8 papers
Ensemble Distillation Approaches for Grammatical Error Correction
Yassir Fathullah, Mark Gales, Andrey Malinin
Ensemble approaches are commonly used techniques to improving a system by combining multiple model predictions. Additionally these schemes allow the uncertainty, as well as the sou…
Regression Prior Networks
Andrey Malinin, Sergey Chervontsev, Ivan Provilkov +1
Prior Networks are a recently developed class of models which yield interpretable measures of uncertainty and have been shown to outperform state-of-the-art ensemble approaches on…
Uncertainty in Gradient Boosting via Ensembles
Andrey Malinin, Liudmila Prokhorenkova, Aleksei Ustimenko
For many practical, high-risk applications, it is essential to quantify uncertainty in a model's predictions to avoid costly mistakes. While predictive uncertainty is widely studie…
Uncertainty Estimation in Autoregressive Structured Prediction
Andrey Malinin, Mark Gales
Uncertainty estimation is important for ensuring safety and robustness of AI systems. While most research in the area has focused on un-structured prediction tasks, limited work ha…
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness
Andrey Malinin, Mark Gales
Ensemble approaches for uncertainty estimation have recently been applied to the tasks of misclassification detection, out-of-distribution input detection and adversarial attack de…
Ensemble Distribution Distillation
Andrey Malinin, Bruno Mlodozeniec, Mark Gales
Ensembles of models often yield improvements in system performance. These ensemble approaches have also been empirically shown to yield robust measures of uncertainty, and are capa…