# First ICML Conference

The International Conference on Machine Learning (ICML) is the oldest and one of the most prestigious academic conferences in machine learning, held annually since 1980. It began as a workshop at Carnegie Mellon University and has grown into a major venue for AI research publications and industry recruitment.

The International Conference on Machine Learning (ICML) is an international academic conference in machine learning held annually since 1980. It is the oldest and, along with NeurIPS and ICLR, one of the three primary conferences of highest impact and reputation in machine learning and artificial intelligence research. Organized by the International Machine Learning Society (IMLS), ICML serves as a central venue for presenting advances in statistical learning theory, algorithmic innovation, and reinforcement learning.

## History

ICML originated as the International Workshop on Machine Learning, first held at [Carnegie Mellon University](https://www.wikiprompt.org/wiki/carnegie-mellon-university) in Pittsburgh in July 1980. The workshop was organized by Jaime Carbonell, Ryszard S. Michalski, and Tom M. Mitchell, who sought to create a focused forum for the emerging field of machine learning. In 1993, the event transitioned to a full conference series under its current name, building on more than a decade of workshops.

Since the 2010s, ICML proceedings have been published open-access in the Proceedings of Machine Learning Research (PMLR), which grew out of the Journal of Machine Learning Research's Workshop and Conference Proceedings and was renamed PMLR in 2015. This shift toward open access has made ICML research widely available to the global research community.

ICML has published numerous foundational papers in modern artificial intelligence. Notable works include Lawrence Berkeley National Laboratory's K-means clustering via PCA (ICML 2004), Google's Batch Normalization, Google Brain's EfficientNet (ICML 2019), OpenAI's CLIP (ICML 2021), and UC Berkeley's Soft Actor-Critic (ICML 2018). These contributions have influenced subsequent developments in [deep learning](https://www.wikiprompt.org/wiki/deep-learning) and [generative AI](https://www.wikiprompt.org/wiki/generative-ai).

## Growth and Submissions

The scale of ICML has expanded dramatically over the past decade. Submissions rose from 1,037 papers (270 accepted) in 2015 to 12,107 submissions (3,260 accepted) in 2025. Acceptance rates have averaged between 21% (2020) and 30% (2024). In 2026, the conference set a new record with 24,371 submitted papers, more than doubling the previous year's total.

Accepted submissions are presented on-site as posters. Particularly outstanding contributions are selected for oral presentations or designated as "spotlight papers" by a committee. In 2026, 2.2% of submitted contributions were designated as spotlights, and 0.7% additionally received oral presentation slots. The best contribution is honored annually with the Outstanding Paper Award, formerly known as the Best Paper Award, selected by a jury.

## Scope and Profile

ICML's scope covers the full breadth of machine learning, with particular emphasis on theoretical analysis, algorithmic innovation, and statistical learning. Compared to the other two major machine learning conferences, NeurIPS and ICLR, ICML traditionally features more content on statistical learning theory, reinforcement learning, robotics, and optimization theory. Its annual proceedings are published in the open-access PMLR.

The conference attracts sponsors seeking access to machine learning research and talent. Technology companies such as Google, Microsoft, Amazon, Meta, and Apple regularly sponsor the event, publish their research there, and recruit researchers on-site. Financial firms including Citadel Securities, Jane Street Capital, and D.E. Shaw also maintain a presence at ICML, reflecting the growing importance of machine learning in quantitative finance.

## Impact on Artificial Intelligence

ICML has played a critical role in advancing [artificial intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence) research. Many techniques now central to modern AI systems, including [neural networks](https://www.wikiprompt.org/wiki/neural-network) and reinforcement learning algorithms, have been presented and refined at ICML. The conference's emphasis on rigorous theoretical foundations has helped establish machine learning as a mature scientific discipline.

The conference also serves as a bridge between academia and industry. Researchers from leading labs such as [Google DeepMind](https://www.wikiprompt.org/wiki/google-deepmind), [OpenAI](https://www.wikiprompt.org/wiki/openai), and [Anthropic](https://www.wikiprompt.org/wiki/anthropic) regularly present their latest work at ICML, while university groups from institutions like [Stanford](https://www.wikiprompt.org/wiki/stanford-ai-lab), [MIT](https://www.wikiprompt.org/wiki/mit-csail), and [UC Berkeley](https://www.wikiprompt.org/wiki/berkeley-ai-research) contribute fundamental research. This collaboration has accelerated the translation of theoretical insights into practical applications.

## See Also

- [Machine Learning](https://www.wikiprompt.org/wiki/machine-learning)
- [Deep Learning](https://www.wikiprompt.org/wiki/deep-learning)
- [Artificial Intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence)
- [Carnegie Mellon University](https://www.wikiprompt.org/wiki/carnegie-mellon-university)

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Source: https://www.wikiprompt.org/wiki/icml-first-edition
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-07T21:33:08.830229+00:00