Search Results for author:
Found 6 papers, 5 papers with code
A Voting Approach for Explainable Classification with Rule Learning
State-of-the-art results in typical classification tasks are mostly achieved by unexplainable machine learning methods, like deep neural networks, for instance.
Rule Learning by Modularity
In this paper, we present a modular methodology that combines state-of-the-art methods in (stochastic) machine learning with traditional methods in rule learning to provide efficient and scalable algorithms for the classification of vast data sets, while remaining explainable.
Type-Based Analysis of Logarithmic Amortised Complexity
We introduce a novel amortised resource analysis couched in a type-and-effect system.
Logic in Computer Science Programming Languages F.3.2
Runtime Complexity Analysis of Logically Constrained Rewriting
Logically constrained rewrite systems (LCTRSs) are a versatile and efficient rewriting formalism that can be used to model programs from various programming paradigms, as well as simplification systems in compilers and SMT solvers.
Computational Complexity Logic in Computer Science
Analysis of Logarithmic Amortised Complexity
We introduce a novel amortised resource analysis based on a potential-based type system.
Programming Languages F.3.2
The Complexity of Interaction (Long Version)
In this paper, we analyze the complexity of functional programs written in the interaction-net computation model, an asynchronous, parallel and confluent model that generalizes linear-logic proof nets.
Programming Languages F.3.2
