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# AutoML-Space

The Space of AutoML

## Sitemaps

- [XML Sitemap](https://automl.space/sitemap.xml): Contains all public & indexable URLs for this website.

## Posts

- [Blog](https://automl.space/blog/)
- [How many repetitions for reliable AutoRL? - Efficient Heteroscedastic Bayesian Optimization for Risk-Aware AutoRL](https://automl.space/erahbo/) - TL;DR We present ERAHBO, a method that improves RL hyperparameter optimization by adaptively managing run-to-run randomness. Compared to a fixed evaluation budget per configuration, ERAHBO uses an adaptive stopping rule to halt evaluation of bad configurations immediately. Supported by a new, large-scale benchmark dataset with 50 seeds per configuration, ERAHBO efficiently identifies reliable hyperparameters across
- [ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning](https://automl.space/arlbench/) - Highlight: ARLBench provides a standardized AutoRL benchmark by combining JAX-powered implementations with representative subsets, making high-performance AutoRL research accessible. Speed: Delivers up to 10x faster evaluation than standard libraries, using JAX-based training and statistically selected environment subsets. Flexibility: Supports static, multi-fidelity, and dynamic HPO (like PBT) through a Gymnasium-like interface with full checkpointing capabilities. Validity:
- [Provable Sample Cost Reduction in Prior-Guided Hyperparameter Optimization](https://automl.space/provable-sample-cost-reduction-in-prior-guided-hyperparameter-optimization/) - We set out to understand: How exactly does prior informativeness translate into lower sample complexity? And how can we exploit that safely in practice? Reframing HPO Through the Lens of ϵ-Best-Arm Identification To answer these questions, we model HPO as a fixed-budget ϵ-Best-Arm Identification (BAI) problem. Each configuration becomes an arm with an unknown full-fidelity
- [Auto-nnU-Net: towards Automated Medical Image Segmentation](https://automl.space/auto-nnu-net-towards-automated-medical-image-segmentation/) - Medical Image Segmentation (MIS) encompasses a variety of objectives, ranging from bone segmentation to organ segmentation, with each task presenting its own set of difficulties in identifying an optimal segmentation model. While the state-of-the-art nnU-Net framework has successfully automated many aspects of model configuration, it surprisingly remains constrained by fixed hyperparameters and manually designed heuristics.
- [When Are RL Hyperparameters Benign? Disentangling Objective and Data Quality](https://automl.space/when-are-rl-hyperparameters-benign-disentangling-objective-and-data-quality/) - When it comes to training Deep Reinforcement Learning (RL) agents, the experience can often be described as "hyperparameter hell" - small changes to learning rate, target update rates, or regularization can flip a run from strong performance to complete collapse. Worse, the knobs that matter can change over training, as previously demonstrated by (TODO: Add
- [DynaBO: Allowing Expert users to Speedup HPO](https://automl.space/dynabo-allowing-expert-users-to-speedup-hpo/) - Automatic Hyperparameter Optimization (HPO) has proven its usefulness across various applications, but users still prefer to retain control over the selected hyperparameters. To tackle this, we recently proposed a new approach for Dynamic Bayesian Optimization (DynaBO) that allows users to exert dynamic control visualised below. Motivation for DynaBO While Hyperparameter optimization is great at finding
- [Neural Attention Search: NAS beyond Layer Level](https://automl.space/neural-attention-search-nas-beyond-layer-level/) - Neural Architecture Search (NAS) has achieved great success by searching for optimal architectures for specific tasks by selecting the best operations across different layers. However, in the era of Large Language Models (LLMs), people tend to use a single model with a unified architecture, e.g., the transformer, and apply it to diverse tasks. However, this does
- [Towards Token-Level Hybrid Attention Architectures](https://automl.space/towards-token-level-hybrid-attention-architectures/) - The softmax attention models have become a keystone in modern large language models (LLMs). However, the quadratic computational and linear memory complexities have gradually become bottlenecks in the long term. In the meantime, recent advances in linear attention models have provided another promising direction for more efficient attention models. Linear attention replaces the non-linear softmax
