76 citations · 168 across the 6 of their papers we have counts for
4 papers · 1 filter
Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose?
Balázs Kégl, Gabriel Hurtado, Albert Thomas
We contribute to micro-data model-based reinforcement learning (MBRL) by rigorously comparing popular generative models using a fixed (random shooting) control agent. We find that…
Spurious samples in deep generative models: bug or feature?
Balázs Kégl, Mehdi Cherti, Akın Kazakçı
Traditional wisdom in generative modeling literature is that spurious samples that a model can generate are errors and they should be avoided. Recent research, however, has shown i…
Similarity encoding for learning with dirty categorical variables
Patricio Cerda, Gaël Varoquaux, Balázs Kégl
For statistical learning, categorical variables in a table are usually considered as discrete entities and encoded separately to feature vectors, e.g., with one-hot encoding. "Dirt…
Fast classification using sparse decision DAGs
Djalel Benbouzid, Robert Busa-Fekete, Balazs Kegl
In this paper we propose an algorithm that builds sparse decision DAGs (directed acyclic graphs) from a list of base classifiers provided by an external learning method such as Ada…