ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data

Valentin Margraf, Marcel Wever, Sandra Gilhuber, Gabriel Marques Tavares, Thomas Seidl, Eyke Hüllermeier·June 25, 2024

Summary

ALPBench is a proposed benchmark for active learning in tabular data, aimed at standardizing comparison of query strategies in resource-constrained scenarios. It consists of 86 real-world datasets, 5 settings, and 430 problems, ensuring reproducibility through built-in measures. The benchmark evaluates 72 pipelines with various algorithms, like GBDT, deep learning, and Random Forest. A comprehensive study using 9 query strategies and 8 learners found that TabPFN, Catboost, and Random Forest perform well, highlighting the importance of considering different algorithms. ALPBench is available as a Python package, facilitating research in industries with limited labeled data. The study reveals that performance varies depending on the learner, query strategy, and dataset characteristics, with some methods showing superiority in specific settings. ALPBench contributes to advancing the field by promoting research on adaptive query strategies and the impact of hyperheuristics.

Key findings

10

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the problem of benchmarking active learning pipelines on tabular data . This is a new problem as the paper introduces a new library for active learning benchmarking under the MIT license . The focus is on providing a benchmark for active learning pipelines specifically tailored to tabular data, which involves evaluating and comparing the performance of different active learning strategies in this context.


What scientific hypothesis does this paper seek to validate?

The scientific hypothesis that the paper seeks to validate is related to the design and evaluation of active learning pipelines on tabular data. The paper aims to validate the hypothesis that by providing a standardized setting for benchmarking active learning pipelines and considering crucial aspects of pipeline synthesis, insights can be extracted from the performance of different configurations . The focus is on constructing active learning pipelines using various combinations of learning algorithms and query strategies to improve the efficiency and effectiveness of the pipeline . The paper also discusses the limitations of the work performed, highlighting additional perspectives that should be considered when investigating active learning pipelines .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data" introduces several novel contributions in the field of active learning pipelines .

  • The paper presents a new library for active learning benchmarking under the MIT license, providing a standardized setting for active learning pipeline benchmarking .
  • It carefully tailors claims based on experimental design and results, highlighting the importance of considering all steps of pipeline creation in benchmarks .
  • The paper discusses various query strategies (QSs) for active learning, categorizing them into information-based, representation-based, and hybrid strategies, and implements different approaches like margin sampling, entropy sampling, and least-confident sampling .
  • Additionally, the paper introduces the ALPBench benchmark in 2024, which offers a comprehensive evaluation of different learning algorithms and query strategies for active learning pipelines .
  • Furthermore, the paper emphasizes the importance of reproducibility by providing open access to data and code, enabling readers to replicate the experiments reported in the paper .
  • The authors also address the limitations of their work, discussing possible additional perspectives that should be considered when investigating active learning pipelines .
  • Overall, the paper contributes to advancing the field of active learning pipelines by providing a benchmark, discussing query strategies, ensuring reproducibility, and acknowledging the limitations of the proposed approaches. The paper "ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data" introduces several characteristics and advantages compared to previous methods in the field of active learning pipelines .
  • One key characteristic is the introduction of a new library for active learning benchmarking under the MIT license, providing a standardized setting for benchmarking active learning pipelines .
  • The paper emphasizes the importance of carefully tailoring claims based on experimental design and results, ensuring a comprehensive evaluation of different learning algorithms and query strategies for active learning pipelines .
  • It categorizes query strategies into information-based, representation-based, and hybrid strategies, implementing various approaches like margin sampling, entropy sampling, and least-confident sampling .
  • Additionally, the paper focuses on reproducibility by providing open access to data and code, enabling the replication of experiments reported in the paper .
  • The authors acknowledge the limitations of their work and discuss additional perspectives that should be considered when investigating active learning pipelines, contributing to the advancement of the field . Overall, the characteristics and advantages of the paper lie in its novel library for benchmarking, careful experimental design, diverse query strategies, emphasis on reproducibility, and acknowledgment of limitations, setting it apart from previous methods in the domain of active learning pipelines.

Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of active learning pipelines on tabular data. Noteworthy researchers in this field include Gasperin , Geurts, Ernst, and Wehenkel , Gilhuber, Beer, Ma, and Seidl , Grinsztajn, Oyallon, and Varoquaux , Guo and Greiner , Guo and Schuurmans , Bahri, Jiang, Schuster, and Rostamizadeh , Beluch, Genewein, Nürnberger, and Köhler , Citovsky, DeSalvo, Gentile, Karydas, Rajagopalan, Rostamizadeh, and Kumar , Cohn , Dasgupta and Hsu , and many others mentioned in the references of the ALPBench paper .

