Troubling Trends in Machine Learning Scholarship

By Zachary C. Lipton* & Jacob Steinhardt*
*equal authorship

Originally presented at ICML 2018: Machine Learning Debates
[arXiv link]

1   Introduction

Collectively, machine learning (ML) researchers are engaged in the creation and dissemination of knowledge about data-driven algorithms. In a given paper, researchers might aspire to any subset of the following goals, among others: to theoretically characterize what is learnable, to obtain understanding through empirically rigorous experiments, or to build a working system that has high predictive accuracy. While determining which knowledge warrants inquiry may be subjective, once the topic is fixed, papers are most valuable to the community when they act in service of the reader, creating foundational knowledge and communicating as clearly as possible.

What sort of papers best serve their readers? We can enumerate desirable characteristics: these papers should (i) provide intuition to aid the reader’s understanding, but clearly distinguish it from stronger conclusions supported by evidence; (ii) describe empirical investigations that consider and rule out alternative hypotheses [62]; (iii) make clear the relationship between theoretical analysis and intuitive or empirical claims [64]; and (iv) use language to empower the reader, choosing terminology to avoid misleading or unproven connotations, collisions with other definitions, or conflation with other related but distinct concepts [56].

Recent progress in machine learning comes despite frequent departures from these ideals. In this paper, we focus on the following four patterns that appear to us to be trending in ML scholarship:

  1. Failure to distinguish between explanation and speculation.
  2. Failure to identify the sources of empirical gains, e.g. emphasizing unnecessary modifications to neural architectures when gains actually stem from hyper-parameter tuning.
  3. Mathiness: the use of mathematics that obfuscates or impresses rather than clarifies, e.g. by confusing technical and non-technical concepts.
  4. Misuse of language, e.g. by choosing terms of art with colloquial connotations or by overloading established technical terms.

Heuristics for Scientific Writing (a Machine Learning Perspective)

It’s January 28th and I should be working on my paper submissions. So should you! But why write when we can meta-write? ICML deadlines loom only twelve days away. And KDD follows shortly after. The schedule hardly lets up there, with ACL, COLT, ECML, UAI, and NIPS all approaching before the summer break. Thousands of papers will be submitted to each.

The tremendous surge of interest in machine learning along with ML’s democratization due to open source software, YouTube coursework, and the availability of preprint articles are all exciting happenings. But every rose has a thorn. Of the thousands of papers that hit the arXiv in the coming month, many will be unreadable. Poor writing will damn some to rejection while others will fail to reach their potential impact. Even among accepted and influential papers, careless writing will sow confusion and damn some papers to later criticism for sloppy scholarship (you better hope Ali Rahimi and Ben Recht don’t win another test of time award!).

But wait, there’s hope! Your technical writing doesn’t have to stink. Over the course of my academic career, I’ve formed strong opinions about how to write a paper (as with all opinions, you may disagree). While one-liners can be trite, I learned early in my PhD from Charles Elkan that many important heuristics for scientific  paper writing can be summed up in snappy maxims.  These days, as I work with younger students, teaching them how to write clear scientific prose, I find myself repeating these one-liners, and occasionally inventing new ones.

The following list consists of easy-to-memorize dictates, each with a short explanation. Some address language, some address positioning, and others address aesthetics. Most are just heuristics so take each with a grain of salt, especially when they come into conflict. But if you’re going to violate one of them, have a good reason. This can be a living document, if you have some gems, please leave a comment.

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