Reconsidering Conferences: A Practical Look at ML Journal

Reconsidering Conferences: A Practical Look at ML Journal Paths

In recent reflections, I've been contemplating a shift from ML conferences to journals, primarily due to some frustrating experiences with the conference review process.

April 15, 20263 min readMachine Learning

The shift away from ML conferences toward journals is not a retreat from rigor, it is a recognition that the review process, for many researchers, has become the bottleneck rather than the gatekeeper. The original poster's frustration is familiar: long waits, inconsistent feedback, and a growing sense that the conference system rewards presentation over substance. But the solution is not to abandon peer review altogether. It is to find the right venue for the work you actually do, and that requires understanding what journals like TMLR, Neurocomputing, and Neural Networks really offer beyond a Q1 label.

The Q1 designation is a metric, not a verdict. It tells you a journal is indexed and cited, but it says nothing about whether your specific paper will receive a fair, timely, or constructive review. The poster's instinct to avoid JMLR due to extended waiting times is practical, not lazy. Short papers, which are often the most focused and useful, can disappear in a system built for longer, more comprehensive submissions. TMLR, with its commitment to rapid iteration and community-driven review, is a strong fit for that reality. But it is not the only option. Journals like Neurocomputing and Neural Networks are Q1, but they operate with different editorial priorities, review cultures, and acceptance patterns. The question is not which is "better" in the abstract, but which aligns with your timeline, your paper's length, and your need for actionable feedback.

What is striking is the honesty about not reading journal papers regularly. That is not a personal failing; it is a structural habit. The conference culture dominates ML, and journals have been treated as an afterthought. But that is changing. As more researchers voice similar frustrations, the demand for alternatives grows. The practical takeaway is to treat journal selection like a research problem: define your constraints, read recent papers from each candidate journal, and look at their review timelines and acceptance rates. Do not rely on a quartile ranking to tell you whether a venue will respect your work.

The path forward is not to abandon the ecosystem, but to become a more deliberate participant in it. If conferences have failed you, that is a reason to seek better alternatives, not to opt out of formal review altogether. The poster's openness to TMLR and other journals is the right instinct. The next step is to test the waters with a submission that fits the venue's scope and style. A bad experience with one system should not condemn an entire method of scholarly communication. It should push you to find the one that works for your work. That is not a compromise. It is a strategy.

From Machine Learning

Recently I’ve been thinking about shifting from conferences to journals due to a few bad experiences with ML conferences reviewing process. The truth is I don’t really have much experience with journals, and I rarely read papers from them.
I don’t really want to submit to something like JMLR because of the extremely long waiting times, and also because my papers tend to be shorter.

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