ALPs within our evaluation study with statistical significance (first and second subfigure) and without (third and fourth). Information-based, representation-based, and hybrid qs are colored in red, green, and blue, respectively, and random sampling is in purple.
Refer to caption: x2.png

Figure: Figure 3: Lose-Heatmaps for all ALPs excluding TabNet without statistical significance, considering binary and multiclass datasets. The color-coding is consistent with Figure 2.
Refer to caption: x6.png

Budget curves. Budget curves quantify the (test) performance of an ALP at each round of the AL procedure. The area under the budget curve (AUBC) then offers a robust metric to compare different ALPs. Tailored to our benchmark, we generate budget curves for each dataset and learning algorithm.

Win-matrices. Further, we compute a win-matrix $W$ for each learning algorithm, comparing the performances of query strategies for that learner. Sticking to the notation above, this results in an $M\times M$ matrix $W$ for each learner. To make the plots of the win-matrices visually more appealing, we slightly modify the definition of the entry at position $(i,j)$ compared to [, ] as

$$ $W_{(i,j)}=\frac{\sum_{d=1}^{D}\mathds{1}[\text{\acs{qs} i beats \acs{qs} j on dataset d}]}{D}$ $$

Again, wins and losses are only defined in case of statistical significance. To determine a win or a loss, respectively, we compare the AUBC of two qs after the total amount of iterations. This provides us with a robust measure since the overall performance across all iterations is captured.

### 5.3 Results

In this section, we present our main insights, aiming to answer the following research questions (RQ): RQ1: Which ALPs perform best and worst? RQ2: Given a specific learning algorithm, which QS is particularly well suited? RQ3: Are there datasets and/or settings where AL leads to a decrease in performance?

Figure: Figure 4: Win-Matrices for different learners (SVM, Catboost and TabPFN) for the small setting considering multi-class datasets with statistical significance.
Refer to caption: x10.png

RQ1. In Figure 2, we show heatmaps as described in Section 5.2. \Acrf, Catboost, and TabPFN are quite dominant, as they constitute to many winning ALPs. \Acxgb also performs well overall, however, showing a preference for large over small settings. TabNet, MLP, and k-NN are performing inferior, which, in the case of TabNet, might be due to limited training time. In the third and fourth subfigure, we present heatmaps without considering statistical significance, which may seem an unusual perspective. However, it reveals insights into which ALPs reach peak performance for each dataset, showcasing that almost every combination of learning algorithm and QS at least once constitutes a winning pipeline. While Catboost and TabPFN are still quite dominant, we find that repr.-based and hybr. qs perform better in the small setting. This is reasonable, since the number of labeled instances might not suffice to learn the data distribution in this setting, which initially might favor querying diverse instances. In contrast, in the large setting, the learner is initially given enough instances to roughly learn the data distribution and then gains more when instances close to the decision boundary are queried.

To investigate which ALPs perform particularly poorly, we present Lose-Heatmaps in Figure 3, where the losing pipeline replaces ALPd. We again neglect statistical significance and consider binary and multi-class datasets separately, to investigate class-dependent differences. In this figure, we exclude TabNet as it did not perform at all in our investigated setting. We find that it is more important to choose a strong learner than selecting a suitable QS and that repr. and hybr. qs are better suited for multi-class than for binary datasets. Concretely, one should avoid using MLP or k-NN, and ALPs combining k-NN with PowBALD or MLP with FALCUN proved disadvantageous.

RQ2. In Figure 4, we present win-matrices for different learning algorithms. We choose SVM since it has been chosen as a learning algorithm in other AL studies, such as [, ]. Further, we choose Catboost and TabPFN, as they have shown strong performance when combined into ALPs. The win matrices clearly show that the suitability of different qs varies, depending on the given learning algorithm. Specifically for SVM, Rand outperforms most other qs on roughly a third of the datasets, which does not hold for Catboost nor TabPFN. While the dependence of the suitability of different qs on the chosen learning algorithm may not be surprising, it highlights that findings from AL benchmarks considering only one learning algorithm [, , , ] are biased towards that particular learning algorithm. This reinforces the need for a joint benchmark as ALPBench.

RQ3. In Figure 5, we present budget curves for RF, k-NN, and XGB on two datasets (OpenML ID 846 and 1053) in the small setting. For better visual clarity, we only combine the learners with Rand, MS, CoreSet, and CluMS, each representing a different family of qs.

