2023

1. 

   Long-distance Targeted Poisoning Attacks on Graph Neural Networks

   Anqi Zhang, Aurojit Panda, Jinyang Li, and Siddhartha Sen

   Preprints, , 2023

2022

1. 

   Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments

   Anqi Zhang, Jinkun Lin, Mathias Lécuyer, Jinyang Li, Aurojit Panda, and Siddhartha Sen

   In Proceedings of the 39th International Conference on Machine Learning, 17–23 jul 2022

   Abs PDF

   We develop a new, principled algorithm for estimating the contribution of training data points to the behavior of a deep learning model, such as a specific prediction it makes. Our algorithm estimates the AME, a quantity that measures the expected (average) marginal effect of adding a data point to a subset of the training data, sampled from a given distribution. When subsets are sampled from the uniform distribution, the AME reduces to the well-known Shapley value. Our approach is inspired by causal inference and randomized experiments: we sample different subsets of the training data to train multiple submodels, and evaluate each submodel’s behavior. We then use a LASSO regression to jointly estimate the AME of each data point, based on the subset compositions. Under sparsity assumptions (k ≪N datapoints have large AME), our estimator requires only O(k\log N) randomized submodel trainings, improving upon the best prior Shapley value estimators.

2017

1. SCI

   WLAN interference self-optimization using som neural networks

   Haipeng Yao, Hao Yang, Anqi Zhang, Chao Fang, and Yiru Guo

   Concurrency and Computation: Practice and Experience, 17–23 jul 2017

   e3913 cpe.3913

   Abs PDF

   Summary In order to suppress the interference in local area networks, this paper presents a Wireless Local Area Networks (WLAN) interference self-optimization method based on a Self-Organizing Feature Map (SOM) neural network model. This method trains the model by using original data sets as the initial vector set and using the whole Signal to Interference plus Noise Ratio (SINR) vector generated by the change of one Wireless Access Point (AP) channel as the basic feature. After the training, the SOM neural network can quickly locate the fault AP and optimize the network according to the changes of the network environment. Simulation results reveal that the proposed scheme can efficiently locate the AP where interference happens and optimize the interference with an improved user experience. Copyright © 2016 John Wiley & Sons, Ltd.

2016

1. IEEE

   Optimization Design and Application of Intelligent Parking Lot in Smart City

   Yang Hao, Anqi Zhang, and Dou Yingtong

   Published in IEEE Third International Conference on Universal Village, 6-8 OCT, Japan, , 17–23 jul 2016