computer science

International Conference on Machine Learning

International Conference on Machine Learning is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand International Conference on Machine Learning rather than just read about it. In short: 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.

Key takeaways

International Conference on Machine Learning belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect International Conference on Machine Learning to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of International Conference on Machine Learning from memory before moving on to harder problems.

Reference excerpt

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. It is organized by the International Machine Learning Society (IMLS). Precise dates vary year to year, but paper submissions are generally due at the end of January, and the conference is generally held the following July.

History ICML began as the International Workshop on Machine Learning, first held at Carnegie Mellon University in Pittsburgh in July 1980 and organized by Jaime Carbonell, Ryszard S. Michalski, and Tom M. Mitchell. In 1993 it transitioned to a full conference series under the current name, building on nearly a decade of workshops. Since the 2010s its proceedings have been published open-access in the Proceedings of Machine Learning Research (PMLR), which grew out of JMLR’s Workshop & Conference Proceedings and was renamed PMLR in 2015. ICML has published many important research papers that form the foundation of modern Artificial Intelligence. Some 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 the UC Berkeley's Soft Actor-Critic (ICML 2018). The number of submitted (and accepted) papers has sharply increased within 10 years; from 1037 (270) in 2015 to 12107 (3260) in 2025. Meanwhile, the acceptance rate continues to average between 21 % (2020) and 30 % (2024). 2026 marked a new record with 24,371 submitted papers, more than doubling the previous year. Accepted submissions are presented on-site as posters; furthermore, particularly outstanding contributions are presented orally or are designated by a committee as "spotlight papers." In 2026, 2.2% of submitted contributions were designated as "spotlight" and 0.7% additionally as "oral". In addition, the best contribution is honored annually by a jury with an "Outstanding Paper Award" (formally "Best Paper Award").

Profile ICML’s scope spans the full breadth of machine learning, with particular emphasis on theoretical analysis, algorithmic innovation, and statistical learning. Compared to the other two big machine learning conferences NeurIPS and ICLR, ICML traditionally features more content on statistical learning theory, reinforcement learning and robotics, and optimization theory. Its annual proceedings are published in the open-access Proceedings of Machine Learning Research (PMLR). ICML attracts sponsors seeking access to ML research and talent. Technology companies such as Google, Microsoft, Amazon, Meta, Apple are among sponsors, regularly publishing their research and recruiting researchers at the conference. Financial firms, such as Citadel Securities, Jane Street Capital and D.E. Shaw, also often have a presence at ICML.

Locations

See also ICLR Journal of Machine Learning Research Machine Learning (journal) NeurIPS

References

External links icml.cc ICML website machinelearning.org Website of the International Machine Learning Society

Worked examples

Example 1 — a first encounter with International Conference on Machine Learning

Start with the simplest possible case. Write down what International Conference on Machine Learning claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to International Conference on Machine Learning before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about International Conference on Machine Learning ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of International Conference on Machine Learning

In research: International Conference on Machine Learning appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses International Conference on Machine Learning in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: International Conference on Machine Learning is common in secondary-school and first-year university syllabi. It links to neighbouring topics Artificial intelligence conferences, so understanding it makes those chapters shorter.
In everyday life: Look for International Conference on Machine Learning outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study International Conference on Machine Learning in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what International Conference on Machine Learning means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain International Conference on Machine Learning out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is International Conference on Machine Learning in simple terms?

Why does International Conference on Machine Learning matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study International Conference on Machine Learning?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on International Conference on Machine Learning.