Real-world machine learning applications often face simultaneous covariate and semantic shifts, challenging traditional domain generalization and out-of-distribution (OOD) detection methods. We introduce Meta-learned Across Domain Out-of-distribution Detection (MADOD), a novel framework designed to address both shifts concurrently. MADOD leverages meta-learning and G-invariance to enhance model generalizability and OOD detection in unseen domains. Our key innovation lies in task construction: we randomly designate in-distribution classes as pseudo-OODs within each meta-learning task, simulating OOD scenarios using existing data. This approach, combined with energy-based regularization, enables the learning of robust, domain-invariant features while calibrating decision boundaries for effective OOD detection. Operating in a test domain-agnostic setting, MADOD eliminates the need for adaptation during inference, making it suitable for scenarios where test data is unavailable. Extensive experiments on real-world and synthetic datasets demonstrate MADOD's superior performance in semantic OOD detection across unseen domains, achieving an AUPR improvement of 8.48% to 20.81%, while maintaining competitive in-distribution classification accuracy, representing a significant advancement in handling both covariate and semantic shifts.
Carnegie Mellon University（卡内基梅隆大学）；Chinese Academy of Science（中国科学院）；University of Cambridge（剑桥大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Alan Turing Institute（艾伦·图灵研究所）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）
A fundamental challenge for machine learning models is generalizing to out-of-distribution (OOD) data, in part due to spurious correlations. To tackle this challenge, we first formalize the OOD generalization problem as constrained optimization, called Disentanglement-constrained Domain Generalization (DDG). We relax this non-trivial constrained optimization problem to a tractable form with finite-dimensional parameterization and empirical approximation. Then a theoretical analysis of the extent to which the above transformations deviates from the original problem is provided. Based on the tra…
Out-of-Domain Generalization in Dynamical Systems Reconstruction
Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University（海德堡大学曼海姆医学院中央精神卫生研究所）；Heidelberg University（海德堡大学）；Interdisciplinary Center for Scientific Computing, Heidelberg University（海德堡大学跨学科科学计算中心）
Merging Smarter, Generalizing Better: Enhancing Model Merging on OOD Data
College of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；Rensselaer Polytechnic Institute（拉特盖尔理工学院）；College of applied Science, Taiyuan University of Science and Technology（太原科技大学应用科学学院）；CSIRO’s Data61（CSIRO数据61）
Data modification, either via additional training datasets, data augmentation, debiasing, and dataset filtering, has been proposed as an effective solution for generalizing to out-of-domain (OOD) inputs, in both natural language processing and computer vision literature. However, the effect of data modification on adversarial robustness remains unclear. In this work, we conduct a comprehensive study of common data modification strategies and evaluate not only their in-domain and OOD performance, but also their adversarial robustness (AR). We also present results on a two-dimensional synthetic …
Deep neural networks are known to be overconfident when applied to out-of-distribution (OOD) inputs which clearly do not belong to any class. This is a problem in safety-critical applications since a reliable assessment of the uncertainty of a classifier is a key property, allowing the system to trigger human intervention or to transfer into a safe state. In this paper, we aim for certifiable worst case guarantees for OOD detection by enforcing not only low confidence at the OOD point but also in an $l_\infty$-ball around it. For this purpose, we use interval bound propagation (IBP) to upper b…
Domain generalization aims to improve the generalization capability of machine learning systems to out-of-distribution (OOD) data. Existing domain generalization techniques embark upon stationary and discrete environments to tackle the generalization issue caused by OOD data. However, many real-world tasks in non-stationary environments (e.g. self-driven car system, sensor measures) involve more complex and continuously evolving domain drift, which raises new challenges for the problem of domain generalization. In this paper, we formulate the aforementioned setting as the problem of evolving d…
