Bowei Xi

Publication by Area

Adversarial Machine Learning/AI Security

• Understanding Adversarial Examples Through Deep Neural Network’s Classification Boundary and Uncertainty Regions, AISafety@ IJCAI, 2022
• Adversarial Machine Learning for Cyber Security and Computer Vision: Current Developments and Challenges, to appear in Wiley Interdisciplinary Reviews: Computational Statistics, 2020
• Bowei Xi, Yujie Chen, Fei Fan, Zhan Tu, Xinyan Deng, Bio-Inspired Adversarial Attack Against Deep Neural Networks , 1--5, Proceedings of the Workshop on Artificial Intelligence Safety (SafeAI 2020) co-located with 34th AAAI Conference on Artificial Intelligence (AAAI 2020), New York, USA, Feb 7, 2020.
• Zhou, Y., Kantarcioglu, M., Xi, B., A Survey of Game Theoretic Approaches for Adversarial Machine Learning, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(3): e1259, 2019
• Zhou, Y., Kantarcioglu, M., Xi, B., Breaking Transferability of Adversarial Samples with Randomness, submitted ( Preprint is available as arXiv:1805.04613)
• Invited talk slides, Adversarial Machine Learning: Big Data Meets Cyber Security, 2018
• Three hour tutorial, Adversarial Data Mining: Big Data Meets Cyber Security, the 23rd ACM Conference on Computer and Communications Security, Vienna, Austria, October 24-28, 2016, by Kantarcioglu and Xi. Abstract is in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS 2016), pages 1866--1867. Here is the tutorial slides.
• Zhou, Y., Kantarcioglu, M., Xi, B., Adversarial Active Learning, submitted
• Wei, W., Xi, B., Kantarcioglu, M., Adversarial Clustering: A Grid Based Defensive Clustering Algorithm against Active Adversaries ( Preprint is available as arXiv:1804.04780)
• Zhou, Y., Kantarcioglu, M., Xi, B., A Survey of Game Theoretic Approaches for Adversarial Machine Learning, to appear in WIREs Data Mining and Knowledge Discovery
• Zhou, Y., Kantarcioglu, M., Xi, B., (Hot Topic Essay) Adversarial Learning: Mind Games with the Opponent, ACM Computing Reviews, August, 2017
• Kantarcioglu, M., and Xi, B., Adversarial Data Mining, North Atlantic Treaty Organization (NATO) SAS-106 Symposium on Analysis Support to Decision Making in Cyber Defence, 1--11, June, 2014, Estonia.
• Zhou, Y., Kantarcioglu, M., Thuraisingham, B., and Xi, B., Adversarial Support Vector Machine Learning, in Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2012), 1059--1067, New York, NY.
• Classifier Evaluation and Attribute Selection against Active Adversaries, Data Mining and Knowledge Discovery, 22(1-2), 291-335 (45 pages), 2011, by Murat Kantarcioglu, Bowei Xi, Chris Clifton. (An earlier version is Technical Report No.09-01, Department of Statistics, Purdue University).

Other Machine Learning and Data Mining Topics

• Sharabati, W., Xi, B., Robust Weighted Regression for Ultrasound Image Super-Resolution, IEEE Workshop on Machine Learning and Artificial Intelligence for Multimedia Creation (co-located with IEEE International Conference on Multimedia and Expo (ICME)), 1--6, San Diego, CA, July, 2018.
• Sharabati, W., Xi, B., A Neighborhood Regression Approach for Removing Multiple Types of Additive Noises, EURASIP Journal on Image and Video Processing, 2018:19, 1--14 (Featured Article, March-April 2018).
• Jiang, J., Xi, B., Kantarcioglu, M., Spatial Counts under Differential Privacy Mechanism on Changing Spatial Scales, to appear in Computer & Security
• Tong, X., Xi, B., Kantarcioglu, M., Inan, A., Mixture of Gaussian Models for Classification and Hypothesis Testing under Differential Privacy, The 31st Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy (DBSec 2017), LNCS 10359, 123–141, Springer 2017.
• Sharabati, W., Xi, B., Polynomial Local Regression for Speckle Noise Removal, 1--6, the 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, Dec, 2016 (Selected for oral presentation).
• Xi, B., Tan, K.M., and Liu, C., Logarithmic Transformation Based Gamma Random Number Generators, Journal of Statistical Software, 2013, 55(4), 1--17.
• Xi, B., Kantarcioglu, M., Inan, A., Mixture of Gaussian Models and Bayes Error under Differential Privacy, in Proceedings of the first ACM Conference on Data and Application Security and Privacy (CODASPY 2011), 179--190, Feburary 2011, San Antonio, Texas
• Fuentes, M., Xi, B., and Cleveland, W.S., Trellis Display for Modeling Data from Designed Experiments, Statistical Analysis and Data Mining, 4(1), 133--145, 2011.
• Xia, Y., and Xi, B., Conceptual Clustering Categorical Data with Uncertainty, in Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2007), 329--336, Patras, Greece, October 2007.
• Xi, B., Liu, Z., Raghavachari, M., Xia, C., and Li, Z., A Smart Hill-Climbing Algorithm for Application Server Configuration, in Proceedings of the 13th International Conference on World Wide Web (WWW 2004), 287--296, New York City, May 2004. (US Patents #7272707 and #7490234).

