5 citations · 8 across the 7 of their papers we have counts for
7 papers
Interpretability-by-Design with Accurate Locally Additive Models and Conditional Feature Effects
Vasilis Gkolemis, Loukas Kavouras, Dimitrios Kyriakopoulos +5
Generalized additive models (GAMs) offer interpretability through independent univariate feature effects but underfit when interactions are present in data. GAMs add selected p…
Fast and Robust Simulation-Based Inference With Optimization Monte Carlo
Vasilis Gkolemis, Christos Diou, Michael U. Gutmann
Bayesian parameter inference for complex stochastic simulators is challenging due to intractable likelihood functions. Existing simulation-based inference methods often require lar…
Effector: A Python package for regional explanations
Vasilis Gkolemis, Christos Diou, Dimitris Kyriakopoulos +10
Effector is a Python package for interpreting machine learning (ML) models that are trained on tabular data through global and regional feature effects. Global effects, like Partia…
Regionally Additive Models: Explainable-by-design models minimizing feature interactions
Vasilis Gkolemis, Anargiros Tzerefos, Theodore Dalamagas +2
Generalized Additive Models (GAMs) are widely used explainable-by-design models in various applications. GAMs assume that the output can be represented as a sum of univariate funct…
RHALE: Robust and Heterogeneity-aware Accumulated Local Effects
Vasilis Gkolemis, Theodore Dalamagas, Eirini Ntoutsi +1
Accumulated Local Effects (ALE) is a widely-used explainability method for isolating the average effect of a feature on the output, because it handles cases with correlated feature…
An Extendable Python Implementation of Robust Optimisation Monte Carlo
Vasilis Gkolemis, Michael Gutmann, Henri Pesonen
Performing inference in statistical models with an intractable likelihood is challenging, therefore, most likelihood-free inference (LFI) methods encounter accuracy and efficiency…
DALE: Differential Accumulated Local Effects for efficient and accurate global explanations
Vasilis Gkolemis, Theodore Dalamagas, Christos Diou
Accumulated Local Effect (ALE) is a method for accurately estimating feature effects, overcoming fundamental failure modes of previously-existed methods, such as Partial Dependence…