Budget curves in AL are generally expected to show an upward trend, indicating improved performance with an increasing budget, as visual in the first subfigure. However, this pattern is not consistent across all learners, as for the combination of k-NN and CoreSet, the performance decreases. On a different dataset (third subfigure), this also holds for random sampling and slightly for MS and CluMS. Even for a learning algorithm that demonstrates a strong overall performance, the picture can look quite similar, as shown in the fourth subfigure. To conclude, AL can deteriorate performance, as has also been shown in [, ]. We also want to emphasize again the strong dependence of the performance of ALPs on the chosen learning algorithm, dataset, setting, and potentially other properties, which still need to be understood. We hope that ALPBench will serve as a tool to gain more insights into this.

Figure: Figure 5: Budget curves for different ALPs combined out of RF, k-NN, XGB and Rand, MS, CoreSet and CluMS on different datasets, considering the small setting.
Refer to caption: x13.png

## 6 Conclusion and Future Work

We proposed ALPBench, a benchmark for active learning pipelines (ALPs) on tabular data. ALPBench allows for easily combining qs and learning algorithms into ALPs and provides a unified API to evaluate and benchmark them against each other. The open-source implementation of our benchmark is available as a Python package.

In the benchmark so far, we focused on tabular classification tasks. A natural extension to ALPBench would thus be to add more benchmark families such as regression tasks or different types of data, such as image data. We predefined five different settings, which were partly inspired by []. However, the exploration of more settings having different requirements for suitable pairs of qs and learning algorithms outlines an interesting avenue for future work. Further, it might be appealing to incorporate other more recent trends, such as label noise, multiple annotators, etc.

In our experimental evaluation, we find that most of the time, strong pipelines consist of learners such as RF, Catboost, or TabPFN and information-based query strategies. However, there are also datasets for which representation-based and hybrid approaches achieve better results. Also, the suitability of a QS heavily depends on the chosen learner and the number of classes of the dataset.

With this benchmark and library, we hope to foster further research to fairly evaluate new QS considering different datasets, settings, and learners. Moreover, it might be appealing to specifically develop new qs for certain settings and/or learners. Lastly, we would also like to study whether it might be advantageous to devise hyperheuristics switching between different qs within one active learning procedure. Based on the findings in Figure 2, it might be a good idea to first focus more on diversity and later on uncertainty.

## 7 Limitations and Broader Impact Statement

Both the benchmark and the evaluation study are limited to tabular classification problems and consider a specific set of active learning settings. Furthermore, in the empirical study, we restricted the training time to 180 seconds per iteration, which might limit generalizability for the large settings. Nevertheless, we observe complementary performance for both learning algorithms and query strategies, which underpins the need for a benchmark as ALPBench.

This is also one of the reasons why we believe that ALPBench has the clear potential to foster more research on active learning and may incentivize the development of new query strategies that are adaptive to the learning algorithm’s behavior.
As this paper presents work that aims to advance the field of machine learning, there are many potential societal consequences of our work. However, we feel that none of these needs to be specifically highlighted here.

## Acknowledgments and Disclosure of Funding

The authors gratefully acknowledge the computing time provided to them on the high-performance computers Noctua 2 at the NHR Center PC^2, granted under the AProSys project funded by Bundesministerium für Wirtschaft und Klimaschutz (BMWK), Förderkennzeichen: 03EI6090E. These are funded by the German Federal Ministry of Education and Research and the state governments participating on the basis of the resolutions of the GWK for the national high-performance computing at universities (www.nhr-verein.de/unsere-partner).

