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ICML 2026: Paper reviews
27 August 2026
- News
ICML 2026 brought together the global machine learning community to share the latest advances in the field. Our researchers were there to explore new ideas, engage with leading academics and stay at the forefront of machine learning research.
They’ve compiled reviews of the papers they found most insightful. Explore them now.
What I like about ICML, or conferences along these lines, is you get a sense of what is up and coming, what people's minds are turning to, what people are working on. And that's fascinating.
My name is Fabian. I am a senior researcher at g Research, where I work on predicting future returns of stocks and other assets. Energy this week in ICML has been phenomenal. So many people, so many researchers presenting their research. The value in coming to ICML to actually talk to the people behind the research, so to be able to ask questions, to get the intuition behind papers and not just what is in the paper. There was a tutorial on Monday about connecting optimization theory to how it's used in practice. You can go beyond the formality and rigor of the proofs to actually get heuristics, see how some of this stuff is actually relevant to how you think about how you optimize in deep learning. You get a sense of what is up and coming. So what people think, what people's minds are turning to, what people are working on, and that's fascinating. Particularly interesting topic was agentic workflows, and it is striking to what extent people use agents in the most unlikely, most varied scenarios. The biggest unanswered question remains just how much potential there is for research questions. They are immensely useful, but how far can agents go when it comes to ideation now or in the future? I think that, for me, remains to be seen.
The value in coming to ICML is to actually talk to the people behind the research, to ask questions, to get the intuition behind papers, and not just what is in the paper. One paper is one result. But how they think about the field of ML, that's what's really quite helpful to understand.
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