- [AutoML@AAAI26](https://automl.space/automlaaai26/) - Having recently returned from the 40th Annual AAAI Conference on Artificial Intelligence (AAAI-26) held in Singapore, I was curious to see the current trajectory of Automated Machine Learning (AutoML) research within our community. Since AutoML concerns all kinds of AI research, it was great to see many AutoML papers at AAAI. Let me give you
- [Dynamic Hyperparameter Importance for Multi-Objective AutoML](https://automl.space/dynamic-hyperparameter-importance-for-multi-objective-automl/) - Multi-objective hyperparameter optimization is now routine in many application settings, where practitioners must balance predictive performance against competing objectives such as inference time, model size, or energy consumption. Despite this, most hyperparameter optimization methods for multi-objective problems still treat the hyperparameter space as static: all dimensions are assumed to be equally relevant throughout the search,
- [HyperSHAP: Opening the Black Box of Hyperparameter Optimization with Explainable AI](https://automl.space/hypershap-opening-the-black-box-of-hyperparameter-optimization-with-explainable-ai/) - Which value of a hyperparameter configuration was doing the trick? Why does tuning optimizer momentum help only when weight decay is tuned as well? And why does the optimizer keep “missing” the best configuration? These are the kinds of questions every machine learning practitioner eventually asks, and often struggles to answer. Hyperparameter optimization (HPO) delivers
- [10 years of Auto-sklearn](https://automl.space/10-years-of-auto-sklearn/) - With NeurIPS just around the corner let’s take some time to reflect on Auto-sklearn, a project that we published ten years ago at NeurIPS and that started my career as a machine learning researcher and brought me into my current position as assistant professor for "AutoML and Optimization" at TU Dortmund University and the Lamarr
- [AutoML'25: Iterative Monte Carlo Tree Search for NAS](https://automl.space/automl25-iterative-monte-carlo-tree-search-for-nas/) - Submitted by Mehraveh Javan Roshtkhari, Matthew Toews and Marco Pedersoli as part of the AutoML’25 conference The architecture of a neural network (number of layers, connections, operations etc.) has a great impact on its performance: a well-designed architecture can lead to significant improvements in a model's accuracy and efficiency. Neural Architecture Search (NAS) is an automated
- [AutoML'25: What Makes Freezing Layers in Deep Neural Networks Effective? A Linear Separability Perspective](https://automl.space/automl25-what-makes-freezing-layers-in-deep-neural-networks-effective-a-linear-separability-perspective/) - Submitted by Collin Coil and Nick Cheney as part of the AutoML’25 conference Freezing layers in deep neural networks seems counterintuitive. Why would training less of a network lead to faster convergence and better generalization? To our knowledge, no empirical work has been done to explain how freezing layers produces these two benefits. Some prior works
- [AutoML'25: SmartCal: A Novel Automated Approach to Classifier Probability Calibration](https://automl.space/automl25-smartcal-a-novel-automated-approach-to-classifier-probability-calibration/) - Submitted by Abdelrahman et al. as part of the AutoML'25 conference Imagine developing a highly accurate machine learning (ML) model for medical diagnosis that consistently underestimates rare but critical conditions. Lives could be put at risk due to such miscalibrations in predictive probabilities. Similarly, consider a fraud detection system that frequently flags legitimate transactions due
- [AutoML'25: Automated Data Preparation for Machine Learning](https://automl.space/automated-data-preparation-for-machine-learning/) - by Sasa Mladenovic, Marius Lindauer and Carola Doerr Data preparation, though essential for effective machine learning, remains a manual and time-consuming process. It is often overlooked in AutoML, which tends to rely on data already being fit for machine learning. Our paper "Automated Data Preparation for Machine Learning: a Survey" explores how to automate this
- [OptunaHub Benchmarks: A New Feature to Use/Register Various Benchmark Problems](https://automl.space/optunahub-benchmarks-a-new-feature-to-use-register-various-benchmark-problems/) - This entry is a cross post of the OptunaHub Benchmark article written by one of the Optuna developers, Yoshihiko Ozaki. Introduction Performance benchmarking is essential in both research and algorithm development because it enables direct comparisons of under-developing algorithms with some baseline algorithms to quantify the performance for idea verification purposes or to claim the superiority