## NeurIPS Paper Checklist

- 1.
Claims
- Question: Do the main claims made in the abstract and introduction accurately reflect the paper’s contributions and scope?
- Answer: [Yes]
- Justification: Yes, the claims presented in abstract and introduction of the paper are carefully tailored based on the experimental design and results obtained from it. We show that other benchmarks ignore crucial aspects of pipeline synthesis, often neglecting one or multiple steps of pipeline creation. We provide a standardized setting for active learning pipeline benchmarking (see Sections 4 and 5) and extract insights from the performance of different configurations (see Sections 5.3).
- Guidelines:
•
The answer NA means that the abstract and introduction do not include the claims made in the paper.
•
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.
•
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
•
It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.
- 2.
Limitations
- Question: Does the paper discuss the limitations of the work performed by the authors?
- Answer: [Yes]
- Justification: Limitations of the paper are discussed in Section 7, showing possible additional perspectives that should be considered when investigating active learning pipelines.
- Guidelines:
•
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
•
The authors are encouraged to create a separate "Limitations" section in their paper.
•
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
•
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
•
The authors should reflect on the factors that influence the performance of the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
•
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
•
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.
•
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren’t acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
- 3.
Theory Assumptions and Proofs
- Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?
- Answer: [N/A]
- Justification: The paper does not contain theoretical proofs and results.
- Guidelines:
•
The answer NA means that the paper does not include theoretical results.
•
All the theorems, formulas, and proofs in the paper should be numbered and cross-referenced.
•
All assumptions should be clearly stated or referenced in the statement of any theorems.
•
The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition.
•
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
•
Theorems and Lemmas that the proof relies upon should be properly referenced.
- 4.
Experimental Result Reproducibility
- Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
- Answer: [Yes]
- Justification: Yes, the paper provides detailed descriptions on experiment formulations, datasets, configurations and measurements. Section 4 describes how the benchmark was designed and demonstrates an example on how to reproduce results. Section 5 details the used materials and evaluation methods. Moreover, we provide a library to facilitate reproducibility that also enables possible extensions for practitioners.
- Guidelines:
•
The answer NA means that the paper does not include experiments.
•
If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
•
If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
•
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
•
While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a)
If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.
(b)
If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully.
(c)
If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).
(d)
We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.
- 5.
Open access to data and code
- Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?
- Answer: [Yes]
- Justification: Yes, all the used datasets and materials are public. Moreover, we provide the code with clear instructions to enable readers to run the same experiments reported in the paper.
- Guidelines:
•
The answer NA means that paper does not include experiments requiring code.
•
Please see the NeurIPS code and data submission guidelines ([https://nips.cc/public/guides/CodeSubmissionPolicy](https://nips.cc/public/guides/CodeSubmissionPolicy)) for more details.
•
While we encourage the release of code and data, we understand that this might not be possible, so “No” is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).
•
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines ([https://nips.cc/public/guides/CodeSubmissionPolicy](https://nips.cc/public/guides/CodeSubmissionPolicy)) for more details.
•
The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
•
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only a subset of experiments are reproducible, they should state which ones are omitted from the script and why.
•
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
•
Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.
- 6.
Experimental Setting/Details
- Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
- Answer: [Yes]
- Justification: Yes, the experimental setting is carefully described in Sections 4 and 5. The description includes dataset manipulation, experimental setup (dataset splits and active learning settings), learning models and evaluation methods.
- Guidelines:
•
The answer NA means that the paper does not include experiments.
•
The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.
•
The full details can be provided either with the code, in appendix, or as supplemental material.
- 7.
Experiment Statistical Significance
- Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
- Answer: [Yes]
- Justification: In Section 5.2 we provide information about statistical significance of the experiments. As mentioned there, we use Welch’s t-test with $p=0.05$ to determine statistical sinificance.
- Guidelines:
•
The answer NA means that the paper does not include experiments.
•
The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.
•
The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).
•
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
•
The assumptions made should be given (e.g., Normally distributed errors).
•
It should be clear whether the error bar is the standard deviation or the standard error of the mean.
•
It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified.
•
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).
•
If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
- 8.
Experiments Compute Resources
- Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
- Answer: [Yes]
- Justification: Yes, computational resources are reported in Appendix A.4. All experiments were conducted with 2 CPU cores and 8GiB RAM or 16GiB for the small
and large settings, respectively, to resemble end-user environments. The HPC nodes for the computations are
equipped with two AMD Milan 7763 and 256GiB main memory in total. Runs exceeding these limits have been
canceled by the workload manager.
- Guidelines:
•
The answer NA means that the paper does not include experiments.
•
The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
•
The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.
•
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn’t make it into the paper).
- 9.
Code Of Ethics
- Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics [https://neurips.cc/public/EthicsGuidelines](https://neurips.cc/public/EthicsGuidelines)?
- Answer: [Yes]
- Justification: Yes, the paper follows NeurIPS Code of Ethics with no harms caused by the research process.
- Guidelines:
•
The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.
•
If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.
•
The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).
- 10.
Broader Impacts
- Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
- Answer: [Yes]
- Justification: Yes, the paper thoroughly discusses the societal impacts in the broader impact statement in Section 7.
- Guidelines:
•
The answer NA means that there is no societal impact of the work performed.
•
If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.
•
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
•
The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
•
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
•
If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).
- 11.
Safeguards
- Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
- Answer: [N/A]
- Justification: The paper does not present such risks.
- Guidelines:
•
The answer NA means that the paper poses no such risks.
•
Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.
•
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
•
We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.
- 12.
Licenses for existing assets
- Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
- Answer: [Yes]
- Justification: Yes, all data and materials used are publicly available under the MIT License.
- Guidelines:
•
The answer NA means that the paper does not use existing assets.
•
The authors should cite the original paper that produced the code package or dataset.
•
The authors should state which version of the asset is used and, if possible, include a URL.
•
The name of the license (e.g., CC-BY 4.0) should be included for each asset.
•
For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.
•
If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, [paperswithcode.com/datasets](paperswithcode.com/datasets) has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.
•
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
•
If this information is not available online, the authors are encouraged to reach out to the asset’s creators.
- 13.
New Assets
- Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
- Answer: [Yes]
- Justification: Yes, we provide a new library for active learning benchmarking under the MIT license.
- Guidelines:
•
The answer NA means that the paper does not release new assets.
•
Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
•
The paper should discuss whether and how consent was obtained from people whose asset is used.
•
At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
- 14.
Crowdsourcing and Research with Human Subjects
- Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?
- Answer: [N/A]
- Justification: The paper does not involve crowdsourcing nor research with human subjects.
- Guidelines:
•
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
•
Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper.
•
According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
- 15.
Institutional Review Board (IRB) Approvals or Equivalent for Research with Human Subjects
- Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
- Answer: [N/A]
- Justification: The paper does not involve crowdsourcing nor research with human subjects.
- Guidelines:
•
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
•
Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
•
We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.
•
For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.

