79 research outputs found
Machine Unlearning by Suppressing Sample Contribution
Machine Unlearning (MU) is to forget data from a well-trained model, which is
practically important due to the "right to be forgotten". In this paper, we
start from the fundamental distinction between training data and unseen data on
their contribution to the model: the training data contributes to the final
model while the unseen data does not. We theoretically discover that the input
sensitivity can approximately measure the contribution and practically design
an algorithm, called MU-Mis (machine unlearning via minimizing input
sensitivity), to suppress the contribution of the forgetting data. Experimental
results demonstrate that MU-Mis outperforms state-of-the-art MU methods
significantly. Additionally, MU-Mis aligns more closely with the application of
MU as it does not require the use of remaining data
QueryNet: Attack by Multi-Identity Surrogates
Deep Neural Networks (DNNs) are acknowledged as vulnerable to adversarial
attacks, while the existing black-box attacks require extensive queries on the
victim DNN to achieve high success rates. For query-efficiency, surrogate
models of the victim are used to generate transferable Adversarial Examples
(AEs) because of their Gradient Similarity (GS), i.e., surrogates' attack
gradients are similar to the victim's ones. However, it is generally neglected
to exploit their similarity on outputs, namely the Prediction Similarity (PS),
to filter out inefficient queries by surrogates without querying the victim. To
jointly utilize and also optimize surrogates' GS and PS, we develop QueryNet, a
unified attack framework that can significantly reduce queries. QueryNet
creatively attacks by multi-identity surrogates, i.e., crafts several AEs for
one sample by different surrogates, and also uses surrogates to decide on the
most promising AE for the query. After that, the victim's query feedback is
accumulated to optimize not only surrogates' parameters but also their
architectures, enhancing both the GS and the PS. Although QueryNet has no
access to pre-trained surrogates' prior, it reduces queries by averagely about
an order of magnitude compared to alternatives within an acceptable time,
according to our comprehensive experiments: 11 victims (including two
commercial models) on MNIST/CIFAR10/ImageNet, allowing only 8-bit image
queries, and no access to the victim's training data. The code is available at
https://github.com/Sizhe-Chen/QueryNet.Comment: QueryNet reduces queries by about an order of magnitude against SOTA
black-box attack
Does ownership concentration affect corporate environmental responsibility engagement? The mediating role of corporate leverage
This paper examines the effect of ownership concentration on engagement in corporate environmental responsibility (CER) in time and spatial dimensions. The time dimension focuses on the macroeconomic environment, in particular, periods of rapid and moderate-speed economic growth. The spatial dimension focuses on industry characteristics and different types of ownership (state or private). Further, it explores the mediating role of corporate leverage using panel regression models and stepwise regression with a sample of Chinese A-share listed companies over the period 2008–2016. The results show that ownership concentration has a significantly negative effect on CER. In addition, when we consider the macroeconomic growth rate, ownership type, and industry characteristics, the effect is heterogeneous. In periods with rapid economic growth, ownership concentration has a significantly negative effect on CER whereas it is not significant in a period with moderate economic growth. Further, the negative effect exists at state-owned and non-state-owned companies and at non-heavy-polluting industries. Corporate leverage has a partial mediating effect between ownership concentration and engagement in CER
Online Continual Learning via Logit Adjusted Softmax
Online continual learning is a challenging problem where models must learn
from a non-stationary data stream while avoiding catastrophic forgetting.
Inter-class imbalance during training has been identified as a major cause of
forgetting, leading to model prediction bias towards recently learned classes.
In this paper, we theoretically analyze that inter-class imbalance is entirely
attributed to imbalanced class-priors, and the function learned from
intra-class intrinsic distributions is the Bayes-optimal classifier. To that
end, we present that a simple adjustment of model logits during training can
effectively resist prior class bias and pursue the corresponding Bayes-optimum.
Our proposed method, Logit Adjusted Softmax, can mitigate the impact of
inter-class imbalance not only in class-incremental but also in realistic
general setups, with little additional computational cost. We evaluate our
approach on various benchmarks and demonstrate significant performance
improvements compared to prior arts. For example, our approach improves the
best baseline by 4.6% on CIFAR10
Religious atmosphere, seismic impact, and corporate charitable donations in China
This study examines the external socio-cultural and natural environment factors that driving corporate philanthropy in China. We focus on two predominant influences: religiosity, specifically the traditional Three-Teachings (Confucianism, Buddhism, and Taoism), and seismic activities. Using a large sample of 31,673 firm-year observations from Chinese listed firms from 2009 to 2020, our findings reveal that (a) firms immersed in more pronounced religious-cultural presence have higher donation incentives, and (b) firms experiencing higher seismic impacts or are located in high seismic risk areas show heightened corporate philanthropic tendencies. Our multidisciplinary approach bridges various academic disciplines, presenting an innovative framework for understanding the intersection of corporate philanthropy, socio-cultural environments, and natural disasters in China. Overall, we highlight the importance of external environmental factors in shaping corporate charitable behaviours
Spatial relevancy of digital finance in the urban agglomeration of Pearl River Delta and the influence factors
At present, the rapid development of digital finance is closely related to the economic development of urban agglomerations. An urban agglomeration provides conditions for digital finance to form a spatial relevancy network. Exploring the development of digital finance in the urban agglomeration of the Pearl River Delta (PRD), which is the bellwether of China's economy, can provide important practical experience for the economic construction of coastal areas and even the whole country. In this study, using the urban digital finance index issued by the Guangzhou Institute of International Finance, we measured the intensity and direction of the spatial relevancy of digital finance in the PRD urban agglomeration by applying the gravity model, modified in the calculation of distance between cities. Then, we examined the influencing factors of the spatial network of digital finance through the quadratic assignment procedure (QAP) approach. The achieved results are as follows. First, although the overall density is low, the network is tightly connected and stable. Second, in terms of individual characteristics of the network, Guangzhou, Shenzhen, Foshan still play the leading roles in the spatial network of digital finance. Third, the digital finance network does not have bidirectional spillover block. The links between segments are relatively loose. Fourth, economic level, degree of opening up, Internet level and geographical location are important factors in driving the formation of spatial relevancy of digital finance in the PRD urban agglomeration
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