AIStats
AIStats at Beyond Market Intelligence is a file of 3 stories. The newest of them: “Navigate AIStats 2027 with clarity on templates and paper fit.”, “Discovering space for statistical ML beyond the LLM tide”, and “Why causal inference was missing from NeurIPS workshops this year”. Submitting a paper three times across three different venues is a test of endurance, and the pattern here is hard to ignore. 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 AIStats story on Beyond Market Intelligence, newest first.
Navigate AIStats 2027 with clarity on templates and paper fit.
Submitting a paper three times across three different venues is a test of endurance, and the pattern here is hard to ignore. The UAI rejection came down to verification, not necessarily validity, and the ICDM rejection came with a blank meta-review and silence. That is not feedback; that is a dead end. The finance win and the journal offer signal the work has real substance, but the core problem is a mismatch.
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.