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Found 11 papers, 4 papers with code
Robust Stochastic Graph Generator for Counterfactual Explanations
Counterfactual Explanation (CE) techniques have garnered attention as a means to provide insights to the users engaging with AI systems.
Towards a Prediction of Machine Learning Training Time to Support Continuous Learning Systems Development
In particular, we present an extensive empirical study of the Full Parameter Time Complexity (FPTC) approach by Zheng et al., which is, to the best of our knowledge, the only approach formalizing the training time of ML models as a function of both dataset's and model's parameters.
A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation
Due to the growing attention in graph learning, we focus on the concepts of CE for GNNs.
Modeling Quality and Machine Learning Pipelines through Extended Feature Models
Over the years, several solutions have been proposed to automate the building of ML pipelines, most of them focused on semantic aspects and characteristics of the input dataset.
GRETEL: A unified framework for Graph Counterfactual Explanation Evaluation
To present GRETEL, we show the experiments conducted to integrate and test several synthetic and real datasets with several existing explanation techniques and base ML models.
ECIR 2020 Workshops: Assessing the Impact of Going Online
In this report, we describe the experience of organizing the ECIR 2020 Workshops in this scenario from two perspectives: the workshop organizers and the workshop participants.
Unsupervised Boosting-based Autoencoder Ensembles for Outlier Detection
Autoencoders, as a dimensionality reduction technique, have been recently applied to outlier detection.
Graph-based Selective Outlier Ensembles
A problem with this approach is that poor components are likely to negatively affect the quality of the consensus result.
What to Write? A topic recommender for journalists
In this paper we present a recommender system, What To Write and Why, capable of suggesting to a journalist, for a given event, the aspects still uncovered in news articles on which the readers focus their interest.
Hashtag Sense Clustering Based on Temporal Similarity
Hashtags are creative labels used in micro-blogs to characterize the topic of a message/discussion.
