Handbook for PhD Students

This PhD Handbook serves a dual purpose: it defines the research methodology of our group and gives general advice to students, and it sets out standards and processes which all students in the group are expected to strive for.

Research integrity and reproducibility direct link

Integrity: Research integrity means several things. Always make it clear which parts are from you and which parts were done by other researchers by citing their work. When comparing your algorithm with others, aim for a fair comparison (e.g. put equal effort into finding good hyper-parameters for each algorithm). Don't cherry-pick your results; if you persistently get some bad results, it may be that your work is not yet ready or you may have to narrow the scope to a particular subclass of problems and make your assumptions clear. See also the University's page on research integrity and this online course for PGR students.

Reproducibility: Scientists often point out limited reproducibility in their research areas (e.g. [1], [2], [3], [4], [5]). There is even a ML Reproducibility Challenge. We are aiming to produce top-quality research, and part of that is complete reproducibility of our work and results. This means that your papers must include all details required to reproduce your results. If you don't have the space, put the details into an appendix and link to it from the main paper. Once your paper is published, you should upload your documented code (algorithms, environments) to our code repo. Besides ensuring reproducibility, making your code available has additional benefits: (1) other researchers will more readily use your algorithms if the code is already there; (2) it protects you in that they will use a correct implementation (rather than a bad/buggy implementation of their own).

Read: On reproducible AI: Towards reproducible research, open science, and digital scholarship in AI publications