Institute for AI Industry Research (AIR), Tsinghua University（清华大学智能产业研究院（AIR））；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
In offline reinforcement learning (RL), one detrimental issue to policy learning is the error accumulation of deep Q function in out-of-distribution (OOD) areas. Unfortunately, existing offline RL methods are often over-conservative, inevitably hurting generalization performance outside data distribution. In our study, one interesting observation is that deep Q functions approximate well inside the convex hull of training data. Inspired by this, we propose a new method, DOGE (Distance-sensitive Offline RL with better GEneralization). DOGE marries dataset geometry with deep function approximato…
Towards Robust Out-of-Distribution Generalization Bounds via Sharpness
School of Computing, National University of Singapore（新加坡国立大学计算机学院）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究院）；Department of Computer Science & Technology, Tsinghua University（清华大学计算机科学与技术系）
Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection
Institute for Foundations of Data Science, University of Wisconsin, Madison（数据科学基础研究院，威斯康星大学麦迪逊分校）；Department of Computer Sciences, University of Wisconsin, Madison（计算机科学系，威斯康星大学麦迪逊分校）；Department of Electrical and Computer Engineering, University of Wisconsin, Madison（电气与计算机工程系，威斯康星大学麦迪逊分校）
On the Robustness of Open-World Test-Time Training: Self-Training with Dynamic Prototype Expansion
South China University of Technology（华南理工大学）；Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局（A*STAR）信息通信研究院（I2R））
Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；Polytechnique Montréal（蒙特利尔综合理工学院）
Meta-Learning algorithms for few-shot learning aim to train neural networks capable of generalizing to novel tasks using only a few examples. Early-stopping is critical for performance, halting model training when it reaches optimal generalization to the new task distribution. Early-stopping mechanisms in Meta-Learning typically rely on measuring the model performance on labeled examples from a meta-validation set drawn from the training (source) dataset. This is problematic in few-shot transfer learning settings, where the meta-test set comes from a different target dataset (OOD) and can pote…
Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）
A major bottleneck in the real-world applications of machine learning models is their failure in generalizing to unseen domains whose data distribution is not i.i.d to the training domains. This failure often stems from learning non-generalizable features in the training domains that are spuriously correlated with the label of data. To address this shortcoming, there has been a growing surge of interest in learning good explanations that are hard to vary, which is studied under the notion of Out-of-Distribution (OOD) Generalization. The search for good explanations that are \textit{invariant} …
How Out-of-Distribution Detection Learning Theory Enhances Transformer: Learnability and Reliability
Shanghai Jiao Tong University, China（上海交通大学）；University of Oxford, UK（牛津大学）；Shanghai AI Laboratory, China（上海人工智能实验室）；University of New South Wales, Sydney, Australia（新南威尔士大学）；Shanghai Innovation Institute, China（上海创新研究院）
Microsoft Research（微软研究院）；MILA-Quebec AI Institute（米拉-魁北克人工智能研究所）；University of Montreal（蒙特利尔大学）
With the goal of generalizing to out-of-distribution (OOD) data, recent domain generalization methods aim to learn "stable" feature representations whose effect on the output remains invariant across domains. Given the theoretical connection between generalization and privacy, we ask whether better OOD generalization leads to better privacy for machine learning models, where privacy is measured through robustness to membership inference (MI) attacks. In general, we find that the relationship does not hold. Through extensive evaluation on a synthetic dataset and image datasets like MNIST, Fashi…
NORCE Norwegian Research Centre AS（NORCE挪威研究中心）；University of Stavanger（斯塔万格大学）
Generalizing the application of machine learning models to situations where the statistical distribution of training and test data are different has been a complex problem. Our contributions in this paper are threefold: (1) we introduce an anchored-based Out of Distribution (OOD) Regression Mixup algorithm, leveraging manifold hidden state mixup and observation similarities to form a novel regularization penalty, (2) we provide a first of its kind, high resolution Distributed Acoustic Sensor (DAS) dataset that is suitable for testing OOD regression modelling, allowing other researchers to benc…