## Appendix A Appendix

### A.1 Glossary of Acronyms

### A.2 Comparison to existing benchmarks

In the following, we present an extensive tabular which compares ALPBench with existing active learning benchmarks. The QS and learning algorithms are ordered by their year of appearance.

| Query Strategy | Year | [] () | [] () | [] () | [] () | ALPBench (2024) |
| --- | --- | --- | --- | --- | --- | --- |
| ES [] |  | ✓ | ✓ | ✓ | ✓ | ✓ |
| QBC [] |  | ✗ | ✓ | ✗ | ✓ | ✓ |
| VR [] |  | ✗ | ✓ | ✗ | ✓ | ✓ |
| LC [] |  | ✗ | ✓ | ✓ | ✓ | ✓ |
| FIVR [] |  | ✓ | ✗ | ✗ | ✗ | ✗ |
| MS [] |  | ✗ | ✓ | ✓ | ✓ | ✓ |
| EER [] |  | ✓ | ✓ | ✗ | ✓ | ✓ |
| MaxER [] |  | ✓ | ✗ | ✗ | ✗ | ✗ |
| CER [] |  | ✓ | ✗ | ✗ | ✗ | ✗ |
| EVR [] |  | ✓ | ✗ | ✗ | ✗ | ✗ |
| EMC [] |  | ✗ | ✓ | ✗ | ✗ | ✗ |
| MLI [] |  | ✓ | ✗ | ✗ | ✗ | ✗ |
| BALD [] |  | ✗ | ✗ | ✓ | ✗ | ✓ |
| MMC [] |  | ✓ | ✗ | ✗ | ✗ | ✗ |
| MaxEnt [] |  | ✗ | ✗ | ✓ | ✗ | ✓ |
| QBC VR [] |  | ✗ | ✗ | ✓ | ✗ | ✓ |
| EU [] |  | ✗ | ✗ | ✗ | ✗ | ✓ |
| PowMS [] |  | ✗ | ✗ | ✓ | ✗ | ✓ |
| Min Margin [] |  | ✗ | ✗ | ✓ | ✗ | ✓ |
| k-means sampling (k-means) [] |  | ✗ | ✓ | ✗ | ✗ | ✓ |
| HIER [] |  | ✗ | ✓ | ✗ | ✓ | ✗ |
| CoreSet [] |  | ✗ | ✗ | ✓ | ✓ | ✓ |
| TypClu [] |  | ✗ | ✗ | ✓ | ✗ | ✓ |
| FALCUN [] |  | ✗ | ✗ | ✗ | ✗ | ✓ |
| MarginDensity [] |  | ✗ | ✗ | ✓ | ✗ | ✗ |
| DWUS [] |  | ✗ | ✓ | ✗ | ✓ | ✗ |
| QUIRE [] |  | ✗ | ✓ | ✗ | ✓ | ✗ |
| GRAPH [] |  | ✗ | ✓ | ✗ | ✓ | ✗ |
| AAL [] |  | ✓ | ✗ | ✗ | ✗ | ✗ |
| ALBL [] |  | ✗ | ✓ | ✗ | ✓ | ✗ |
| CluMS [] |  | ✗ | ✗ | ✓ | ✗ | ✓ |
| clustering uncertainty-weighted embeddings (CLUE) [] |  | ✗ | ✗ | ✗ | ✗ | ✓ |

| Learning Algorithm | Year | [] () | [] () | [] () | [] () | ALPBench (2024) |
| --- | --- | --- | --- | --- | --- | --- |
| LR [] |  | ✓ | ✗ | ✗ | ✗ | ✓ |
| k-NN [] |  | ✗ | ✗ | ✗ | ✗ | ✓ |
| MLP [] |  | ✗ | ✗ | ✓ | ✗ | ✓ |
| NB [] |  | ✗ | ✗ | ✗ | ✗ | ✓ |
| SVM [] |  | ✗ | ✓ | ✗ | ✓ | ✓ |
| RF [] |  | ✗ | ✗ | ✗ | ✗ | ✓ |
| ETC [] |  | ✗ | ✗ | ✗ | ✗ | ✓ |
| XGB [] |  | ✗ | ✗ | ✗ | ✗ | ✓ |
| Catboost [] |  | ✗ | ✗ | ✗ | ✗ | ✓ |
| TabNet [] |  | ✗ | ✗ | ✗ | ✗ | ✓ |
| TabPFN [] |  | ✗ | ✗ | ✗ | ✗ | ✓ |

### A.3 Predefined Settings

In Table 2, we list the different settings for configuring different active learning scenarios that are predefined and implemented in ALPBench.

