Where to submit stat/prob ML [D]

The recent Reddit post by /u/didimoney highlighting the dominance of Large Language Model (LLM) research at top machine learning conferences resonates deeply with many in the statistical and probabilistic machine learning (stat/prob ML) community. It’s a sentiment echoed in discussions around the evolving landscape of AI research, a shift that feels, for some, like a near-total eclipse. The observation that finding non-LLM related posters at ICLR required considerable luck, and that workshops are increasingly focused on agentic applications, is a stark illustration of this trend. This isn’t simply about a change in topic; it’s about a potential realignment of priorities within the field, raising questions about where researchers in traditional stat/prob ML can best share their work and maintain a vibrant community. As one reader noted in a related discussion about How important is having an internship to get a good job for ML PhD in USA?, navigating career paths in a rapidly shifting field can feel disorienting, and this conference dynamic adds another layer of complexity. The current situation also mirrors concerns about the reproducibility and robustness of LLM benchmarks, as highlighted in the analysis of hourly LLM scores: I analyzed 31,352 hourly LLM benchmark scores: within-day variation was 2.8 points, while between-day variation was 8.4, suggesting that focusing solely on these benchmarks may not provide a complete picture of progress.

The author’s suggestion of AISTATS and UAI as potential havens for stat/prob ML research is a reasonable one. These conferences have historically fostered a strong community focused on the theoretical foundations and rigorous methodologies underpinning probabilistic inference, Bayesian methods, and causal reasoning – areas often overshadowed by the more empirically driven LLM focus. However, the broader issue isn't simply about finding alternative venues. It’s about ensuring that the fundamental research driving advancements in statistical modeling, graphical models, and probabilistic programming doesn't become marginalized. There's a risk that the perceived prestige of the "top 3" conferences (NeurIPS, ICML, ICLR) could inadvertently discourage researchers from submitting work that doesn’t align with the current LLM hype cycle, even if that work represents significant theoretical or practical advancements. The implicit understanding that these venues are "prestigious" can create a self-fulfilling prophecy, further reinforcing the dominance of a particular research direction. Understanding the underlying concepts that drive these systems, as explored in WTF is a World Model?, is crucial, and requires a grounding in the very statistical principles that are currently facing reduced visibility.

This isn’t to say that LLMs are unimportant or that research into them shouldn’t be prioritized. The transformative potential of these models is undeniable. However, a balanced and healthy AI ecosystem requires a diversity of research perspectives. Stat/prob ML provides the theoretical rigor and foundational understanding necessary to address the limitations of current LLMs, such as their lack of true understanding, susceptibility to biases, and difficulty in reasoning about causality. Neglecting these areas risks building increasingly sophisticated systems on shaky ground. Furthermore, many real-world applications, particularly those requiring reliable decision-making in safety-critical domains, demand the robust probabilistic reasoning capabilities that stat/prob ML excels at—capabilities that are not inherent in current LLM architectures.

Looking ahead, the challenge lies in fostering a renewed appreciation for the value of rigorous statistical foundations within the broader AI community. This might involve actively promoting interdisciplinary collaborations, highlighting the successes of stat/prob ML in specific applications, and encouraging conference organizers to create dedicated tracks or workshops that showcase this vital research. The question isn't whether LLMs will continue to be a dominant force, but rather how we can ensure that the broader field of machine learning remains diverse, balanced, and grounded in the principles that will ultimately lead to truly intelligent and reliable AI systems. Will we see a resurgence of interest in fundamental statistical methods, or will the momentum behind LLMs continue to overshadow these critical areas of research?