Feel free to substitute 'journals' for any other useful portal of knowledge.

I am interested in keeping an eye on new developments in machine learning, with a view to practical applications. I am not an academic seeking to publish my own work (at least not in this field), but I do want to be aware of potential new algorithms or tricks that would be useful on a practical level.

The only caveat is that the journal/conference proceeding or whatever must be freely available without requiring a subscription.

  • $\begingroup$ The arxiv entry for recent machine learning submissions is also a good option; at least for checking some abstracts during your morning coffee. $\endgroup$
    – user10525
    May 30, 2012 at 8:17
  • $\begingroup$ @Procrastinator, I did check arXiv before posting the question, but it didn't seem right that there are 'only' a handful of pre-prints a day. I'm used to seeing 100+ papers every day in the arXiv categories in my field. I though that maybe the ML community wasn't really into arXiv. Can you confirm that the majority of ML papers get posted to arXiv? If so that would be wonderfully convenient, as I already peruse other parts of arXiv daily anyway. $\endgroup$ May 31, 2012 at 4:51
  • $\begingroup$ I am sure only few ML papers are posted on arXiv, some of them are either posted on universities websites, personal websites or even never posted as preprints. Also, there are many useless papers which makes difficult to get the useful ones. On the other hand, when you are lucky enough and find a good one, you can read it before it is published. Publishing may take even two years. So, my opinion about arXiv is that it is worth to take a quick look at the abstracts and see if you find something useful, but I agree it is not the best option (this is why I posted this as a comment). $\endgroup$
    – user10525
    May 31, 2012 at 8:18

3 Answers 3


New developments in ML are almost always presented in conferences first, and sometimes later refined into journal papers.

If you only follow two conferences, they should be:

  • NIPS (Neural Information Processing Systems); December. Conference site, proceedings. (Despite the name, most papers are unrelated to neuroscience or neural networks.)
  • ICML (International Conference on Machine Learning); July. Site (including proceedings links).

These conferences also include workshops that publish less-polished work, which can often be a good way to find about ongoing and not-yet-published research.

The following ML conferences also contain many excellent papers, though they are not as "first-tier" as NIPS and ICML and may be more focused in scope:

  • AISTATS (Artificial Intelligence and Statistics); May. Conference site; proceedings published in JMLR and available here. Sometimes more theoretical, especially from a statistics viewpoint.
  • COLT (Conference on Learning Theory); July. 2015 site, proceedings also published in JMLR. Very theoretical.
  • UAI (Uncertainty in Artificial Intelligence); July. Conference site, proceedings. Typically more focused on graphical models and/or Bayesian techniques.
  • ICLR (International Conference on Learning Representations); May. Conference site. (Focused on deep learning, relatively new; all submissions appear on arXiv.)
  • ECML PKDD (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases); September. Conference site.
  • ACML (Asian Conference on Machine Learning); November. Conference site.

Some AI conferences also include good machine learning papers or specific tracks on machine learning, especially:

Conferences in related fields are also often relevant, especially:

  • KDD (Knowledge Discovery and Data Mining); August. Conference site, links to individual conferences here.
  • CVPR (Computer Vision and Pattern Recognition); June. 2016 site, overview.

The Journal of Machine Learning is freely-available online and on the cutting edge, but it is pretty heavy.


I think that the best way to keep track of the latest developments in Machine Learning is to follow the Reddit feed:


Many researchers post some comments about the papers they recently submitted to different venues.

You might also follow what is submitted to Arxiv here:


Most researchers submit pre-print versions of their papers to Arxiv before publication.

Also, you might want to have a Twitter account and follow particular researchers/professors who work in machine learning. However, the people you might want to follow really depend on your area of interest. A good starting point might be following the hashtag #machinelearning

Also remember that the terms machine learning, data mining, knowledge discovery in data bases, data science are sometimes used interchangeably. In order to find some interesting developments in machine learning you might look at news in those other areas as well.


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