The key to the solution mentioned in the paper revolves around the development of active learning pipelines for tabular data, focusing on the evaluation of different combinations of query strategies and learning algorithms. The paper provides a benchmark called ALPBench that allows for the comparison of various active learning pipelines to demonstrate their effectiveness in different scenarios . The solution emphasizes the importance of maintaining consistent configurations for future studies, conducting large-scale experimental studies, and providing logging facilities to monitor the active learning process, including labeling statistics and learner performances .


How were the experiments in the paper designed?

The experiments in the paper were designed by conducting an empirical study that compared various active learning pipelines composed of different combinations of query strategies (QSs) and learning algorithms . The study investigated the effectiveness of 9 QSs paired with 8 learning algorithms, making it the most extensive study on active learning pipelines . The experimental setup was explained in Section 5.1, detailing the selection of 86 real-world datasets and the inclusion of different types of QSs and learning algorithms . The study aimed to compare the performance of different combinations of QSs and learning algorithms on tabular classification tasks, limiting the training time of learning algorithms to 180 seconds per iteration to contain computational costs .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study on active learning pipelines is comprised of 86 real-world datasets from the OpenML-CC18 and the TabZilla benchmark suites . The code for the study is open source, as indicated by the guidelines that encourage the release of code and data, although it is acknowledged that releasing code and data may not always be feasible .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results in the paper provide strong support for the scientific hypotheses that need to be verified. The paper ensures reproducibility of the main experimental results by providing detailed descriptions on experiment formulations, datasets, configurations, and measurements . It discloses all the information necessary to reproduce the main experimental results, which significantly impacts the main claims and conclusions of the paper . Additionally, the paper follows the NeurIPS Code of Ethics, ensuring that the research conducted conforms to ethical standards without causing harm . The authors thoroughly discuss the societal impacts of their work, addressing both potential positive and negative impacts in the broader impact statement . Furthermore, the paper accurately reflects its contributions and scope, presenting claims that align with the experimental design and results obtained . The limitations of the work are also discussed, highlighting additional perspectives that should be considered when investigating active learning pipelines .


What are the contributions of this paper?

The contributions of the paper "ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data" include:

  • Providing a standardized setting for benchmarking active learning pipelines .
  • Extracting insights from the performance of different configurations in active learning pipelines .
  • Discussing the limitations of the work performed by the authors, highlighting additional perspectives that should be considered in investigating active learning pipelines .
  • Offering convenience functionalities to facilitate large-scale experimental studies, such as a cross-product experiment grid and logging facilities to observe the active learning process .
  • Demonstrating the usefulness of ALPBench through an empirical study comparing various active learning pipelines composed of different combinations of query strategies .

What work can be continued in depth?

Work that can be continued in depth typically involves projects or tasks that require further analysis, research, or development. This could include:

  1. Research projects that require more data collection, analysis, and interpretation.
  2. Complex problem-solving tasks that need further exploration and experimentation.
  3. Long-term projects that require detailed planning and execution.
  4. Skill development activities that require continuous practice and improvement.
  5. Creative endeavors that benefit from deeper exploration and refinement.

If you have a specific area of work in mind, feel free to provide more details so I can offer more tailored suggestions.


Introduction
Background
Standardization of active learning comparison in resource-constrained scenarios
Objective
To facilitate research and comparison of query strategies in limited labeled data settings
Methodology
Dataset Composition
86 Real-World Datasets
Diverse range of domains and sizes
5 Settings
Resource constraints, data complexity, and task variations
430 Problems
Comprehensive coverage for different scenarios
Pipeline Evaluation
72 Pipelines
GBDT, deep learning, Random Forest, and other algorithms
9 Query Strategies
Comprehensive selection for strategy analysis
8 Learners
Evaluation across different machine learning models
Performance Analysis
TabPFN, Catboost, and Random Forest as top performers
Impact of algorithm choice on performance
Availability
Python package for easy implementation and reproduction
Key Findings
Performance variations based on learner, query strategy, and dataset characteristics
Emphasis on adaptive query strategies and hyperheuristics
Applications and Implications
Advancing active learning research in industries with limited labeled data
Encouraging exploration of tailored solutions for specific scenarios
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
Which types of algorithms are evaluated in the ALPBench benchmark?
What are the main findings of the comprehensive study using ALPBench?
How many datasets, settings, and problems are included in ALPBench?
What is ALPBench primarily designed for?

ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data

Valentin Margraf, Marcel Wever, Sandra Gilhuber, Gabriel Marques Tavares, Thomas Seidl, Eyke Hüllermeier·June 25, 2024

Summary

ALPBench is a proposed benchmark for active learning in tabular data, aimed at standardizing comparison of query strategies in resource-constrained scenarios. It consists of 86 real-world datasets, 5 settings, and 430 problems, ensuring reproducibility through built-in measures. The benchmark evaluates 72 pipelines with various algorithms, like GBDT, deep learning, and Random Forest. A comprehensive study using 9 query strategies and 8 learners found that TabPFN, Catboost, and Random Forest perform well, highlighting the importance of considering different algorithms. ALPBench is available as a Python package, facilitating research in industries with limited labeled data. The study reveals that performance varies depending on the learner, query strategy, and dataset characteristics, with some methods showing superiority in specific settings. ALPBench contributes to advancing the field by promoting research on adaptive query strategies and the impact of hyperheuristics.
Mind map
Emphasis on adaptive query strategies and hyperheuristics
Performance variations based on learner, query strategy, and dataset characteristics
Python package for easy implementation and reproduction
Impact of algorithm choice on performance
TabPFN, Catboost, and Random Forest as top performers
Evaluation across different machine learning models
8 Learners
Comprehensive selection for strategy analysis
9 Query Strategies
GBDT, deep learning, Random Forest, and other algorithms
72 Pipelines
Comprehensive coverage for different scenarios
430 Problems
Resource constraints, data complexity, and task variations
5 Settings
Diverse range of domains and sizes
86 Real-World Datasets
To facilitate research and comparison of query strategies in limited labeled data settings
Standardization of active learning comparison in resource-constrained scenarios
Encouraging exploration of tailored solutions for specific scenarios
Advancing active learning research in industries with limited labeled data
Key Findings
Availability
Performance Analysis
Pipeline Evaluation
Dataset Composition
Objective
Background
Applications and Implications
Methodology
Introduction
Outline
Introduction
Background
Standardization of active learning comparison in resource-constrained scenarios
Objective
To facilitate research and comparison of query strategies in limited labeled data settings
Methodology
Dataset Composition
86 Real-World Datasets
Diverse range of domains and sizes
5 Settings
Resource constraints, data complexity, and task variations
430 Problems
Comprehensive coverage for different scenarios
Pipeline Evaluation
72 Pipelines
GBDT, deep learning, Random Forest, and other algorithms
9 Query Strategies
Comprehensive selection for strategy analysis
8 Learners
Evaluation across different machine learning models
Performance Analysis
TabPFN, Catboost, and Random Forest as top performers
Impact of algorithm choice on performance
Availability
Python package for easy implementation and reproduction
Key Findings
Performance variations based on learner, query strategy, and dataset characteristics
Emphasis on adaptive query strategies and hyperheuristics
Applications and Implications
Advancing active learning research in industries with limited labeled data
Encouraging exploration of tailored solutions for specific scenarios
Key findings
10

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the problem of benchmarking active learning pipelines on tabular data . This is a new problem as the paper introduces a new library for active learning benchmarking under the MIT license . The focus is on providing a benchmark for active learning pipelines specifically tailored to tabular data, which involves evaluating and comparing the performance of different active learning strategies in this context.


What scientific hypothesis does this paper seek to validate?

The scientific hypothesis that the paper seeks to validate is related to the design and evaluation of active learning pipelines on tabular data. The paper aims to validate the hypothesis that by providing a standardized setting for benchmarking active learning pipelines and considering crucial aspects of pipeline synthesis, insights can be extracted from the performance of different configurations . The focus is on constructing active learning pipelines using various combinations of learning algorithms and query strategies to improve the efficiency and effectiveness of the pipeline . The paper also discusses the limitations of the work performed, highlighting additional perspectives that should be considered when investigating active learning pipelines .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data" introduces several novel contributions in the field of active learning pipelines .

  • The paper presents a new library for active learning benchmarking under the MIT license, providing a standardized setting for active learning pipeline benchmarking .
  • It carefully tailors claims based on experimental design and results, highlighting the importance of considering all steps of pipeline creation in benchmarks .
  • The paper discusses various query strategies (QSs) for active learning, categorizing them into information-based, representation-based, and hybrid strategies, and implements different approaches like margin sampling, entropy sampling, and least-confident sampling .
  • Additionally, the paper introduces the ALPBench benchmark in 2024, which offers a comprehensive evaluation of different learning algorithms and query strategies for active learning pipelines .
  • Furthermore, the paper emphasizes the importance of reproducibility by providing open access to data and code, enabling readers to replicate the experiments reported in the paper .
  • The authors also address the limitations of their work, discussing possible additional perspectives that should be considered when investigating active learning pipelines .
  • Overall, the paper contributes to advancing the field of active learning pipelines by providing a benchmark, discussing query strategies, ensuring reproducibility, and acknowledging the limitations of the proposed approaches. The paper "ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data" introduces several characteristics and advantages compared to previous methods in the field of active learning pipelines .
  • One key characteristic is the introduction of a new library for active learning benchmarking under the MIT license, providing a standardized setting for benchmarking active learning pipelines .
  • The paper emphasizes the importance of carefully tailoring claims based on experimental design and results, ensuring a comprehensive evaluation of different learning algorithms and query strategies for active learning pipelines .
  • It categorizes query strategies into information-based, representation-based, and hybrid strategies, implementing various approaches like margin sampling, entropy sampling, and least-confident sampling .
  • Additionally, the paper focuses on reproducibility by providing open access to data and code, enabling the replication of experiments reported in the paper .
  • The authors acknowledge the limitations of their work and discuss additional perspectives that should be considered when investigating active learning pipelines, contributing to the advancement of the field . Overall, the characteristics and advantages of the paper lie in its novel library for benchmarking, careful experimental design, diverse query strategies, emphasis on reproducibility, and acknowledgment of limitations, setting it apart from previous methods in the domain of active learning pipelines.

Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of active learning pipelines on tabular data. Noteworthy researchers in this field include Gasperin , Geurts, Ernst, and Wehenkel , Gilhuber, Beer, Ma, and Seidl , Grinsztajn, Oyallon, and Varoquaux , Guo and Greiner , Guo and Schuurmans , Bahri, Jiang, Schuster, and Rostamizadeh , Beluch, Genewein, Nürnberger, and Köhler , Citovsky, DeSalvo, Gentile, Karydas, Rajagopalan, Rostamizadeh, and Kumar , Cohn , Dasgupta and Hsu , and many others mentioned in the references of the ALPBench paper .

The key to the solution mentioned in the paper revolves around the development of active learning pipelines for tabular data, focusing on the evaluation of different combinations of query strategies and learning algorithms. The paper provides a benchmark called ALPBench that allows for the comparison of various active learning pipelines to demonstrate their effectiveness in different scenarios . The solution emphasizes the importance of maintaining consistent configurations for future studies, conducting large-scale experimental studies, and providing logging facilities to monitor the active learning process, including labeling statistics and learner performances .


How were the experiments in the paper designed?

The experiments in the paper were designed by conducting an empirical study that compared various active learning pipelines composed of different combinations of query strategies (QSs) and learning algorithms . The study investigated the effectiveness of 9 QSs paired with 8 learning algorithms, making it the most extensive study on active learning pipelines . The experimental setup was explained in Section 5.1, detailing the selection of 86 real-world datasets and the inclusion of different types of QSs and learning algorithms . The study aimed to compare the performance of different combinations of QSs and learning algorithms on tabular classification tasks, limiting the training time of learning algorithms to 180 seconds per iteration to contain computational costs .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study on active learning pipelines is comprised of 86 real-world datasets from the OpenML-CC18 and the TabZilla benchmark suites . The code for the study is open source, as indicated by the guidelines that encourage the release of code and data, although it is acknowledged that releasing code and data may not always be feasible .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results in the paper provide strong support for the scientific hypotheses that need to be verified. The paper ensures reproducibility of the main experimental results by providing detailed descriptions on experiment formulations, datasets, configurations, and measurements . It discloses all the information necessary to reproduce the main experimental results, which significantly impacts the main claims and conclusions of the paper . Additionally, the paper follows the NeurIPS Code of Ethics, ensuring that the research conducted conforms to ethical standards without causing harm . The authors thoroughly discuss the societal impacts of their work, addressing both potential positive and negative impacts in the broader impact statement . Furthermore, the paper accurately reflects its contributions and scope, presenting claims that align with the experimental design and results obtained . The limitations of the work are also discussed, highlighting additional perspectives that should be considered when investigating active learning pipelines .


What are the contributions of this paper?

The contributions of the paper "ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data" include:

  • Providing a standardized setting for benchmarking active learning pipelines .
  • Extracting insights from the performance of different configurations in active learning pipelines .
  • Discussing the limitations of the work performed by the authors, highlighting additional perspectives that should be considered in investigating active learning pipelines .
  • Offering convenience functionalities to facilitate large-scale experimental studies, such as a cross-product experiment grid and logging facilities to observe the active learning process .
  • Demonstrating the usefulness of ALPBench through an empirical study comparing various active learning pipelines composed of different combinations of query strategies .

What work can be continued in depth?

Work that can be continued in depth typically involves projects or tasks that require further analysis, research, or development. This could include:

  1. Research projects that require more data collection, analysis, and interpretation.
  2. Complex problem-solving tasks that need further exploration and experimentation.
  3. Long-term projects that require detailed planning and execution.
  4. Skill development activities that require continuous practice and improvement.
  5. Creative endeavors that benefit from deeper exploration and refinement.

If you have a specific area of work in mind, feel free to provide more details so I can offer more tailored suggestions.

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