**Table 2: Predefined active learning settings in ALPBench.**
| Name | $|D^{0}_{L}|$ | $B$ | $R$ |
| --- | --- | --- | --- |
| small | 30 | 200 | 10 |
| medium | 100 | 1,000 | 50 |
| large | 300 | 10,000 | 200 |
| small (dynamic) | 10 | $100\cdot|\mathcal{Y}|$ | $5\cdot|\mathcal{Y}|$ |
| large (dynamic) | 10 | $400\cdot|\mathcal{Y}|$ | $20\cdot|\mathcal{Y}|$ |

### A.4 Experiments

In this section, we elaborate in more detail on the experiments that were conducted within our evaluation study.

Datasets. From the 90 datasets from the OpenML-CC18 [] and the TabZilla Benchmark Suite [] we filtered and excluded the datasets with OpenML IDs 1567, 1169, 41147, and 1493. The first three were filtered out for all settings because they consist of more than 300,000 data points, which would result in a large amount of computing time for the non-info. based qs. The last dataset with OpenML ID 1493 was filtered out since it consists of 100 classes, which would result in a huge amount of $\mathcal{R}$, limiting the number of iterations to a high degree. Further, for the large setting, we wanted to guarantee that at least 10 iterations can be performed until all instances from $\mathcal{D}_{U}$ are queried. This led to the removal of OpenML IDs 11, 12, 14, 16, 18, 22, 25, 51, 54, 188, 307, 458, 469, 1468, 1501, 40966, and 40979 for this setting. For the preprocessing steps, we proceed as follows. Categorical features are one-hot encoded and missing values are imputed by the mean or mode of the corresponding feature.

Active Learning Setting. As mentioned, we investigate a small and a large setting. Explicitly, the small and large settings are specified by $|\mathcal{D}_{L}^{0}|=R=5\cdot|\mathcal{Y}|$ and $|\mathcal{D}_{L}^{0}|=R=20\cdot|\mathcal{Y}|$, respectively, for the given dataset and a total amount of 20 iterations or until all instances from the unlabeled pool $\mathcal{D}_{U}$ are queried. We choose the factor 5 for the small setting, since then $\mathcal{R}$ matches the one in the (static) small setting in []. For the large setting, we should have chosen a factor of 100 to be again consistent with []. However, this seemed unrealistic to us for real-world applications. For some (imbalanced) datasets, it may happen that not every class is at least once represented in $\mathcal{D}_{L}^{0}$. In these cases, we additionally randomly sample one instance from $\mathcal{D}_{U}$ per missing class and add them with their corresponding label to $\mathcal{D}_{L}^{0}$.
We run each ALP ten times with different seeds, where the seed defines the $\frac{2}{3}/\frac{1}{3}$-split of the total dataset $\mathcal{D}$ into $\mathcal{D}_{\text{train}}$ and $\mathcal{D}_{\text{test}}$ as well as the split of $\mathcal{D}_{\text{train}}$ into $\mathcal{D}_{L}$ and $\mathcal{D}_{U}$. Needless to say, the datasets we consider are originally (fully-)labeled datasets. Tailored to the AL setting, we discard the labels for the instances in $\mathcal{D}_{U}$ and assure that only the oracle $\mathcal{O}$ can access them.

Configuration of Learning Algorithms.
In general, we do not perform any hyperparameter optimization (HPO) but rather stick to the default parameters. For XGB and Catboost, we reduce the training time by setting the tree method to hist and limiting the amount of iterations, respectively. For Catboost, we implement a timeout of three minutes for the same purpose. TabPFN [] can so far only be fitted on a maximum amount of 1,000 instances. Therefore, we uniformly sample 1,000 instances from the current dataset to be fitted on, in case this constraint is violated, similar to []. For TabPFN and TabNet we modify the implementation for the representation-based and hybrid approaches. Concretely, we extract the output of the encoder from the TabPFN and the activations of the penultimate layer from TabNet to compute the representativeness of each instance based on its embedding. The exact details can obviously be looked up in our implementation.

