scholarly journals Re-identification risk prediction paradigm using incomplete statistical information and recursive hypergeometric distribution

2021 ◽  
Author(s):  
Zhigang Yang ◽  
Ruyan Wang ◽  
Dapeng Wu ◽  
Boran Yang ◽  
Daizhong Luo
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Frequency Distribution ◽  
Real World ◽  
Binomial Distribution ◽  
General Rule ◽  
Statistical Information ◽  
Superior Performance ◽  
Real World Datasets ◽  
Huge Challenge

Abstract The dataset anonymization has not eliminated the re-identification risk, the evaluation of which remains a huge challenge, especially given incomplete statistical information. The re-identification risk of individuals depends on their tuple frequency. The paper proposes the recursive hypergeometric (RH) distribution to accurately calculate the tuple frequency and leverages the binomial distribution to approximate the RH distribution and to efficiently predict the re-identification risk of individuals in both generated and real-world datasets. The experimental results show that our tuple frequency based re-identification risk (TFRR) prediction model has a superior performance (average AUC 0.86~0.98) for all types of datasets. Furthermore, we exploit the value dependence knowledge to rectify the prediction result for some subsets (average AUC 0.95~0.98). Our research reveals the general rule of the tuple frequency distribution and enables individuals and regulators to responsively predict the re-identification risk.

scholarly journals Objective-aware Traffic Simulation via Inverse Reinforcement Learning

2021 ◽  
Author(s):  
Guanjie Zheng ◽  
Hanyang Liu ◽  
Kai Xu ◽  
Zhenhui Li
Keyword(s):  
Reinforcement Learning ◽  
Physical Model ◽  
Real World ◽  
Traffic Simulation ◽  
Transportation Systems ◽  
Superior Performance ◽  
Inverse Reinforcement Learning ◽  
Reward Function ◽  
Car Following Model ◽  
Real World Datasets

Traffic simulators act as an essential component in the operating and planning of transportation systems. Conventional traffic simulators usually employ a calibrated physical car-following model to describe vehicles' behaviors and their interactions with traffic environment. However, there is no universal physical model that can accurately predict the pattern of vehicle's behaviors in different situations. A fixed physical model tends to be less effective in a complicated environment given the non-stationary nature of traffic dynamics. In this paper, we formulate traffic simulation as an inverse reinforcement learning problem, and propose a parameter sharing adversarial inverse reinforcement learning model for dynamics-robust simulation learning. Our proposed model is able to imitate a vehicle's trajectories in the real world while simultaneously recovering the reward function that reveals the vehicle's true objective which is invariant to different dynamics. Extensive experiments on synthetic and real-world datasets show the superior performance of our approach compared to state-of-the-art methods and its robustness to variant dynamics of traffic.

scholarly journals Stabilizing Adversarial Invariance Induction from Divergence Minimization Perspective

2020 ◽  
Author(s):  
Yusuke Iwasawa ◽  
Kei Akuzawa ◽  
Yutaka Matsuo
Keyword(s):  
Neural Network ◽  
Real World ◽  
Optimization Procedure ◽  
Superior Performance ◽  
Simple Variant ◽  
Invariant Representations ◽  
Real World Datasets ◽  
Divergence Minimization

Adversarial invariance induction (AII) is a generic and powerful framework for enforcing an invariance to nuisance attributes into neural network representations. However, its optimization is often unstable and little is known about its practical behavior. This paper presents an analysis of the reasons for the optimization difficulties and provides a better optimization procedure by rethinking AII from a divergence minimization perspective. Interestingly, this perspective indicates a cause of the optimization difficulties: it does not ensure proper divergence minimization, which is a requirement of the invariant representations. We then propose a simple variant of AII, called invariance induction by discriminator matching, which takes into account the divergence minimization interpretation of the invariant representations. Our method consistently achieves near-optimal invariance in toy datasets with various configurations in which the original AII is catastrophically unstable. Extentive experiments on four real-world datasets also support the superior performance of the proposed method, leading to improved user anonymization and domain generalization.

Sampling Sparse Representations with Randomized Measurement Langevin Dynamics

2021 ◽  
Vol 15 (2) ◽  
pp. 1-21
Author(s):  
Kafeng Wang ◽  
Haoyi Xiong ◽  
Jiang Bian ◽  
Zhanxing Zhu ◽  
Qian Gao ◽  
...  
Keyword(s):  
Real World ◽  
Kernel Density ◽  
Langevin Dynamics ◽  
Sparse Representations ◽  
Superior Performance ◽  
Algorithm Analysis ◽  
High Dimensional ◽  
Measurement Matrix ◽  
Real World Datasets ◽  
Derivatives Of

