58 citations · 288 across the 24 of their papers we have counts for
25 papers
Computing Marginal and Conditional Divergences between Decomposable Models with Applications
Loong Kuan Lee, Geoffrey I. Webb, Daniel F. Schmidt +1
The ability to compute the exact divergence between two high-dimensional distributions is useful in many applications but doing so naively is intractable. Computing the alpha-beta…
Optimum-Preserving QUBO Parameter Compression
Sascha Mücke, Thore Gerlach, Nico Piatkowski
Quadratic unconstrained binary optimization (QUBO) problems are well-studied, not least because they can be approached using contemporary quantum annealing or classical hardware ac…
Shapley Values with Uncertain Value Functions
Raoul Heese, Sascha Mücke, Matthias Jakobs +2
We propose a novel definition of Shapley values with uncertain value functions based on first principles using probability theory. Such uncertain value functions can arise in the c…
Explaining Quantum Circuits with Shapley Values: Towards Explainable Quantum Machine Learning
Raoul Heese, Thore Gerlach, Sascha Mücke +3
Methods of artificial intelligence (AI) and especially machine learning (ML) have been growing ever more complex, and at the same time have more and more impact on people's lives.…
Full Kullback-Leibler-Divergence Loss for Hyperparameter-free Label Distribution Learning
Maurice Günder, Nico Piatkowski, Christian Bauckhage
The concept of Label Distribution Learning (LDL) is a technique to stabilize classification and regression problems with ambiguous and/or imbalanced labels. A prototypical use-case…
On Quantum Circuits for Discrete Graphical Models
Nico Piatkowski, Christa Zoufal
Graphical models are useful tools for describing structured high-dimensional probability distributions. Development of efficient algorithms for generating unbiased and independent…