Implementation.
All experiments were conducted with 2 CPU cores and 8GiB RAM or 16GiB for the small and large settings, respectively, to resemble end-user environments. The HPC nodes for the computations are equipped with two AMD Milan 7763 and 256GiB main memory in total. Runs exceeding these limits have been canceled by the workload manager.

### A.5 Results

This section contains more experimental results, comprising more heatmaps and win-matrices distinguishing between binary and multi-class datasets and more budget curves for other datasets and learners. Precisely, we first present heatmaps where we - compared to the main paper - distinguish between binary and multi-class datasets, cf. Figure 6 and 7, respectively. The results for the binary setting look quite similar to the ones in the main paper, where we consider all datasets. However, for the multi-class datasets, there are some differences: Specifically, for large datasets, XGB is dominating TabPFN and Catboost.

Figure: Figure 6: Heatmaps for all ALPs within our evaluation study with statistical significance (first and second subfigure) and without (third and fourth) for binary datasets. Info.-based, repr.-based, and hybr. qs are colored in red, green, and blue, respectively, and random sampling in purple.
Refer to caption: x17.png

Figure: Figure 7: Heatmaps for all ALPs within our evaluation study with statistical significance (first and second subfigure) and without (third and fourth) for multi-class datasets. Info.-based, repr.-based, and hybr. qs are colored in red, green, and blue, respectively, and random sampling in purple.
Refer to caption: x21.png

In Figure 8 and 9, we present Win-Matrices for k-NN, MLP and XGB considering the small setting on binary and multi-class datasets, respectively. Notably, FALCUN shows better performance with k-NN and MLP on binary datasets compared to multi-class datasets. Additionally, ES demonstrates superior performance on binary datasets when paired with XGB as the learning algorithm.

Figure: Figure 8: Win-Matrices for k-NN, MLP and XGB considering binary dataset and the small setting.
Refer to caption: x25.png

Figure: Figure 9: Win-Matrices for k-NN, MLP and XGB considering multi-class dataset and the small setting.
Refer to caption: x28.png

We present budget curves for SVM, RF, XGB and TabPFN in Figures 10 to 13 and for all learners excluding TabPPFN in Figures 15 and 14. As illustrated in Figures 10 to 13, the choice of the learning algorithm is crucial to the overall performance. For example, on the dataset with OpenML ID 334, all pipelines using TabPFN as the learner achieve performance at least as good as the best pipeline using XGB. Another notable observation is the performance of TabNet, as shown in the budget curves in Figures 15 and 14. The budget curve resembles a step function, suggesting that TabNet requires a minimum amount of labeled data to achieve reasonable performance.

Figure: Figure 10: Budget curves for different ALPs on the dataset with OpenML ID 1464, considering the small setting.
Refer to caption: x31.png

Figure: Figure 11: Budget curves for different ALPs on dataset with OpenML ID 334, considering the small setting.
Refer to caption: x35.png

Figure: Figure 12: Budget curves for different ALPs on the dataset with OpenML ID 6332, considering the small setting.
Refer to caption: x39.png

Figure: Figure 13: Budget curves for different ALPs on the dataset with OpenML ID 25, considering the small setting.
Refer to caption: x43.png

Figure: Figure 14: Budget curves for different ALPs on the dataset with OpenML ID 182, considering the large setting. TabPFN is excluded since this dataset has more than 10 features.
Refer to caption: x47.png

Figure: Figure 15: Budget curves for different ALPs on the dataset with OpenML ID 46, considering the large setting. TabPFN is excluded since this dataset has more than 10 features.
Refer to caption: x54.png

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 8
presentation: 3
questions: 1. In each row from Table 2, please clarify 
    (i) how many of the "capabilities" in the previous four approaches are covered by ALPBench  
    (ii) which are missing and why (eg, for QS-Hybr, two of the previous approaches cover 4, while you only cover 3; what is the overlap among them?)

2. line 371: how big are the datasets that you have excluded? given that you have included them in ALPBench, you should -at the very least- give the reader a sense of the challenges/costs/time-constraints/ideal-setup of running experiments at that scale.

3. In how many of the experiments is the performance of the base learner impacted by the 180 secs limit? If this is a real issue, you should have mentioned it at the very beginning of the Experimental Results. 

4. is it possible to add to the current QSs two classic strategies such as Query-by-Bagging and Query-by-Boosting? See Abe & Mamitsuka, ICML-1998, "Query Learning Strategies Using Boosting and Bagging."

5. In Fig 4, is KNN the best base learner on those 3 datasets? if not, should we care about the erratic perfornace?

6. Is ALPBench "automatically detecting & reporting" situations in which, after a number of queries, AL hurts rather than helps (as in "5." above") 

7. how difficult would it be to extend ALPBench to cover additional scenarios, such as (i) simulating the stream-based scenario, (ii) interleaving AL and semi-supervised learning, and (iii) multiple-view learners?

