Weighted Risk Invariance: Domain Generalization under Invariant Feature Shift

• URL: http://arxiv.org/abs/2407.18428v1
• Date: Thu, 25 Jul 2024 23:27:10 GMT
• Title: Weighted Risk Invariance: Domain Generalization under Invariant Feature Shift
• Authors: Gina Wong, Joshua Gleason, Rama Chellappa, Yoav Wald, Anqi Liu,
• Abstract summary: Learning models whose predictions are invariant under multiple environments is a promising approach. We show that learning invariant models underperform under certain conditions. We propose a practical to implement WRI by learning the correlations $p(X_textinv) and the model parameters simultaneously.
• Score: 41.60879054101201
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning models whose predictions are invariant under multiple environments is a promising approach for out-of-distribution generalization. Such models are trained to extract features $X_{\text{inv}}$ where the conditional distribution $Y \mid X_{\text{inv}}$ of the label given the extracted features does not change across environments. Invariant models are also supposed to generalize to shifts in the marginal distribution $p(X_{\text{inv}})$ of the extracted features $X_{\text{inv}}$, a type of shift we call an $\textit{invariant covariate shift}$. However, we show that proposed methods for learning invariant models underperform under invariant covariate shift, either failing to learn invariant models$\unicode{x2014}$even for data generated from simple and well-studied linear-Gaussian models$\unicode{x2014}$or having poor finite-sample performance. To alleviate these problems, we propose $\textit{weighted risk invariance}$ (WRI). Our framework is based on imposing invariance of the loss across environments subject to appropriate reweightings of the training examples. We show that WRI provably learns invariant models, i.e. discards spurious correlations, in linear-Gaussian settings. We propose a practical algorithm to implement WRI by learning the density $p(X_{\text{inv}})$ and the model parameters simultaneously, and we demonstrate empirically that WRI outperforms previous invariant learning methods under invariant covariate shift.

Related papers

• Self-Ensembling Gaussian Splatting for Few-shot Novel View Synthesis [55.561961365113554]
  3D Gaussian Splatting (3DGS) has demonstrated remarkable effectiveness for novel view synthesis (NVS) However, the 3DGS model tends to overfit when trained with sparse posed views, limiting its generalization capacity for broader pose variations. We introduce a self-ensembling Gaussian Splatting (SE-GS) approach to alleviate the overfitting problem.
  arXiv  Detail & Related papers  (2024-10-31T18:43:48Z)
• Winning Prize Comes from Losing Tickets: Improve Invariant Learning by Exploring Variant Parameters for Out-of-Distribution Generalization [76.27711056914168]
  Out-of-Distribution (OOD) Generalization aims to learn robust models that generalize well to various environments without fitting to distribution-specific features. Recent studies based on Lottery Ticket Hypothesis (LTH) address this problem by minimizing the learning target to find some of the parameters that are critical to the task. We propose Exploring Variant parameters for Invariant Learning (EVIL) which also leverages the distribution knowledge to find the parameters that are sensitive to distribution shift.
  arXiv  Detail & Related papers  (2023-10-25T06:10:57Z)
• Probabilistic Invariant Learning with Randomized Linear Classifiers [24.485477981244593]
  We show how to leverage randomness and design models that are both expressive and invariant but use less resources. Inspired by randomized algorithms, we propose a class of binary classification models called Randomized Linears (RLCs)
  arXiv  Detail & Related papers  (2023-08-08T17:18:04Z)
• Conformalization of Sparse Generalized Linear Models [2.1485350418225244]
  Conformal prediction method estimates a confidence set for $y_n+1$ that is valid for any finite sample size. Although attractive, computing such a set is computationally infeasible in most regression problems. We show how our path-following algorithm accurately approximates conformal prediction sets.
  arXiv  Detail & Related papers  (2023-07-11T08:36:12Z)
• Statistical Learning under Heterogeneous Distribution Shift [71.8393170225794]
  Ground-truth predictor is additive $mathbbE[mathbfz mid mathbfx,mathbfy] = f_star(mathbfx) +g_star(mathbfy)$.
  arXiv  Detail & Related papers  (2023-02-27T16:34:21Z)
• A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning [113.75991721607174]
  We introduce an interventional prediction module to estimate the probability of two estimated $hatz_i, hatz_j$ belonging to the same environment. We empirically show that $hatZ$ estimated by our method enjoy less redundant information than previous methods.
  arXiv  Detail & Related papers  (2022-06-09T15:01:36Z)
• PAC Generalization via Invariant Representations [41.02828564338047]
  We consider the notion of $epsilon$-approximate invariance in a finite sample setting. Inspired by PAC learning, we obtain finite-sample out-of-distribution generalization guarantees. Our results show bounds that do not scale in ambient dimension when intervention sites are restricted to lie in a constant size subset of in-degree bounded nodes.
  arXiv  Detail & Related papers  (2022-05-30T15:50:14Z)
• Improving the Sample-Complexity of Deep Classification Networks with Invariant Integration [77.99182201815763]
  Leveraging prior knowledge on intraclass variance due to transformations is a powerful method to improve the sample complexity of deep neural networks. We propose a novel monomial selection algorithm based on pruning methods to allow an application to more complex problems. We demonstrate the improved sample complexity on the Rotated-MNIST, SVHN and CIFAR-10 datasets.
  arXiv  Detail & Related papers  (2022-02-08T16:16:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.