Big Data

• Multifractal and Gaussian Fractional Sum-Difference Models for Internet Traffic, Performance Evaluation, 107 (2017), 1-33 (33 pages) , D. Anderson, W. S. Cleveland, B. Xi (an earlier version is a 52 page Technical Report 15-02, Department of Statistics, Purdue University)
• Large Complex Data: Divide and Recombine (D&R) with RHIPE, STAT, 1(1), 53-67, 2012, by Guha, S., Hafen, R., Rounds, J., Xia, J., Li, J., Xi, B., and Cleveland, W.S. ( Project Website: DeltaRho.org ).
• Xi, B., Yang, X., Nair, V.N., and Michailidis, G., Statistical Issues in Computer Networks and Traffic Analysis, Purdue Statistics Tech Report No.15-01, 2015
• Statistical Analysis and Modeling of Internet VoIP Traffic for Network Engineering, the Electronic Journal of Statistics, 4, 58-116 (59 pages), 2010, by Xi, B., Chen, H., Cleveland, W.S., and Telkamp, T.
• Denby, L., Landwehr J., Mallows C., Meloche J., Tuck J., Xi, B., Michailidis G., and Nair, V., Statistical Aspects of the Analysis of Data Networks, Technometrics, 49(3), 318--334, 2007.
• Lawrence, E., Michailidis, G., Nair, V.N., and Xi, B., Network Tomography: A Review and Recent Developments, Frontiers in Statistics, 1--20, editors Jianqing Fan and Hira L. Koul, published by World Scientific, USA, July 2006.
• Estimating Network Loss Rates Using Active Tomography, Journal of the American Statistical Association, Volume 101, Number 476, December 2006 , pp. 1430-1448 (19 pages), by Bowei Xi, George Michailidis, and Vijayan N. Nair.
• Xi, B., Michailidis, G., and Nair, V.N., Least Squares Estimates of Network Link Loss Probabilities Using End-to-End Multicast Measurements, in Proceedings of the 37th Annual Conference on Information Sciences and Systems (CISS 2003), 1--6, Johns Hopkins University, March 2003.

Metabolomics and Biomarker Discovery

• Cheng, L. Y., Xi, B., A Likelihood Ratio Approach for Precise Discovery of Truly Relevant Protein Markers, submitted. ( Preprint is available as arXiv:1804.04249)
• Kwadwo, Owusu-Sarfo, Vincent M. Asiago, Lingli Deng, Haiwei Gu, Siwei Wei, Narasimhamurthy Shanaiah, G. A. Nagana Gowda, Bowei Xi, Elena G. Chiorean and Daniel Raftery, NMR-based Metabolite Profiling of Pancreatic Cancer, Current Metabolomics, 2(3), 204-212, 2014 (Editor Choice Articles; US patent application US 2015/0056605).
• Xi, B., Gu, H., Hamid Baniasadi, and Daniel Raftery, Statistical Analysis and Modeling of Mass Spectrometry-Based Metabolomics Data, Mass Spectrometry in Metabolomics: Methods and Protocols (2014): 333-353.
• Gu, H., Pan, Z., Xi, B., Asiago, V., Musselman, B., and Raftery, D., Principal Component Directed Partial Least Squares Analysis for Combining NMR and MS Data in Metabolomics: Application to the Detection of Breast Cancer, Analytica Chimica Acta, 686(1-2), 57--63, 2011. (Evaluated by a Member of the Faculty of 1000, and placed in the F1000 library of the top 2% of published articles in biology and medicine, 2011)
• Gu, H., Pan, Z., Xi, B., Hainline, B., Shanaiah, N., NaganaGowda, G.A., and Raftery, D., 1H NMR Metabolomics Study of Age Profiling in Children, NMR in Biomedicine, 22(8), 826-833, 2009.
• Gu, H., Chen, H., Pan, Z., Jackson, A.U., Talaty, N., Xi, B., Kissinger, C., Duda, C., Mann, D., Raftery, D., and Cooks, R.G., Monitoring Diet Effects from Biofluids and Their Implications for Metabolomics Studies, Analytical Chemistry, 79(1), 89--97, 2007. (13th most cited article in 2007).