OTHER:
line 206: please explain intuitively what is epistemic uncertainty; in its current form, it is a bit of a "circular definition:" 
              "epistemic uncertainty sampling (EU) (Nguyen et al., 2019) samples instances that have the highest epistemic uncertainty."
soundness: 3
strengths: The paper fills in a clear gap in today's landscape of active learning for tabular data. The work is reasonably original (for an evaluation framework), clearly presented, and with the potential of having a high-impact in standardizing empirical validations (while also making them apples-2-apples). The wide availability of ALPBench would greatly impact future AL evaluations: far too many of the newly submitted AL papers stop after an arbitrary nmb of queries, w/o any indication on whether or not the achieved performance is in any way meaningful.
summary: The paper introduces and illustrates ALPBench, which is a benchmark for reproducible active learning pipelines that covers 86 modern, tabular classification datasets, together with a wide variety of base leaners, query strategies, and setups.
weaknesses: While the paper goes a long way towards standardizing the evaluation of active learners, it can be improved along tow main directions:
1. First of all, instead than the "[somewhat] dry analysis" of the aggregated results in Figures 2 & 3 (which are excellent, but could go into an APPENDIX as supporting evidence), the paper would greatly benefit from a illustrative, step-by-step example of how to use ALPBench in a real-world scenario. Assume that you have a novel tabular dataset NTD for which active learning is essential. Ideally, you would like to follow  a procedure such as (i) identify which base learner BL performs best on NTD - after all, you what to reach SOTA performance with minimum data-annotation cost, (ii) identify which of the existing querying-strategies Q works best with BL for NTD, (iii) identify other datasets have properties similar to NTD, such as having Q+BL as a winner, and (iv) if the results are not satisfactory, invent a novel QS that will deliver better performance that Q+BL, add it to ALPFBench, and re-evaluate. In this scenario, the paper should provide guidance on how to add a new dataset or QS to ALPBench (assuming that it is possible), how to choose between ACC vs AUC metrics, how to choose the best base learner, etc. Similarly, the current insights on what works best for binary vs multi-class, small vs large setup, and the various base learners should be consolidated in a Lessons Leaned section (rather than spread throughout the paper)  
2. along the same lines: (i) the paper should provide a table with the SOTA performance on each dataset; that is, which base learner is the best when trained/tuned on all available training/dev data, and (ii) the paper should provide an additional set of metrics that measure how many queries does the best AL approach need to reach 50%, 75%, 90%, 95%, and 99% of the SOTA performance in "(i)". This guarantees that we are doing AL for the right reasons (ie, reach SOTA-adjacent performance with as few labeled examples as possible), rather than measuring "wins" after an arbitrary number of queries

---

confidence: 4
contribution: 2
initial_rating: 3
presentation: 3
questions: - q1. What is the main problem you had to solve to scale existing
  active learning benchmarks to more downstream models and
  more datasets?
- q2. How did you choose the three active learning regimes in tab. 1?
  How does this capture similar situations in different datasets?
- q3. How did the new benchmark make it easier to answer your three
  research questions? Did you arrive at different conclusion than
  those been drawn in earlier benchmarks?
- q4. What recommendations does your benchmark provide for
  chosing the best active learning method for a down-stream
  learner and based on what evidence?
soundness: 2
strengths: - s1. interesting aspect: looking systematically at different down-stream
  models.
- s2. promised a pip installable python module that should be easy to use.
- s3. well written.
summary: The paper proposes a benchmark for active learning methods
for tabular data. Esp. propose the authors to study active
learning not just for a single down-stream model, but
for a wider selection such as xgboost, catboost, SVMs,
MLPs etc. They suggest to evaluate three different
active learning regimes described by the size of the initial
labeled set, the batch size and the overall budget.
In experiments they compare different active learning
methods with different down-stream models on different
evaluation metrics in different such regimes.
weaknesses: - w1. there is not much innovation in this benchmark besides
  just scaling to more downstream models and more datasets.  
- w2. how the three active learning regimes (tab. 1) have been
  chosen is not discussed.
- w3. it is not clearly demonstrated how this new benchmark
  now makes it easier to answer the three research questions
  asked.
- w4. the maybe main question one would want to answer
  by looking at different down-stream models, namely
  should we use different active learning methods for different
  down-stream models, is not addressed.

review.

Looking at different down-stream models that should
be actively learned is clearly useful and maybe often
overlooked in the literature. 

