Search Results for author:
Found 4 papers, 1 papers with code
Towards More Robust NLP System Evaluation: Handling Missing Scores in Benchmarks
This paper formalize an existing problem in NLP research: benchmarking when some systems scores are missing on the task, and proposes a novel approach to address it.
What are the best systems? New perspectives on NLP Benchmarking
In Machine Learning, a benchmark refers to an ensemble of datasets associated with one or multiple metrics together with a way to aggregate different systems performances.
Learning an Ethical Module for Bias Mitigation of pre-trained Models
In spite of the high performance and reliability of deep learning algorithms in broad range everyday applications, many investigations tend to show that a lot of models exhibit biases, discriminating against some subgroups of the population.
AUC Optimisation and Collaborative Filtering
With this in mind, we propose a class of objective functions over matrix factorisations which primarily represent a smooth surrogate for the real AUC, and in a special case we show how to prioritise the top of the list.
