33 citations · 46 across the 7 of their papers we have counts for
9 papers
Information based explanation methods for deep learning agents -- with applications on large open-source chess models
Patrik Hammersborg, Inga Strümke
With large chess-playing neural network models like AlphaZero contesting the state of the art within the world of computerised chess, two challenges present themselves: The questio…
Against Algorithmic Exploitation of Human Vulnerabilities
Inga Strümke, Marija Slavkovik, Clemens Stachl
Decisions such as which movie to watch next, which song to listen to, or which product to buy online, are increasingly influenced by recommender systems and user models that incorp…
Real-Time Counterfactual Explanations For Robotic Systems With Multiple Continuous Outputs
Vilde B. Gjærum, Inga Strümke, Anastasios M. Lekkas +1
Although many machine learning methods, especially from the field of deep learning, have been instrumental in addressing challenges within robotic applications, we cannot take full…
Reinforcement Learning in an Adaptable Chess Environment for Detecting Human-understandable Concepts
Patrik Hammersborg, Inga Strümke
Self-trained autonomous agents developed using machine learning are showing great promise in a variety of control settings, perhaps most remarkably in applications involving autono…
Interpretable machine learning in Physics
Christophe Grojean, Ayan Paul, Zhuoni Qian +1
Adding interpretability to multivariate methods creates a powerful synergy for exploring complex physical systems with higher order correlations while bringing about a degree of cl…
Socioeconomic disparities and COVID-19: the causal connections
Tannista Banerjee, Ayan Paul, Vishak Srikanth +1
The analysis of causation is a challenging task that can be approached in various ways. With the increasing use of machine learning based models in computational socioeconomics, ex…
Shapley values for feature selection: The good, the bad, and the axioms
Daniel Fryer, Inga Strümke, Hien Nguyen
The Shapley value has become popular in the Explainable AI (XAI) literature, thanks, to a large extent, to a solid theoretical foundation, including four "favourable and fair" axio…
Explaining the data or explaining a model? Shapley values that uncover non-linear dependencies
Daniel Vidali Fryer, Inga Strümke, Hien Nguyen
Shapley values have become increasingly popular in the machine learning literature thanks to their attractive axiomatisation, flexibility, and uniqueness in satisfying certain noti…
Signal mixture estimation for degenerate heavy Higgses using a deep neural network
Anders Kvellestad, Steffen Maeland, Inga Strümke
If a new signal is established in future LHC data, a next question will be to determine the signal composition, in particular whether the signal is due to multiple near-degenerate…