However, I see several major issues with the paper
currently:
w1. there is not much innovation in this benchmark besides
  just scaling to more downstream models and more datasets.  
- it is not clear which problems the authors had to solve
  to arrive at the current benchmark.
- what are the limitations of current benchmarks, besides
  looking at fewer down-stream models and datasets?

w2. how the three active learning regimes (tab. 1) have been
  chosen is not discussed.
- Esp. it is not clear how these different regimes manage to
  capture similar situations in different datasets. Are not
  30 initial samples for a very simple dataset much, but
  for a more difficult dataset very little?
  
w3. it is not clearly demonstrated how this new benchmark
  now makes it easier to answer the three research questions
  asked.
- could we not just have used any of the existing benchmarks
  and run it with your 8 downstream models? if not, can you
  describe why not, and how this is now different in your
  benchmark?

w4. the maybe main question one would want to answer
  by looking at different down-stream models, namely
  should we use different active learning methods for different
  down-stream models, is not addressed.
- fig. 2 provides the best active learning pipeline. Not very surprising,
  gradient boosted decision tree models as down-stream models
  are found to perform best on average.
- from an active learning perspective, would it not be more interesting
  to ask the question for the conditional performance of the different
  active learning methods: given a method, say catboost, what are
  the best active learning methods? and esp. are they different
  if I choose different down-stream models, say an SVM instead?

Furter remarks:
- i1. usually the value of a benchmark is seen in identifying an interesting
  set of experiments that allow to ask and answer some research
  questions. You argue your benchmark is large, e.g., but then you
  subsample your own benchmark to provide results. How does this
  provide a clear benchmark for further research in active learning?
- i2. personally, I do not find the heavily colored tables providing
  a very clear overview. A clear decision criterion for what would make
  a good active learning method would be easier to use.


--- added after the rebuttal

Thank you for your replies. 

w1. limited innovation
- running the algorithms on more downstream model is "novel" in the sense
  that nobody did it before.
- but there is no difficulty that needs to be solved to do so.
- in this sense, there is limited innovation in the paper.

w2. similar AL regimes
- scaling the number of samples by class, not just absolutely, is standard
  procedure in many areas of ML, e.g., in few-shot-learning.
- besides number of classes, learning tasks vary a lot in difficulty,
  and this is not accounted for in your choice of AL regimes.

w3. no clear demonstration of usefulness of the benchmark.
- "standardized evaluation protocols, pre-defined settings, and logging 
  of seeds, exact splits, and model hyperparameters" is done already by 
  modern AL benchmarks, e.g., Lüth et al. 2023 and Ji et al. 2023.
  I think you will need another argument for yours.

w4. no  recommendations for per model active learning strategies.
- Yes, you are right, fig. 7 in the appendix I overlooked. 

I keep three of my points and slightly update the score accordingly.

additional references:
- Ji, Yilin, Daniel Kaestner, Oliver Wirth, and Christian Wressnegger. 
  “Randomness Is the Root of All Evil: More Reliable Evaluation of Deep Active Learning.” 
  In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision,   
  3943–52, 2023.

---

confidence: 3
contribution: 3
initial_rating: 5
presentation: 3
questions: Q1: Can the proposed benchmark be generalized to other modalities and other emerging machine learning models?
Q2: Can the authors discuss more interesting findings that are ignored by existing studies?
soundness: 3
strengths: S1: The authors proposed a benchmark to compare different active learning strategies for tabular classification tasks under various settings but in a consistent environment, which is important for performing fair comparisons across the research community.
S2: The benchmark integrates various datasets to compose a comprehensive benchmark, which covers a variety of settings for evaluating active learning strategies
summary: This paper proposed a benchmark for active learning on tabular data, in particular, for tabular classification tasks. This aims to fix the issue of the lack of consistent benchmarks for evaluating various active learning methods in various settings with different combinations of learning algorithms and query strategies. The authors also attempt to perform extensive experiments to evaluate the effectiveness of the proposed benchmark.
weaknesses: W1: The scope of this paper is quite narrow, only focusing on tabular classification and only a subset of active learning strategies. However, ICML usually focuses on more general areas including computer vision and NLP. Considering that active learning strategies have been broadly used in those areas, it would be better to take those settings and solutions from those areas into account
W2: I think more learning algorithms should be included, such as the recently emerging transformer model for tabular data, such as the model proposed by "TabTransformer: Tabular Data Modeling Using Contextual Embeddings".
W3: I guess it would be better to discuss some surprising findings by using the proposed benchmark. Although the authors mentioned the decrease in performance in some settings in the experiment section, it would be better to discuss more on that aspect, in particular those ignored by existing studies, thus highlighting the necessity of the proposed benchmark.

Meta-review: While the reviews agreed that a paper on this very topic is needed to improve the quality of the work on active learning, they also felt that there is still too much room for improvement in the current version and better to have a full revision before publication would be OK. Although this is certainly a disappointment, the message should rather be read as: you are on the right track, keep it up!

Decision: Reject