In recent years, machine learning models have achieved success based on the
independently and identically distributed assumption. However, this assumption
can be easily violated in real-world applications, leading to the
Out-of-Distribution (OOD) problem. Understanding how modern over-parameterized
DNNs behave under non-trivial natural distributional shifts is essential, as
current theoretical understanding is insufficient. Existing theoretical works
often provide meaningless results for over-parameterized models in OOD
scenarios or even contradict empirical findings. To this end, we are
investigating the performance of the over-parameterized model in terms of OOD
generalization under the general benign overfitting conditions. Our analysis
focuses on a random feature model and examines non-trivial natural
distributional shifts, where the benign overfitting estimators demonstrate a
constant excess OOD loss, despite achieving zero excess in-distribution (ID)
loss. We demonstrate that in this scenario, further increasing the model's
parameterization can significantly reduce the OOD loss. Intuitively, the
variance term of ID loss remains low due to orthogonality of long-tail
features, meaning overfitting noise during training generally doesn't raise
testing loss. However, in OOD cases, distributional shift increases the
variance term. Thankfully, the inherent shift is unrelated to individual x,
maintaining the orthogonality of long-tail features. Expanding the hidden
dimension can additionally improve this orthogonality by mapping the features
into higher-dimensional spaces, thereby reducing the variance term. We further
show that model ensembles also improve OOD loss, akin to increasing model
capacity. These insights explain the empirical phenomenon of enhanced OOD
generalization through model ensembles, supported by consistent simulations
with theoretical results.

研究了过参数化模型在 Out-of-Distribution 问题下的性能，发现模型容量的增加和模型集成有助于提高 OOD 推广能力。

过参数化对于超出分布之外的普适性的益处

On the Benefits of Over-parameterization for Out-of-Distribution  Generalization

Offline Reinforcement Learning (RL) endeavors to leverage offline datasets to
craft effective agent policy without online interaction, which imposes proper
conservative constraints with the support of behavior policies to tackle the
Out-Of-Distribution (OOD) problem. However, existing works often suffer from
the constraint conflict issue when offline datasets are collected from multiple
behavior policies, i.e., different behavior policies may exhibit inconsistent
actions with distinct returns across the state space. To remedy this issue,
recent Advantage-Weighted (AW) methods prioritize samples with high advantage
values for agent training while inevitably leading to overfitting on these
samples. In this paper, we introduce a novel Advantage-Aware Policy
Optimization (A2PO) method to explicitly construct advantage-aware policy
constraints for offline learning under mixed-quality datasets. Specifically,
A2PO employs a Conditional Variational Auto-Encoder (CVAE) to disentangle the
action distributions of intertwined behavior policies by modeling the advantage
values of all training data as conditional variables. Then the agent can follow
such disentangled action distribution constraints to optimize the
advantage-aware policy towards high advantage values. Extensive experiments
conducted on both the single-quality and mixed-quality datasets of the D4RL
benchmark demonstrate that A2PO yields results superior to state-of-the-art
counterparts. Our code will be made publicly available.

离线强化学习通过利用脱机数据集来制定有效的智能体策略而无需在线交互，以克服行为策略所支持的适当保守约束来解决分布不匹配问题。本文引入了一种新的 Advantage-Aware Policy Optimization (A2PO) 方法，用于在混合质量数据集下明确构建基于优势感知的策略约束进行离线学习。通过使用条件变分自编码器 (CVAE) 来解开错综复杂的行为策略的动作分布，并将所有训练数据的优势值建模为条件变量，A2PO 可以遵循这种解开的行为分布约束来优化面向高优势值的策略。在 D4RL 基准测试中，对单一质量和混合质量的数据集进行的广泛实验表明，A2PO 的结果优于现有的最先进的对手。我们的代码将公开发布。

离线强化学习的优势感知策略优化

Advantage-Aware Policy Optimization for Offline Reinforcement Learning

The out-of-distribution (OOD) problem generally arises when neural networks
encounter data that significantly deviates from the training data distribution,
\ie, in-distribution (InD). In this paper, we study the OOD problem from a
neuron activation view. We first formulate neuron activation states by
considering both the neuron output and its influence on model decisions. Then,
we propose the concept of \textit{neuron activation coverage} (NAC), which
characterizes the neuron behaviors under InD and OOD data. Leveraging our NAC,
we show that 1) InD and OOD inputs can be naturally separated based on the
neuron behavior, which significantly eases the OOD detection problem and
achieves a record-breaking performance of 0.03% FPR95 on ResNet-50,
outperforming the previous best method by 20.67%; 2) a positive correlation
between NAC and model generalization ability consistently holds across
architectures and datasets, which enables a NAC-based criterion for evaluating
model robustness. By comparison with the traditional validation criterion, we
show that NAC-based criterion not only can select more robust models, but also
has a stronger correlation with OOD test performance.

本文提出了一种神经元激活覆盖度（NAC）概念，并通过研究神经元行为以区分 InD 和 OOD 数据，打破了 ResNet-50 FPR95 记录并提出了 NAC 标准以评估模型健壮性。

神经元激活覆盖度：重新审视区分与泛化

Neuron Activation Coverage: Rethinking Out-of-distribution Detection and  Generalization

With the rapid development of eXplainable Artificial Intelligence (XAI), a
long line of past work has shown concerns about the Out-of-Distribution (OOD)
problem in perturbation-based post-hoc XAI models and explanations are socially
misaligned. We explore the limitations of post-hoc explanation methods that use
approximators to mimic the behavior of black-box models. Then we propose
eXplanation-based Counterfactual Retraining (XCR), which extracts feature
importance fastly. XCR applies the explanations generated by the XAI model as
counterfactual input to retrain the black-box model to address OOD and social
misalignment problems. Evaluation of popular image datasets shows that XCR can
improve model performance when only retaining 12.5% of the most crucial
features without changing the black-box model structure. Furthermore, the
evaluation of the benchmark of corruption datasets shows that the XCR is very
helpful for improving model robustness and positively impacts the calibration
of OOD problems. Even though not calibrated in the validation set like some OOD
calibration methods, the corrupted data metric outperforms existing methods.
Our method also beats current OOD calibration methods on the OOD calibration
metric if calibration on the validation set is applied.

本文提出了一种新的基于解释的反事实训练方法，即 eXplanation-based Counterfactual Retraining (XCR)，通过提取特征重要性来优化黑盒模型，解决了解释性人工智能的 Out-of-Distribution 问题和社会不协调问题，并在流行的图像数据集和数据损坏基准数据集中进行了实证研究，证明其有效性。