Maths vs machine learning publishing venues [D]

A research mathematician recently authored a 60-page paper in theoretical computer science that they believe is more relevant to machine learning researchers than fellow mathematicians. Seeking to place it in ML venues, they prefer journals over conferences due to the paper's length and a desire to avoid conference culture. They ask for ML journal recommendations, specifically seeking equivalents to prestigious math journals like Transactions of the American Mathematical Society (AMS). This highlights the cultural and logistical gaps between math and ML publishing, including differences in review processes, acceptance rates, and audience expectations.

This query underscores a broader trend of interdisciplinary research challenging traditional publishing boundaries. The mathematician's search for ML equivalents of top math journals reflects a need for clarity in cross-field recognition, impact metrics, and review standards. As ML increasingly draws on theoretical foundations, such navigation becomes critical for researchers aiming to maximize reach and credibility across domains.