{
  "video_id": "reddit_1t8bnmp",
  "channel_slug": "MachineLearning",
  "channel_handle": "r/MachineLearning",
  "title": "What is an average publication outcome for an ML PhD? [D]",
  "url": "https://www.reddit.com/r/MachineLearning/comments/1t8bnmp/what_is_an_average_publication_outcome_for_an_ml/",
  "external_url": null,
  "upload_date": "20260509",
  "published_at": "2026-05-09T17:44:32+00:00",
  "transcript": "I know publication count is not everything, and quality, contribution, advisor/lab culture, subfield, and luck all matter a lot. But to make the comparison easier, I’m curious about the publication-count side specifically.  \nFor an ML PhD, what would you consider an average publication outcome by graduation?\n\nFor example, would something like *3–5 first-author papers at A/top-tier venues*\\* be considered roughly average, or would that already be above average in ML?\n\nBy A\\*/top-tier, I’m thinking of venues such as NeurIPS, ICML, ICLR, CVPR, ACL, EMNLP, etc., depending on the subfield.\n\n**Important**:  \nAgain, I know paper count is a crude metric. I’m just trying to get a rough sense of what people in the field see as average, strong, or unusually strong.\n\n\n\n--- Top Comments ---\n\n\n[190 upvotes] 5 papers in A\\* venue is crazy bar\n\n[85 upvotes] 3 first author papers at any venue I think is reasonable, hopefully at least one top tier venue. If your work is good, I'll defend you with any committee irrespective of where it was published.\n\nGetting accepted is a lottery draw so tying to specific outcomes can be bad IMO when some of these circumstances are uncontrollable.\n\n[49 upvotes] Depends on how long your PhD is / which country. 3 A* is amazing tier in France/3 years.\n\n[32 upvotes] Depends a lot on the lab but probably around 1-3 first-author papers at the top venues ",
  "transcript_chars": 1386,
  "ingested_at": "2026-05-12T11:04:38.207252+00:00",
  "source": "reddit",
  "yt_meta": {
    "score": 70,
    "upvote_ratio": 0.88,
    "num_comments": 90,
    "author": "Hope999991",
    "is_self": true
  }
}