conferences
4 stories filed under conferences on Beyond Market Intelligence. The newest of them: “Navigating JMLR submissions with a cross-disciplinary advisor”, “Discovering space for statistical ML beyond the LLM tide”, and “Why causal inference was missing from NeurIPS workshops this year”. A PhD student's supervisor is pushing for a JMLR submission based on tenure incentives, not fit. Walking the aisles at ICLR this year, you could measure the shift in the ratio of LLM papers to everything else. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every conferences story on Beyond Market Intelligence, newest first.
Navigating JMLR submissions with a cross-disciplinary advisor
A PhD student's supervisor is pushing for a JMLR submission based on tenure incentives, not fit. That tension is real. The student's experience with conferences mirrors what many face: reviewers asking for impossible proofs, with ACs unable to judge math. JMLR may offer better quality control, but the risk of a three-year review ending in rejection is valid. For applied stats work, it is worth checking scope carefully. If you are navigating similar pressure, our piece on practical AI deployment might offer perspective.
Discovering space for statistical ML beyond the LLM tide
Walking the aisles at ICLR this year, you could measure the shift in the ratio of LLM papers to everything else. One in ten, if you were lucky. The workshops lean agentic, and NeurIPS tells the same story. For a researcher who built a career on statistical and probabilistic ML, that feels like watching your home town get gentrified. AISTATS and UAI are looking like the saner bet. The top 3 were never really built for this work; they just carried the prestige.
Why causal inference was missing from NeurIPS workshops this year
Seventy-three workshops at NeurIPS, and causality doesn't get a single slot. That stings, especially for those of us who've watched the field push real boundaries at UAI and CLeaR. It's not that those venues lack merit; they're excellent. But the energy at the top conferences has clearly pivoted toward LLMs and agents, leaving causal inference on the periphery. We're not saying the work vanished, just that its visibility is shrinking. For researchers who value rigorous, human-centered data science, this feels like a quiet loss.
Rediscover the depth and community of specialized conferences
The conference ecosystem has drifted far from its focused roots. BMVC, ACCV, FG, ICIP, and ICASSP once nurtured tight-knit communities where signal processing or face analysis felt like a home. Now, submissions flood a few flagship venues, leaving solid work as non-archival or arXiv-only casualties. That is not just nostalgia; it is a real loss of depth. We still need spaces where niche expertise thrives. For those wrestling with how to manage messy, real-world data, exploring clean data practices might offer a useful parallel.