Wasserstein Differential Privacy

Differential privacy (DP) has achieved remarkable results in the field of privacy-preserving machine learning. However, existing DP frameworks do not satisfy all the conditions for becoming metrics, which prevents them from deriving better basic private properties and leads to exaggerated values on privacy budgets. We propose Wasserstein differential privacy (WDP), an alternative DP framework to measure the risk of privacy leakage, which satisfies the properties of symmetry and triangle inequality. We show and prove that WDP has 13 excellent properties, which can be theoretical supports for the better performance of WDP than other DP frameworks. In addition, we derive a general privacy accounting method called Wasserstein accountant, which enables WDP to be applied in stochastic gradient descent (SGD) scenarios containing sub-sampling. Experiments on basic mechanisms, compositions and deep learning show that the privacy budgets obtained by Wasserstein accountant are relatively stable and less influenced by order. Moreover, the overestimation on privacy budgets can be effectively alleviated. The code is available at https://github.com/Hifipsysta/WDP.Comment: Accepted by AAAI 202

The momentum effect in the Chinese market and its relationship with the simultaneous and the lagged investor sentiment

Motivated by the lack of investigation on the behavioral interpretation on the momentum premium, this paper addresses this issue by focusing on the effect of investor sentiment on a sample of the comprehensive Chinese A-share index covering the period from 2006 to 2015. Expect for uncovering the momentum effect in the A-share market by calculating the momentum returns of ten zero-cost portfolios differed on the formation period, we compare the momentum returns under different sentiment states during the sample period. The difference is obvious that the momentum returns are more evident during the optimistic sentiment period where estimated investor sentiment is over zero. This paper also examines whether the investor sentiment explains the momentum returns and its predictive power on the subsequent momentum premiums. We find the contemporaneous linear relationship between investor sentiment and the momentum returns is less pronounced. Even the slopes of sentiment are positive, only three of them are significant. However, the investor sentiment exhibits strong predictability on future returns of momentum strategy in the short-run, suggesting it can be a contrarian predictor of expected returns of momentum in the short-run

The momentum effect in the Chinese market and its relationship with the simultaneous and the lagged investor sentiment

Motivated by the lack of investigation on the behavioral interpretation on the momentum premium, this paper addresses this issue by focusing on the effect of investor sentiment on a sample of the comprehensive Chinese A-share index covering the period from 2006 to 2015. Expect for uncovering the momentum effect in the A-share market by calculating the momentum returns of ten zero-cost portfolios differed on the formation period, we compare the momentum returns under different sentiment states during the sample period. The difference is obvious that the momentum returns are more evident during the optimistic sentiment period where estimated investor sentiment is over zero. This paper also examines whether the investor sentiment explains the momentum returns and its predictive power on the subsequent momentum premiums. We find the contemporaneous linear relationship between investor sentiment and the momentum returns is less pronounced. Even the slopes of sentiment are positive, only three of them are significant. However, the investor sentiment exhibits strong predictability on future returns of momentum strategy in the short-run, suggesting it can be a contrarian predictor of expected returns of momentum in the short-run

Accelerated Federated Learning with Decoupled Adaptive Optimization

The federated learning (FL) framework enables edge clients to collaboratively learn a shared inference model while keeping privacy of training data on clients. Recently, many heuristics efforts have been made to generalize centralized adaptive optimization methods, such as SGDM, Adam, AdaGrad, etc., to federated settings for improving convergence and accuracy. However, there is still a paucity of theoretical principles on where to and how to design and utilize adaptive optimization methods in federated settings. This work aims to develop novel adaptive optimization methods for FL from the perspective of dynamics of ordinary differential equations (ODEs). First, an analytic framework is established to build a connection between federated optimization methods and decompositions of ODEs of corresponding centralized optimizers. Second, based on this analytic framework, a momentum decoupling adaptive optimization method, FedDA, is developed to fully utilize the global momentum on each local iteration and accelerate the training convergence. Last but not least, full batch gradients are utilized to mimic centralized optimization in the end of the training process to ensure the convergence and overcome the possible inconsistency caused by adaptive optimization methods

VisionGPT: Vision-Language Understanding Agent Using Generalized Multimodal Framework

With the emergence of large language models (LLMs) and vision foundation models, how to combine the intelligence and capacity of these open-sourced or API-available models to achieve open-world visual perception remains an open question. In this paper, we introduce VisionGPT to consolidate and automate the integration of state-of-the-art foundation models, thereby facilitating vision-language understanding and the development of vision-oriented AI. VisionGPT builds upon a generalized multimodal framework that distinguishes itself through three key features: (1) utilizing LLMs (e.g., LLaMA-2) as the pivot to break down users' requests into detailed action proposals to call suitable foundation models; (2) integrating multi-source outputs from foundation models automatically and generating comprehensive responses for users; (3) adaptable to a wide range of applications such as text-conditioned image understanding/generation/editing and visual question answering. This paper outlines the architecture and capabilities of VisionGPT, demonstrating its potential to revolutionize the field of computer vision through enhanced efficiency, versatility, and generalization, and performance. Our code and models will be made publicly available. Keywords: VisionGPT, Open-world visual perception, Vision-language understanding, Large language model, and Foundation modelComment: 17 pages, 5 figures, and 1 table. arXiv admin note: substantial text overlap with arXiv:2311.1012

Identifying interacting genetic variations by fish-swarm logic regression

Understanding associations between genotypes and complex traits is a fundamental problem in human genetics. A major open problem in mapping phenotypes is that of identifying a set of interacting genetic variants, which might contribute to complex traits. Logic regression (LR) is a powerful multivariant association tool. Several LR-based approaches have been successfully applied to different datasets. However, these approaches are not adequate with regard to accuracy and efficiency. In this paper, we propose a new LR-based approach, called fish-swarm logic regression (FSLR), which improves the logic regression process by incorporating swarm optimization. In our approach, a school of fish agents are conducted in parallel. Each fish agent holds a regression model, while the school searches for better models through various preset behaviors. A swarm algorithm improves the accuracy and the efficiency by speeding up the convergence and preventing it from dropping into local optimums. We apply our approach on a real screening dataset and a series of simulation scenarios. Compared to three existing LR-based approaches, our approach outperforms them by having lower type I and type II error rates, being able to identify more preset causal sites, and performing at faster speeds