Uncertainty estimation

Uncertainty estimation enables detecting when ML models make mistakes. This is of critical importance in high risk machine learning applications, such as autonomous vehicle and medical ML.

Area 20. Uncertainty Estimation.svg

Posts

Publications

  • Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts

    Graph machine learningDistributional shiftUncertainty estimation
    Gleb Bazhenov
    Denis Kuznedelev
    Andrey Malinin
    Artem Babenko
    Liudmila Prokhorenkova
    NeurIPS, 2023

    In reliable decision-making systems based on machine learning, models have to be robust to distributional shifts or provide the uncertainty of their predictions. In node-level problems of graph learning, distributional shifts can be especially complex since the samples are interdependent. To evaluate the performance of graph models, it is important to test them on diverse and meaningful distributional shifts. However, most graph benchmarks considering distributional shifts for node-level problems focus mainly on node features, while structural properties are also essential for graph problems. In this work, we propose a general approach for inducing diverse distributional shifts based on graph structure. We use this approach to create data splits according to several structural node properties: popularity, locality, and density. In our experiments, we thoroughly evaluate the proposed distributional shifts and show that they can be quite challenging for existing graph models. We also reveal that simple models often outperform more sophisticated methods on the considered structural shifts. Finally, our experiments provide evidence that there is a trade-off between the quality of learned representations for the base classification task under structural distributional shift and the ability to separate the nodes from different distributions using these representations.

  • Gradient Boosting Performs Gaussian Process Inference

    Machine learning theoryUncertainty estimation Gradient boosting
    Aleksei Ustimenko
    Artem Beliakov
    Liudmila Prokhorenkova
    ICLR, 2023

    This paper shows that gradient boosting based on symmetric decision trees can be equivalently reformulated as a kernel method that converges to the solution of a certain Kernel Ridge Regression problem. Thus, we obtain the convergence to a Gaussian Process' posterior mean, which, in turn, allows us to easily transform gradient boosting into a sampler from the posterior to provide better knowledge uncertainty estimates through Monte-Carlo estimation of the posterior variance. We show that the proposed sampler allows for better knowledge uncertainty estimates leading to improved out-of-domain detection.

  • Scaling Ensemble Distribution Distillation to Many Classes with Proxy Targets

    Computer visionNatural language processing Probabilistic machine learningDistributional shiftUncertainty estimation OptimizationMachine translationSpeech processing
    Max Ryabinin
    Andrey Malinin
    Mark Gales
    NeurIPS, 2021

    Ensembles of machine learning models yield improved system performance as well as robust and interpretable uncertainty estimates; however, their inference costs can be prohibitively high. Ensemble Distribution Distillation (EnD^2) is an approach that allows a single model to efficiently capture both the predictive performance and uncertainty estimates of an ensemble. For classification, this is achieved by training a Dirichlet distribution over the ensemble members' output distributions via the maximum likelihood criterion. Although theoretically principled, this work shows that the criterion exhibits poor convergence when applied to large-scale tasks where the number of classes is very high. Specifically, we show that for the Dirichlet log-likelihood criterion classes with low probability induce larger gradients than high-probability classes. Hence during training the model focuses on the distribution of the ensemble tail-class probabilities rather than the probability of the correct and closely related classes. We propose a new training objective which minimizes the reverse KL-divergence to a Proxy-Dirichlet target derived from the ensemble. This loss resolves the gradient issues of EnD^2, as we demonstrate both theoretically and empirically on the ImageNet, LibriSpeech, and WMT17 En-De datasets containing 1000, 5000, and 40,000 classes, respectively.

Datasets

  • Shifts Dataset

    Distributional shiftUncertainty estimation Tabular dataMachine translationNatural language processing
    Andrey Malinin
    Neil Band
    Yarin Gal
    Mark J. F. Gales
    Alexander Ganshin
    German Chesnokov
    Alexey Noskov
    Andrey Ploskonosov
    Liudmila Prokhorenkova
    Ivan Provilkov
    Vatsal Raina
    Vyas Raina
    Denis Roginskiy
    Mariya Shmatova
    Panos Tigas
    Boris Yangel

    The Shifts Dataset contains curated and labeled examples of real, 'in-the-wild' distributional shifts across three large-scale tasks. Specifically, it contains tabular weather prediction, machine translation, and vehicle motion prediction tasks' data used in Shifts Challenge 2021. Dataset shift is ubiquitous in all of these tasks and modalities.