- [SMAC3 Registered on OptunaHub](https://automl.space/smac3-registered-on-optunahub/) - This entry is a cross post of the SMAC3 article. One of the core developers of SMAC3, Difan Deng, wrote the article below and Shuhei Watanabe from the Optuna team is posting this article on his behalf. SMAC3 is developed by AutoML.org, mainly by the SMAC team, in Germany. AutoML.org is one of the world-leading
- [AutoML Conference 2025 -- See you in New York!](https://automl.space/automl-conference-2025-see-you-in-new-york/) - by Roman Garnett (General chair), Carola Doerr (PC Chair), Jan van Rijn (PC Chair) and Leman Akoglu (PC Chair) After two editions in Europe, the 2025 AutoML conference will return to North America, taking place from 8-11 September in New York City. The AutoML conference provides a platform for high-quality research related to the rich
- [Understanding Hyperparameter Optimization in Machine Learning](https://automl.space/understanding-hyperparameter-optimization-in-machine-learning/) - In the practical application of machine learning to any problem, we will inevitably encounter an essential concept: Hyperparameter Optimization. You might be wondering, "What on earth are hyperparameters, and why should I care about optimizing them?" Well, let’s break it down in the simplest way possible. What Are Hyperparameters? Think of hyperparameters as the settings
- [Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning](https://automl.space/interactive-hyperparameter-optimization-in-multi-objective-problems-via-preference-learning/) - Authors: Joseph Giovanelli, Alexander Tornede, Tanja Tornede and Marius Lindauer Motivation Hyperparameter optimization (HPO) is a critical aspect of maximizing the performance of machine learning (ML) models. In real-world applications, ML practitioners often face multi-objective (MO) problems, which involve optimizing conflicting objectives such as accuracy and energy consumption. Since there is not a single optimal
- [carps: A Framework for Comparing N Hyperparameter Optimizers on M Benchmarks](https://automl.space/carps-a-framework-for-comparing-n-hyperparameter-optimizers-on-m-benchmarks/) - Authors: Carolin Benjamins, Helena Graf, Sarah Segel, Difan Deng, Tim Ruhkopf, Leona Hennig, Soham Basu, Neeratyoy Mallik, Edward Bergman, Deyao Chen, Francois Clément, Matthias Feurer, Katharina Eggensperger, Frank Hutter, Carola Doerr, Marius Lindauer carps in a nutshell (tl;dr) framework for benchmarking N optimization methods on M benchmarks lightweight interface between HPO optimizers and benchmarks many
- [Selecting Instances and Improving Generalization of Dynamic Algorithm Configuration](https://automl.space/selecting-instances-and-improving-generalization-of-dynamic-algorithm-configuration/) - Authors: Carolin Benjamins, Gjorgjina Cenikj, Ana Nikolikj, Aditya Mohan, Tome Eftimov , Marius Lindauer TL;DR: In Dynamic Algorithm Configuration (DAC) [Biedenkapp et al., 2020] we aim to improve generalization of Reinforcement Learning (RL) agents as dynamic algorithm configurators. For this, we subselect representative instances to train the RL agent on. Our main insight is that
- [Position: A Call to Action for a Human-Centered AutoML Paradigm](https://automl.space/position-a-call-to-action-for-a-human-centered-automl-paradigm/) - Paper Authors Marius Lindauer, Florian Karl, Anne Klier, Julia Moosbauer, Alexander Tornede, Andreas Mueller, Frank Hutter, Matthias Feurer, Bernd Bischl Motivation Automated Machine Learning (AutoML) has significantly transformed the machine learning landscape by automating the creation and optimization of ML models. This has opened up a path towards democratized ML, making it accessible to a
- [Review of the Year 2023 - AutoML Hannover](https://automl.space/review-of-the-year-2023-automl-hannover/) - by the AutoML Hannover Team The year 2023 was the most successful for us as a (still relatively young) AutoML group in Hannover. With the start of several big projects, including the ERC starting grant on interactive and explainable AutoML and a BMUV-funded project on Green AutoML, the group has grown and we were able
- [AutoRL: AutoML for RL](https://automl.space/autorl-automl-for-rl/) - An increasingly important subfield of AutoML is AutoRL: Automated Reinforcement Learning. This blog post is an introduction to the most important questions of this field:

## Pages

- [Home](https://automl.space/) - What is AutoML? Accessibility and Ease of Use AutoML lowers the barriers to entry in machine learning, allowing anyone—regardless of expertise level—to build, test, and deploy ML models quickly. By automating complex tasks, it empowers non-experts to work with data and leverage AI insights effectively. Efficiency and Speed AutoML accelerates the model development process. It
- [AutoML-related Events](https://automl.space/automl-related-events/) - If you are aware of more AutoML-related events, please contribute to automl.space by letting us know.
- [About Us](https://automl.space/about-us/) - Our Mission ... is to make the world of Automated Machine Learning (AutoML) accessible and welcoming to everyone, whether you’re just beginning to explore its potential or are an experienced practitioner looking to stay up-to-date. This website is a one-stop hub for all things AutoML: from foundational resources and guides to the latest research, tools,
- [Locations](https://automl.space/collaborations/) - Automated Machine Learning (AutoML) has rapidly evolved into a transformative field, empowering researchers, data scientists, and organizations worldwide to harness the full potential of machine learning with greater efficiency and accessibility. On this page, we invite you to explore a curated map of leading AutoML-related research groups around the globe. From North America to Europe,
- [Optuna](https://automl.space/automl-tools/optuna/) - Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. It features an imperative, define-by-run style user API. Thanks to our define-by-run API, the code written with Optuna enjoys high modularity, and the user of Optuna can dynamically construct the search spaces for the hyperparameters. Optuna has its ecosystem to enhance the
- [Event Venues](https://automl.space/event-venues/)
- [Submit Organizer Form](https://automl.space/submit-organizer-form/)
- [Organizer Dashboard](https://automl.space/organizer-dashboard/)
- [Event Organizers](https://automl.space/event-organizers/)
- [Submit Venue Form](https://automl.space/submit-venue-form/)
- [Venue Dashboard](https://automl.space/venue-dashboard/)
- [Open-Source AutoML Tools](https://automl.space/automl-tools/) - The AutoML community offers a wide variety of open-source packages to meet diverse machine learning needs, whether you're optimizing neural architectures, tuning hyperparameters, or configuring algorithms. From libraries for Neural Architecture Search (NAS) to powerful hyperparameter optimization (HPO) packages, there’s something for every machine learning workflow. Each package comes with specific features that can be
- [AutoGluon](https://automl.space/automl-tools/autogluon/) - Website: auto.gluon.ai Brief Description: AutoGluon is an open source AutoML framework that focuses on accuracy, speed, and ease of use. With just three lines of code, users can train accurate ML models for tabular, time series, or multimodal data. AutoGluon automatically builds a diverse set of models and ensembles them to maximize the predictive accuracy.
- [Videos](https://automl.space/videos/) - By now, several YT channels provide videos about current AutoML research. Below, you can find some of them and play them directly. Please note that these are rather not meant for beginners, but more advanced practitioners or researchers. If you are looking for material to get started with AutoML, please have a look at the
- [What is AutoML?](https://automl.space/about/) - AutoML, short for Automated Machine Learning, is all about making machine learning (ML) easier and faster to use. Instead of manually doing all the complex steps that go into building and tuning an ML model, AutoML systems support you in most of these steps, saving time and improving results. AutoML simplifies many challenging parts of
- [Privacy Statement](https://automl.space/privacy-statement/) - This Privacy Notice for Insitute of AI at Leibniz University Hannover ("we," "us," or "our"), describes how and why we might access, collect, store, use, and/or share ("process") your personal information when you use our services ("Services"), including when you: Visit our website at automl.space, or any website of ours that links to this Privacy
- [Contribute](https://automl.space/contribute/) - Are you a researcher in AutoML?If you are a researcher in AutoML and would like to promote your work as a blog post in the AutoML Space, send us your contribution to contribute[at]automl.space. This can be a paper summary, conference coverage, opinion pieces, tutorials, and videos. Some examples can be seen here: Blog Posts. Do
- [Literature Overview](https://automl.space/literature-overview/) - Surveys Several survey articles provide a good starting point for getting an overview of AutoML breakthroughs, approaches, and applications. Among others, we recommend the following surveys: "AutoML: A Survey of the State-of-the-Art" by Xin He, Kaiyong Zhao and Xiaowen Chu (published 2019; last updated 2021) "Automated machine learning: past, present and future" by Mitra Baratchi,
- [Related Networks](https://automl.space/coseal/) - AutoMLers and Friends on BlueSky Website BlueSky is a growing social network with an active community, particularly on AI topics. More and more AutoML people are active there. Join us to spread ideas on AutoML. As a very nice feature, there are starter packs to quickly find people you can follow and reach out
- [test](https://automl.space/test/)
- [Community](https://automl.space/community/) - AutoML is a thriving field. Here is an overview of community initiatives designed to connect people and topics: talks, networks, podcasts and more. Related networks Networks of researchers working on related topics, e.g., algorithm selection, meta-learning, benchmarking... AutoML Podcast Tune in for insights into the science and engineering behind AutoML. AutoML Seminar Lorem ipsum dolor
- [AutoML Seminar](https://automl.space/automl-seminar/) - The AutoML seminar is a platform for AutoML researchers to share and discuss their work via a series of talks that is also published on YouTube for greater reach. It's an excellent resource for up-to-date topics and interaction within the community.
- [Job Market](https://automl.space/job-market/)
- [AutoML Podcast](https://automl.space/automl-podcast/)

