What is the criteria for a ML paper to be published?[D]

Reddit r/MachineLearning / 4/16/2026

💬 OpinionIdeas & Deep AnalysisModels & Research

Key Points

  • The post asks what criteria make an ML paper worth publishing, especially for work with robust methodology but limited predictive performance.
  • The author describes a stock-index forecasting study using macroeconomic variables, applying techniques to address non-stationarity and using SHAP to interpret a random-forest model.
  • They report that SHAP explanations reveal limitations under regime shifts (e.g., inverted relationships across periods), raising concerns about whether the results have sufficient “predictive power” to merit presentation.
  • The author proposes reframing the contribution as a diagnostic or interpretability-focused discussion open to extensions, and asks if this framing could still be acceptable for a local conference.

I'm going to attend a conference soon with my academic supervisor. I want to know what I should be expecting as I'm new to this field.
To be more specific, I'm forecasting a stock index using macroeconomic variables, where the results are robust (addressed non-stationarity and such), but have small predictive power. I've applied SHAP to a random forest model where I noticed that it struggles with regime shifts (like oil becoming a liability instead of an asset depending the period) which is explainable because it didn't learn the inverted relationship.

So I'm not sure if my results even have any worth at all to present? In my opinion, I think they're useful in terms of research discussion and further extensions, but don't indicate strong predictive power (which I think is alright when it comes to stock returns forecasting).
If I frame this well enough, like not claiming a very accurate predictor but rather an interesting diagnostic that's open for interpretability and further work, will I have a chance at a local conference?

submitted by /u/IntroductionCommon11
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