Anyone submit ML articles to ACM journals (eg. TOPML or TIST)? [D]

If you're feeling constrained by traditional ML publication venues, it's time to explore how journals like ACM's TOPML or TIST might offer a fresh path for your research. Recent discussions on platforms like Reddit reveal a growing interest in understanding the nuances of journal submissions, particularly when shifting from conferences to more sustained, scholarly formats. This conversation resonates with broader trends in academia, where researchers increasingly seek environments that prioritize depth over speed—a sentiment echoed in Thoughts and experience on ML journals, which highlights the trade-offs between conference and journal cultures. For many, the decision to pivot hinges on factors like review quality, editorial rigor, and the desire for broader, long-term impact.

The Reddit thread underscores a critical question: How do ACM journals stack up against other venues like TMLR? While conferences often prioritize novelty and rapid dissemination, journals like TOPML emphasize methodological rigor and reproducibility, as noted in Thoughts and experience on ML journals. This aligns with the progressive ethos of reimagining data workflows—just as AI-native tools evolve to simplify complexity, journal selection reflects a strategic choice to align with values of accessibility and depth. The comparison to TMLR, which balances theoretical and applied ML research, further illustrates the spectrum of options available to researchers navigating this landscape.

For those considering submissions, the process itself becomes a lens into the evolving expectations of ML scholarship. The original query about timelines and review quality reveals a pragmatic concern: the feasibility of journal publishing in a fast-paced field. Reviews in journals like TOPML, while thorough, may demand more iterative revisions than conference proceedings, a detail that Thoughts and experience on ML journals frames as both a challenge and an opportunity. This mirrors the balance between technical precision and human-centered design—journals, like AI tools, thrive when they empower researchers without overwhelming them.

As the ML ecosystem matures, the shift from conferences to journals may signal a broader reckoning with how research is validated and shared. Will venues like ACM’s TOPML and TIST lead this transformation, or will they face resistance from entrenched conference cultures? The answer lies in how these platforms adapt to the needs of a community increasingly prioritizing substance over spectacle. For now, the conversation remains vital: What does it mean to publish in a way that honors both innovation and integrity? — Thoughts and experience on ML journals offers a starting point, but the dialogue must continue.