text: 406,3,1446326187416961026,990433714948661250,Sergey Levine,"Is there a principled way to adapt a model to distributional shift without labels? In our new paper, ""Training on Test Data"", we propose a Bayesian adaptation strategy based on BNNs and entropy minimization. w/ Aurick Zhou: <LINK> A thread: First: what information do we gain from observing unlabeled datapoints from a new distribution? We can draw a graphical model for this: x is input, y is label, theta is classifier params, phi parameterizes x distro. Unfortunately, if y is unobserved, x tells nothing about theta! <LINK> We need a better graphical model. What if we assume new datapoints are not arbitrary: if we are asked to classify a new OOD, it likely belongs to *one of* the classes, we just don't know which one! Now there is a relationship between theta and phi for each distribution! <LINK> This naturally leads to an entropy minimization procedure at test time: get some unlabeled points, and then update the parameter posterior to get lower entropy on test points, but don't stray too far from parameter distribution on training set! <LINK> To avoid needing to store all training data, we can learn posterior q(theta) using any BNN approach, and then incorporate this as a regularizer when minimizing entropy at test time on unlabeled data. <LINK> This leads to better accuracy *and* better calibration on unlabeled test points. Inspired by some classics on entropy minimization: Y. Grandvalet, Y. Bengio. Semi-supervised Learning by Entropy Minimization M. Seeger. Input-dependent Regularization of Conditional Density Models",https://arxiv.org/abs/2109.12746,"When faced with distribution shift at test time, deep neural networks often make inaccurate predictions with unreliable uncertainty estimates. While improving the robustness of neural networks is one promising approach to mitigate this issue, an appealing alternate to robustifying networks against all possible test-time shifts is to instead directly adapt them to unlabeled inputs from the particular distribution shift we encounter at test time. However, this poses a challenging question: in the standard Bayesian model for supervised learning, unlabeled inputs are conditionally independent of model parameters when the labels are unobserved, so what can unlabeled data tell us about the model parameters at test-time? In this paper, we derive a Bayesian model that provides for a well-defined relationship between unlabeled inputs under distributional shift and model parameters, and show how approximate inference in this model can be instantiated with a simple regularized entropy minimization procedure at test-time. We evaluate our method on a variety of distribution shifts for image classification, including image corruptions, natural distribution shifts, and domain adaptation settings, and show that our method improves both accuracy and uncertainty estimation. ",Training on Test Data with Bayesian Adaptation for Covariate Shift,6,"['Is there a principled way to adapt a model to distributional shift without labels? In our new paper, ""Training on Test Data"", we propose a Bayesian adaptation strategy based on BNNs and entropy minimization. w/ Aurick Zhou: <LINK>\n\nA thread:', 'First: what information do we gain from observing unlabeled datapoints from a new distribution? We can draw a graphical model for this: x is input, y is label, theta is classifier params, phi parameterizes x distro. Unfortunately, if y is unobserved, x tells nothing about theta! https://t.co/E2w5hxaYyV', ""We need a better graphical model. What if we assume new datapoints are not arbitrary: if we are asked to classify a new OOD, it likely belongs to *one of* the classes, we just don't know which one! Now there is a relationship between theta and phi for each distribution! https://t.co/2BPaQCR0yh"", ""This naturally leads to an entropy minimization procedure at test time: get some unlabeled points, and then update the parameter posterior to get lower entropy on test points, but don't stray too far from parameter distribution on training set! https://t.co/TdgRlHmEZQ"", 'To avoid needing to store all training data, we can learn posterior q(theta) using any BNN approach, and then incorporate this as a regularizer when minimizing entropy at test time on unlabeled data. https://t.co/cHlCQLDQRu', 'This leads to better accuracy *and* better calibration on unlabeled test points.\n\nInspired by some classics on entropy minimization:\n\nY. Grandvalet, Y. Bengio. Semi-supervised Learning by Entropy Minimization\nM. Seeger. Input-dependent Regularization of Conditional Density Models']",21,09,1543