Stochastic Gradient Langevin Dynamics (SGLD) have been widely used for Bayesian sampling from certain probability distributions, incorporating derivatives of the log-posterior. With the derivative evaluation of the log-posterior distribution, SGLD methods generate samples from the distribution through performing as a thermostats dynamics that traverses over gradient flows of the log-posterior with certainly controllable perturbation. Even when the density is not known, existing solutions still can first learn the kernel density models from the given datasets, then produce new samples using the SGLD over the kernel density derivatives. In this work, instead of exploring new samples from kernel spaces, a novel SGLD sampler, namely, Randomized Measurement Langevin Dynamics (RMLD) is proposed to sample the high-dimensional sparse representations from the spectral domain of a given dataset. Specifically, given a random measurement matrix for sparse coding, RMLD first derives a novel likelihood evaluator of the probability distribution from the loss function of LASSO, then samples from the high-dimensional distribution using stochastic Langevin dynamics with derivatives of the logarithm likelihood and Metropolis–Hastings sampling. In addition, new samples in low-dimensional measuring spaces can be regenerated using the sampled high-dimensional vectors and the measurement matrix. The algorithm analysis shows that RMLD indeed projects a given dataset into a high-dimensional Gaussian distribution with Laplacian prior, then draw new sparse representation from the dataset through performing SGLD over the distribution. Extensive experiments have been conducted to evaluate the proposed algorithm using real-world datasets. The performance comparisons on three real-world applications demonstrate the superior performance of RMLD beyond baseline methods.

scholarly journals SpHMC: Spectral Hamiltonian Monte Carlo

2019 ◽  
Vol 33 ◽  
pp. 5516-5524
Author(s):  
Haoyi Xiong ◽  
Kafeng Wang ◽  
Jiang Bian ◽  
Zhanxing Zhu ◽  
Cheng-Zhong Xu ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Probability Distribution ◽  
Real World ◽  
Dimensional Space ◽  
Probability Distributions ◽  
Superior Performance ◽  
High Dimensional ◽  
Hamiltonian Monte Carlo ◽  
Real World Datasets ◽  
Low Dimensional

Stochastic Gradient Hamiltonian Monte Carlo (SGHMC) methods have been widely used to sample from certain probability distributions, incorporating (kernel) density derivatives and/or given datasets. Instead of exploring new samples from kernel spaces, this piece of work proposed a novel SGHMC sampler, namely Spectral Hamiltonian Monte Carlo (SpHMC), that produces the high dimensional sparse representations of given datasets through sparse sensing and SGHMC. Inspired by compressed sensing, we assume all given samples are low-dimensional measurements of certain high-dimensional sparse vectors, while a continuous probability distribution exists in such high-dimensional space. Specifically, given a dictionary for sparse coding, SpHMC first derives a novel likelihood evaluator of the probability distribution from the loss function of LASSO, then samples from the high-dimensional distribution using stochastic Langevin dynamics with derivatives of the logarithm likelihood and Metropolis–Hastings sampling. In addition, new samples in low-dimensional measuring spaces can be regenerated using the sampled high-dimensional vectors and the dictionary. Extensive experiments have been conducted to evaluate the proposed algorithm using real-world datasets. The performance comparisons on three real-world applications demonstrate the superior performance of SpHMC beyond baseline methods.

Development of Neurobehavioral Deterioration Risk Prediction Model for Welder: A Proposed Study

2018 ◽  
Vol 10 (5) ◽  
Author(s):  
Nuur Azreen Paiman ◽  
◽  
Azian Hariri ◽  
Ibrahim Masood ◽  
Arma Noor ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model

Time-Efficient Ensemble Learning with Sample Exchange for Edge Computing

2021 ◽  
Vol 21 (3) ◽  
pp. 1-17
Author(s):  
Wu Chen ◽  
Yong Yu ◽  
Keke Gai ◽  
Jiamou Liu ◽  
Kim-Kwang Raymond Choo
Keyword(s):  
Ensemble Learning ◽  
Real World ◽  
Interaction Mechanism ◽  
Training Model ◽  
Edge Computing ◽  
Learning Techniques ◽  
Multi Agent ◽  
Real World Datasets ◽  
Entire Dataset ◽  
Exchange Data

In existing ensemble learning algorithms (e.g., random forest), each base learner’s model needs the entire dataset for sampling and training. However, this may not be practical in many real-world applications, and it incurs additional computational costs. To achieve better efficiency, we propose a decentralized framework: Multi-Agent Ensemble. The framework leverages edge computing to facilitate ensemble learning techniques by focusing on the balancing of access restrictions (small sub-dataset) and accuracy enhancement. Specifically, network edge nodes (learners) are utilized to model classifications and predictions in our framework. Data is then distributed to multiple base learners who exchange data via an interaction mechanism to achieve improved prediction. The proposed approach relies on a training model rather than conventional centralized learning. Findings from the experimental evaluations using 20 real-world datasets suggest that Multi-Agent Ensemble outperforms other ensemble approaches in terms of accuracy even though the base learners require fewer samples (i.e., significant reduction in computation costs).

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