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 sorts of papers best serve their readers? Ideally, papers should accomplish the following: provide intuition to aid the reader's understanding but clearly distinguish it from stronger conclusions supported by evidence; describe empirical investigations that consider and rule out alternative hypotheses; make clear the relationship between theoretical analysis and intuitive or empirical claims; and 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.
Recent progress in machine learning comes despite frequent departures from these ideals. This installment of Research for Practice focuses on the following four patterns that appear to be trending in ML scholarship:
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