160 citations · 173 across the 5 of their papers we have counts for
5 papers
River: machine learning for streaming data in Python
Jacob Montiel, Max Halford, Saulo Martiello Mastelini +8
River is a machine learning library for dynamic data streams and continual learning. It provides multiple state-of-the-art learning methods, data generators/transformers, performan…
Selectivity correction with online machine learning
Max Halford, Philippe Saint-Pierre, Franck Morvan
Computer systems are full of heuristic rules which drive the decisions they make. These rules of thumb are designed to work well on average, but ignore specific information about t…
Selectivity Estimation with Attribute Value Dependencies using Linked Bayesian Networks
Max Halford, Philippe Saint-Pierre, Franck Morvan
Relational query optimisers rely on cost models to choose between different query execution plans. Selectivity estimates are known to be a crucial input to the cost model. In pract…
An Approach Based on Bayesian Networks for Query Selectivity Estimation
Max Halford, Philippe Saint-Pierre, Frank Morvan
The efficiency of a query execution plan depends on the accuracy of the selectivity estimates given to the query optimiser by the cost model. The cost model makes simplifying assum…
Explaining Machine Learning Models using Entropic Variable Projection
François Bachoc, Fabrice Gamboa, Max Halford +2
In this paper, we present a new explainability formalism designed to shed light on how each input variable of a test set impacts the predictions of machine learning models. Hence,…