8 citations · 10 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
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.…