698 research outputs found
Decentralized Differentially Private Without-Replacement Stochastic Gradient Descent
While machine learning has achieved remarkable results in a wide variety of
domains, the training of models often requires large datasets that may need to
be collected from different individuals. As sensitive information may be
contained in the individual's dataset, sharing training data may lead to severe
privacy concerns. Therefore, there is a compelling need to develop
privacy-aware machine learning methods, for which one effective approach is to
leverage the generic framework of differential privacy. Considering that
stochastic gradient descent (SGD) is one of the mostly adopted methods for
large-scale machine learning problems, two decentralized differentially private
SGD algorithms are proposed in this work. Particularly, we focus on SGD without
replacement due to its favorable structure for practical implementation. In
addition, both privacy and convergence analysis are provided for the proposed
algorithms. Finally, extensive experiments are performed to verify the
theoretical results and demonstrate the effectiveness of the proposed
algorithms
Signaling Models for the Valuation of IPOs: An Empirical Test of IPOs in China (1993-2004)
This dissertation empirically tests the IPO signaling models including Leland & Pyle (1977) capital structure model, Bhattacharya (1979) & Heinkel (1978) dividend model, Krinsky & Rotenberg (1989) firm risk model and a new multivariable model based on the Chinese IPO issuing market data. The regression result not only support the above existing literatures, and also prove that other factors such as state-owned or not according to the special market features in China, plays an important role in the valuation of IPOs as well. Moreover, it can be concluded from the results that the better the information disclosed, the more rational the IPO price will be
Signaling Models for the Valuation of IPOs: An Empirical Test of IPOs in China (1993-2004)
This dissertation empirically tests the IPO signaling models including Leland & Pyle (1977)'s capital structure model, Bhattacharya (1979) & Heinkel (1978)'s dividend model, Krinsky & Rotenberg (1989)'s firm risk model and a new multivariable model based on the Chinese IPO issuing market data. The regression result not only support the above existing literatures, and also prove that other factors such as state-owned or not according to the special market features in China, plays an important role in the valuation of IPOs as well. Moreover, it can be concluded from the results that the better the information disclosed, the more rational the IPO price will be
Interpretable Graph Anomaly Detection using Gradient Attention Maps
Detecting unusual patterns in graph data is a crucial task in data mining.
However, existing methods often face challenges in consistently achieving
satisfactory performance and lack interpretability, which hinders our
understanding of anomaly detection decisions. In this paper, we propose a novel
approach to graph anomaly detection that leverages the power of
interpretability to enhance performance. Specifically, our method extracts an
attention map derived from gradients of graph neural networks, which serves as
a basis for scoring anomalies. In addition, we conduct theoretical analysis
using synthetic data to validate our method and gain insights into its
decision-making process. To demonstrate the effectiveness of our method, we
extensively evaluate our approach against state-of-the-art graph anomaly
detection techniques. The results consistently demonstrate the superior
performance of our method compared to the baselines
A pilot experiment on affective multiple biosensory mapping for possible application to visual resource analysis and smart urban landscape design
This paper is designed to identify potential stressors as well as negative and positive environmental stimulators in urban landscapes, using wearable physiological sensors and GPS devices. An 8-channeled Procomp Infiniti device was used in this study, recording electrocardiogram (ECG), electroencephalogram (EEG), skin conductance, skin temperature, electromyography (EMG) of facial muscles expression and respiration, with a maximum sample rate at 1024/s. Probands in the pilot experiment were asked to take a 15-minute walk on a designated route for three times. Physiological measures were first filtered and then combined with GPS locations and visual eyesights. Affective mapping analysis based on the collected data allows first conclusions on the responsiveness of probands towards different visual experiences. Further analyses will determine the impacts of urban environments on stressors and what role latest technological advancements in smart landscape design in form of augmented reality can play for improved well-being of city dwellers
Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees
Federated learning (FL) has emerged as a prominent distributed learning
paradigm. FL entails some pressing needs for developing novel parameter
estimation approaches with theoretical guarantees of convergence, which are
also communication efficient, differentially private and Byzantine resilient in
the heterogeneous data distribution settings. Quantization-based SGD solvers
have been widely adopted in FL and the recently proposed SIGNSGD with majority
vote shows a promising direction. However, no existing methods enjoy all the
aforementioned properties. In this paper, we propose an intuitively-simple yet
theoretically-sound method based on SIGNSGD to bridge the gap. We present
Stochastic-Sign SGD which utilizes novel stochastic-sign based gradient
compressors enabling the aforementioned properties in a unified framework. We
also present an error-feedback variant of the proposed Stochastic-Sign SGD
which further improves the learning performance in FL. We test the proposed
method with extensive experiments using deep neural networks on the MNIST
dataset and the CIFAR-10 dataset. The experimental results corroborate the
effectiveness of the proposed method
- …