## Events

- [AutoML Conference 2026](https://automl.space/event/automl-conference-2026/) - International Conference on Automated Machine Learning
- [COnfiguration and SElection of ALgorithms 2026](https://automl.space/event/configuration-and-selection-of-algorithms-2026/) - COSEAL is a workshop format for an international group of researchers focusing on algorithm selection and algorithm configuration. Both problems can be seen as instantiations of automated algorithm design, i.e., automatically optimizing the performance of algorithms.

## Organizers

- [Roman Garnett et al.](https://automl.space/event-organizer/roman-garnett-et-al/)
- [Katharina Eggensperger](https://automl.space/event-organizer/katharina-eggensperger/)
- [Carlos Soares](https://automl.space/event-organizer/carlos-soares/)

## Categories

- [Uncategorized](https://automl.space/category/uncategorized/)
- [AutoRL](https://automl.space/category/autorl/)
- [iAutoML](https://automl.space/category/iautoml/)
- [MOO](https://automl.space/category/moo/)
- [DAC](https://automl.space/category/dac/)
- [Benchmarking](https://automl.space/category/benchmarking/)
- [HPO](https://automl.space/category/hpo/)
- [AutoML](https://automl.space/category/automl/)
- [Events](https://automl.space/category/events/)
- [AutoDS](https://automl.space/category/autods/)
- [AutoDL](https://automl.space/category/autodl/)
- [NAS](https://automl.space/category/nas/)

## Tags

- [AutoRL](https://automl.space/tag/autorl/)
- [RL](https://automl.space/tag/rl/)
- [COSEAL](https://automl.space/tag/coseal/)
- [DAC](https://automl.space/tag/dac/)
- [iAutoML](https://automl.space/tag/iautoml/)
- [MOO](https://automl.space/tag/moo/)
- [hyperparameter-importance](https://automl.space/tag/hyperparameter-importance/)
- [benchmarking](https://automl.space/tag/benchmarking/)
- [HPO](https://automl.space/tag/hpo/)
- [AutoML](https://automl.space/tag/automl/)
- [throwback](https://automl.space/tag/throwback/)
- [Events](https://automl.space/tag/events/)
- [Data Science](https://automl.space/tag/data-science/)
- [Machine Learning](https://automl.space/tag/machine-learning/)
- [Artificial Intelligence](https://automl.space/tag/artificial-intelligence/)
- [Programming](https://automl.space/tag/programming/)
- [Technology](https://automl.space/tag/technology/)
- [Science](https://automl.space/tag/science/)
- [Software Engineering](https://automl.space/tag/software-engineering/)
- [Optuna](https://automl.space/tag/optuna/)
- [Multi-Objective Optimization](https://automl.space/tag/multi-objective-optimization/)
- [NAS](https://automl.space/tag/nas/)
- [Sparse Transformer](https://automl.space/tag/sparse-transformer/)
- [Attention Models](https://automl.space/tag/attention-models/)
- [BO](https://automl.space/tag/bo/)
- [Medical AI](https://automl.space/tag/medical-ai/)
- [Theory](https://automl.space/tag/theory/)

## Event types

- [Workshop](https://automl.space/event-type/workshop/)