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Found 8 papers, 2 papers with code
Towards Explainable Real Estate Valuation via Evolutionary Algorithms
Human lives are increasingly influenced by algorithms, which therefore need to meet higher standards not only in accuracy but also with respect to explainability.
Adaptive Sampling for Fast Constrained Maximization of Submodular Function
Our algorithm is suitable to maximize a non-monotone submodular function under a $p$-system side constraint, and it achieves a $(p + O(\sqrt{p}))$-approximation for this problem, after only poly-logarithmic adaptive rounds and polynomial queries to the valuation oracle function.
Maps for Learning Indexable Classes
We study learning of indexed families from positive data where a learner can freely choose a hypothesis space (with uniformly decidable membership) comprising at least the languages to be learned.
Normal Forms for (Semantically) Witness-Based Learners in Inductive Inference
Such results are key to understanding the, yet undiscovered, mutual relation between various important learning paradigms when learning behaviourally correctly.
Mapping Monotonic Restrictions in Inductive Inference
In particular, we show that explanatory monotone learners, although known to be strictly stronger, do (almost) preserve the pairwise relation as seen in strongly monotone learning.
Learning Languages with Decidable Hypotheses
This so-called $W$-index allows for naming arbitrary computably enumerable languages, with the drawback that even the membership problem is undecidable.
timeXplain -- A Framework for Explaining the Predictions of Time Series Classifiers
Modern time series classifiers display impressive predictive capabilities, yet their decision-making processes mostly remain black boxes to the user.
Non-Monotone Submodular Maximization with Multiple Knapsacks in Static and Dynamic Settings
We show that our proposed algorithm competes with the state-of-the-art in static settings.
