For many machine learning algorithms, two main assumptions are required to
guarantee performance. One is that the test data are drawn from the same
distribution as the training data, and the other is that the model is correctly
specified. In real applications, however, we often have little prior knowledge
on the test data and on the underlying true model. Under model
misspecification, agnostic distribution shift between training and test data
leads to inaccuracy of parameter estimation and instability of prediction
across unknown test data. To address these problems, we propose a novel
Decorrelated Weighting Regression (DWR) algorithm which jointly optimizes a
variable decorrelation regularizer and a weighted regression model. The
variable decorrelation regularizer estimates a weight for each sample such that
variables are decorrelated on the weighted training data. Then, these weights
are used in the weighted regression to improve the accuracy of estimation on
the effect of each variable, thus help to improve the stability of prediction
across unknown test data. Extensive experiments clearly demonstrate that our
DWR algorithm can significantly improve the accuracy of parameter estimation
and stability of prediction with model misspecification and agnostic
distribution shift.

提出了一种新的 Decorrelated Weighting Regression (DWR) 算法，能够提高模型错误规范化和未知测试数据下的参数估计准确性和预测稳定性。

模型错误规范和不可知分布转移下的稳定预测

Stable Prediction with Model Misspecification and Agnostic Distribution  Shift

Following the recent adoption of deep neural networks (DNN) accross a wide
range of applications, adversarial attacks against these models have proven to
be an indisputable threat. Adversarial samples are crafted with a deliberate
intention of undermining a system. In the case of DNNs, the lack of better
understanding of their working has prevented the development of efficient
defenses. In this paper, we propose a new defense method based on practical
observations which is easy to integrate into models and performs better than
state-of-the-art defenses. Our proposed solution is meant to reinforce the
structure of a DNN, making its prediction more stable and less likely to be
fooled by adversarial samples. We conduct an extensive experimental study
proving the efficiency of our method against multiple attacks, comparing it to
numerous defenses, both in white-box and black-box setups. Additionally, the
implementation of our method brings almost no overhead to the training
procedure, while maintaining the prediction performance of the original model
on clean samples.

本文提出了一种基于实践观察的新的防御方法，旨在强化深度神经网络的结构，提高其预测稳定性，从而更难受到针对性攻击，并在多种攻击实验中证明了该方法的有效性，相比其他防御方法具有更好的表现，而且在训练过程中的开销几乎可以忽略不计。