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ICML 2026 Lay Summaries

We are continuing the lay summary policy for ICML 2026. As we did last year, all authors of accepted papers will be asked to write a lay summary — a general audience summary — about their work. Below is the guidance we shared with authors in 2025. This advice remains fully applicable for 2026, and we encourage all authors to review it carefully as you prepare your submissions.

Written with the help of Julien Besset, Science Communication Advisor at Mila

How to write a lay summary of your abstract

Starting in 2025, all authors of accepted papers at ICML will be asked to write a lay summary, or general audience summary, about their work. This is different from a technical abstract: it is meant to be understandable by an informed layperson — including people outside of your field of expertise. This blog post provides guidelines and examples to help ICML authors prepare their lay summary.

Why making science more accessible matters

As machine learning continues to reshape many aspects of society, researchers have a responsibility to proactively make their knowledge more accessible. Lay summaries are an important step in that direction, and they are already required by several scientific journals.

Lack of public trust and understanding in science can foster misinformation, including about your own work if it is misinterpreted by the public or by the media.

As a researcher, you have the power to bridge this gap by granting access to trustworthy information about how machine learning works.

This encourages a healthy debate, allowing society to better use machine learning tools and make informed decisions about this transformative technology.

How to write a lay summary

Writing a lay summary may require you to get out of your comfort zone to make your research more accessible. The good news is that it is surprisingly easy to learn.

We suggest using this 3-step formula as a guide: (1) Problem, (2) Solution, (3) Impact. Keep it to a maximum of 10 sentences / 200 words for ICML.

  1. What problem prompted you to start your research?
  2. How did you tackle the problem?
  3. Why does your research matter?

Don’t be so general that your lay summary could be any other paper at ICML.

Fictional example in 50 words:

(1) Methane emissions are one of the biggest contributors to climate change, but tracking them is challenging. (2) We built AI-based satellite imaging systems to monitor methane emissions in remote places. (3) This will help to quickly identify new methane sources and provide insightful data to mitigate the effects of climate change.

See two examples from ICML 2024 below.

5 tips to become a better communicator

  • Know your audience. Don’t expect everyone to know as much about your research as you do. You have spent years developing expertise in a very niche field, but most people have not: try to put yourself in their shoes. Write your lay summary so that a science journalist could understand it.
  • Avoid jargon. Jargon sums up complicated ideas in a few words to speed up communication among experts in the same field. But this can lead to miscommunication if your audience does not know what these words mean. Be creative to avoid jargon words: use metaphors whenever you can; if you can’t, explain the word before using it.
  • Keep it concise. Keep only the most insightful elements, and don’t try to say everything: you cannot sum up years of hard work in a few sentences. Instead, focus on the core aspects of your research that highlight its purpose and impact. The idea of the lay summary is to get other people interested in your work and to increase its reach.
  • Tell a story. Think of your lay summary like a movie trailer for your paper. You don’t want to say everything (once again, you can’t), but you have to provide enough material so that people feel they need to know more. Make your writing engaging. Help your audience understand why your work matters.
  • Read it out loud. This is a great way to cut superfluous words. If it is awkward to pronounce, or you feel that you need to stop to catch your breath, the sentence is too long. Remove words that are unnecessary to understanding what you mean, starting with adjectives. You will be surprised how little words it takes to effectively communicate your ideas.

Examples from ICML 2024:

Example 1:

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition (Test of Time Award)
by Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell

Original abstract:

We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be re-purposed to novel generic tasks. Our generic tasks may differ significantly from the originally trained tasks and there may be insufficient labeled or unlabeled data to conventionally train or adapt a deep architecture to the new tasks. We investigate and visualize the semantic clustering of deep convolutional features with respect to a variety of such tasks, including scene recognition, domain adaptation, and fine-grained recognition challenges. We compare the efficacy of relying on various network levels to define a fixed feature, and report novel results that significantly outperform the state-of-the-art on several important vision challenges. We are releasing DeCAF, an open-source implementation of these deep convolutional activation features, along with all associated network parameters to enable vision researchers to be able to conduct experimentation with deep representations across a range of visual concept learning paradigms.

Lay summary in ~150 words:

We teach computers to analyze images by training them on vast collections of labeled object pictures. We wondered if this learned ability could then be applied to different kinds of image tasks — like being able to differentiate a room from a forest — even with very few new examples.

We took the learned components from a powerful image-analyzing program and used it for tasks like understanding scenes or telling apart different types of birds. We looked at how the program’s internal understanding of images — technically called pre-trained visual features — grouped together for these new tasks. Surprisingly, we found that these representations worked really well, even better than existing methods in some cases.

To help other researchers explore this idea, we have released a free and easy-to-use tool called DeCAF, along with the program’s settings. This lets anyone take these pre-trained visual features and try them out on their own image-related problems.

Example 2:

Information Complexity of Stochastic Convex Optimization: Applications to Generalization, Memorization, and Tracing (winner of one of the 10 best paper awards)

by Idan Attias, Gintare Karolina Dziugaite, Mahdi Haghifam, Roi Livni, Daniel M. Roy

Original abstract:

In this work, we investigate the interplay between memorization and learning in the context of stochastic convex optimization (SCO). We define memorization via the information a learning algorithm reveals about its training data points. We then quantify this information using the framework of conditional mutual information (CMI) proposed by Steinke and Zakynthinou (2020). Our main result is a precise characterization of the tradeoff between the accuracy of a learning algorithm and its CMI, answering an open question posed by Livni (2023). We show that, in the L2 Lipschitz–bounded setting and under strong convexity, every learner with an excess error ϵ has CMI bounded below by Ω(1/ϵ2) and Ω(1/ϵ), respectively. We further demonstrate the essential role of memorization in learning problems in SCO by designing an adversary capable of accurately identifying a significant fraction of the training samples in specific SCO problems. Finally, we enumerate several implications of our results, such as a limitation of generalization bounds based on CMI and the incompressibility of samples in SCO problems.

Lay summary in ~170 words:

How much information do good learning algorithms have to reveal about the data they have been trained on? We wanted to answer this question by using a mathematical tool called conditional mutual information (CMI) to measure the “memorization” of the training data.

Our paper presents the surprising result that for some learning problems, some memorization is necessary to get good performance. This is surprising, as machine learning scientists traditionally thought that learning methods should avoid memorizing the training data to genuinely “understand” how to solve the task — akin to a student who memorizes homework solutions instead of grasping the underlying concepts. Our second result is that, contrary to what was previously believed, CMI cannot always be used to explain why a learning algorithm succeeds in certain scenarios.

Our findings have implications for how we assess the ability of learning algorithms to make predictions on previously unseen data and suggest that there are limits to how much we can compress the data used for training.

Additional resources

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