---
title: "Last Month’s Machine Learning Lessons Learned"
url: https://daily.dev/posts/last-month-s-machine-learning-lessons-learned-slznkgusd
source_url: https://towardsdatascience.com/last-months-lessons-learned
type: article
source: "Towards Data Science"
published: 2026-08-06T15:26:02.228Z
updated: 2026-08-06T15:26:24.256Z
tags: ["machine-learning"]
reading_time: 6
upvotes: 1
comments: 0
language: en
---

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# Last Month’s Machine Learning Lessons Learned

**[Towards Data Science](https://daily.dev/sources/tds)** · 6 min read · 1 upvotes · 0 comments

## Summary

A reflection on the hidden costs of attending major ML conferences like ICML, NeurIPS, and ICLR. Beyond the conference days themselves, researchers face significant overhead: booking logistics, long-haul travel, jet lag, and a backlog of accumulated work upon return. A five-day conference can realistically consume two full weeks when all factors are counted, disrupting deep research work that requires extended, uninterrupted focus.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://towardsdatascience.com/last-months-lessons-learned>

## Questions this post answers

### What are the acceptance rates for top ML conferences like ICML, NeurIPS, and ICLR?

Acceptance rates at the three premier ML conferences — ICML, NeurIPS, and ICLR — typically fall between 20% and 30% of submitted papers. The process involves anonymous peer review, author rebuttals, and a waiting period of one to two months before decisions are made, making publication at these venues genuinely competitive.

_ML researchers tracking submission deadlines and acceptance trends find the latest conference news on daily.dev._

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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning)

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