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