What a pleasure #UAI2025 has been! Great researchers, great talks. I'm looking forward to coming another time!

Thanks a lot for the organisation :-)

In addition to that historical journey, we provide examples of such milestones and snippets of code to reproduce them on the fly

However, learning such a model ks tricky, because common statistical tools do not apply when we assume nothing about the data distribution

When doing unsupervised learning, we have two different ways to build our model. One is discriminative: we assume nothing of the data distribution, and try to infer clusters straight out of it. Implicit hyptheses are built within the model

Since DISCOTEC relies on ensemble, its performance is tied to the number of models used for computing the consensus. This is even stronger for the binarised variant. (4/5)

An interesting advantage is that binarising the consensus matrix drastically improves the ranking of the clustering algorithms. (3/5)

We introduce the DISCOTEC score.

It simply consists in two steps: (i) compute the consensus matrix for a set of clustering algorithms (ii) compute the average distance between connectivities and consensus matrices

Bonus: must link and cannot link constraints are gracefully supported (2/5)

Had a blast today discussing and tracing the evolution of discriminative clustering methods at the Pioneer Centre for AI @aicentre.dk in Copenhagen. Lots of interesting talks and perspectives; thanks for the invitation!

📸: @jesfrellsen.bsky.social