5 papers
Efficient Contextual Preferential Bayesian Optimization with Historical Examples
Farha A. Khan, Tanmay Chakraborty, Jörg P. Dietrich +1
State-of-the-art multi-objective optimization often assumes a known utility function, learns it interactively, or computes the full Pareto front-each requiring costly expert input.…
Comparative Explanations: Explanation Guided Decision Making for Human-in-the-Loop Preference Selection
Tanmay Chakraborty, Christian Wirth, Christin Seifert
This paper introduces Multi-Output LOcal Narrative Explanation (MOLONE), a novel comparative explanation method designed to enhance preference selection in human-in-the-loop Prefer…
Explainable Bayesian Optimization
Tanmay Chakraborty, Christian Wirth, Christin Seifert
Manual parameter tuning of cyber-physical systems is a common practice, but it is labor-intensive. Bayesian Optimization (BO) offers an automated alternative, yet its black-box nat…
Explanation format does not matter; but explanations do -- An Eggsbert study on explaining Bayesian Optimisation tasks
Tanmay Chakraborty, Marion Koelle, Jörg Schlötterer +3
Bayesian Optimisation (BO) is a family of methods for finding optimal parameters when the underlying function to be optimised is unknown. BO is used, for example, for hyperparamete…
An Empirical Analysis of Fairness Notions under Differential Privacy
Anderson Santana de Oliveira, Caelin Kaplan, Khawla Mallat +1
Recent works have shown that selecting an optimal model architecture suited to the differential privacy setting is necessary to achieve the best possible utility for a given privac…