Google Scholar Profile 

Preprints

• Mehrdad Pournaderi and Yu Xiang, "Training-conditional coverage bounds under covariate shift", May 2024

• Austin V. Goddard,  Audrey Su, Yu Xiang, and Craig J. Bryan, "Multi-environment prediction of suicidal beliefs", submitted to Frontiers in Psychiatry, 2024.

Journal Articles

• Mehrdad Pournaderi and Yu Xiang, "Variable selection with the knockoffs: composite null hypotheses", Journal of Statistical Planning and Inference, vol. 231, pp. 106--119, 2024.

• Kang Du and Yu Xiang, "Causal inference from slowly varying nonstationary processes", IEEE Transactions on Signal and Information Processing over Networks, vol. 10, pp. 406--416, 2024.

• Kang Du and Yu Xiang, "Learning invariant representations under general interventions on the response", IEEE Journal on Selected Areas in Information Theory, vol. 4, pp. 808--819, 2023. [Code]

• Mehrdad Pournaderi and Yu Xiang, "On large-scale multiple testing over networks: an asymptotic approach", IEEE Transactions on Signal and Information Processing over Networks, vol. 9, pp. 442--457, 2023.

• Austin V. Goddard, Yu Xiang, and Craig J. Bryan, "Invariance-based causal prediction to identify the direct causes of suicidal behavior", Frontiers in Psychiatry, 2022.

• Yu Xiang, Jie Ding, and Vahid Tarokh, "Estimation of the evolutionary spectra with application to stationarity test'',  IEEE Transactions on Signal Processing, vol 67, no. 5, pp 1353--1365, 2019. [Code]

• Ilya Soloveychik, Yu Xiang, and Vahid Tarokh, "Symmetric pseudo-random matrices'', IEEE Transaction on Information Theory, vol. 64, no. 4, pp. 3179--3196, April 2018.

• Ilya Soloveychik, Yu Xiang, and Vahid Tarokh, "Pseudo-Wigner matrices'', IEEE Transaction on Information Theory, vol. 64, no. 4, pp. 3170--3178, April 2018.

• Jie Ding, Yu Xiang, Lu Shen, and Vahid Tarokh, "Multiple change point analysis: fast implementation and strong consistency'', IEEE Transactions on Signal Processing, vol. 65, no. 17, pp. 4495--4510, 2017.

• Hamed Farhadi, Yu Xiang, Seongah Jeong, Xiang Li, Ning Guo, Jorge Sepulcre, Vahid Tarokh, and Quanzheng Li, "Inferring the causality network of Abeta and Tau accumulation in the aging brain: a statistical inference approach'',  Journal of Nuclear Medicine, vol. 58 no. supplement 1 803, May 1, 2017.

• Yu Xiang and Young-Han Kim, “Gaussian channel with noisy feedback and peak energy constraint”,  IEEE Transaction on Information Theory, vol. 59, no. 8, pp. 4746--4756, August 2013.

 Conference Proceedings

• Austin V. Goddard, Kang Du, and Yu Xiang, "Mining invariance from nonlinear multi-environment data: binary classification", IEEE International Symposium on Information Theory (ISIT), Athens, Greece, July 2024.

• Mehrdad Pournaderi and Yu Xiang, "Training-conditional coverage bounds for uniformly stable learning algorithms",  IEEE International Symposium on Information Theory (ISIT) Workshop on Information-Theoretic Methods for Trustworthy Machine Learning (IT-TML), Athens, Greece, July 2024.

• Kang Du and Yu Xiang, "Low-rank approximation of structural redundancy for self-supervised learning", Conference on Causal Learning and Reasoning (CLeaR), Los Angeles, CA, April 2024. [Code]

• Mehrdad Pournaderi and Yu Xiang, "Communication-efficient distribution-free inference over networks", IEEE Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, October 2023.

• Kang Du, Yu Xiang, and Ilya Soloveychik, "Identifying direct causes using intervened target variable", IEEE Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, October 2023.

• Austin V. Goddard, Yu Xiang, and Ilya Soloveychik, "Error probability bounds for Invariant causal prediction via multiple access channels", IEEE Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, October 2023.

• Mehrdad Pournaderi and Yu Xiang, "Sample-and-forward: communication-efficient control of the false discovery rate in networks", IEEE International Symposium on Information Theory (ISIT),  pp. 1949--1954, Taipei, Taiwan, June 2023.

• Kang Du and Yu Xiang, "Generalized invariant matching property via lasso", IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),  pp. 1--5, Rhodes Island, Greece, June 2023.

• Kang Du and Yu Xiang, "Learning invariant representations under general interventions on the response",  Neural Information Processing Systems (NeurIPS) Workshop on Distribution Shifts (DistShift), New Orleans, LA, December 2022. (spotlight presentation, acceptance rate ~ 6%)

• Austin V. Goddard, Yu Xiang, and Ilya Soloveychik, "Lower bounds on the error probability for invariant causal prediction", IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1--6, Xi'an, China, August 2022.

• Kang Du and Yu Xiang, "An invariant matching property for distribution generalization under intervened response", IEEE European Signal Processing Conference (EUSIPCO), 1387--1391, Belgrade, Serbia, August 2022.

• Mehrdad Pournaderi and Yu Xiang, "Communication-efficient distributed multiple testing for large-scale inference",  IEEE International Symposium on Information Theory (ISIT),  pp. 1477--1482, Espoo, Finland, June 2022.

• Mehrdad Pournaderi and Yu Xiang, "Differentially private variable selection via the knockoff filter", IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1--6, Gold Coast, Australia (virtual conference), October 2021.

• Kang Du and Yu Xiang, "Causal inference using linear time-varying filters with additive noise",  IEEE International Symposium on Information Theory (ISIT),  pp. 896--901, Melbourne, Australia (virtual conference), July 2021.

• Austin V. Goddard and Yu Xiang, "A subsampling-based method for causal discovery on discrete data", IEEE Statistical Signal Processing Workshop (SSP), pp. 341--345, Rio de Janeiro, Brazil (virtual conference), July 2021.

• Xinran Wang, Yu Xiang, Jun Gao, and Jie Ding, "Information laudering for model privacy", International Conference on Learning Representations (ICLR), Vienna, Austria (virtual conference), May 2021. (spotlight presentation, acceptance rate ~ 6%) 

• Austin V. Goddard, Yu Xiang, Justin C. Baker, Lauren R. Khazem, Jeffrey Tabares and Craig J. Bryan, "Identifying the causal relationships for psychological research using data-driven approaches",  Association for Psychological Science Annual Convention, Chicago, IL, May 2020. 

• Mehrdad Pournaderi and Yu Xiang, "Sketching for variable selection via the knockoff filter", Information Theory and Applications Workshop (ITA), invited talk, San Diego, CA, Feb. 2020. 

• Kang Du, Austin V. Goddard, and Yu Xiang, "On the robustness of causal discovery with additive noise models on discrete data", IEEE Data Compression Conference (DCC), Salt Lake City, UT, March 2020. 

• Yu Xiang, "Distributed false discovery rate control with quantization",  IEEE International Symposium on Information Theory (ISIT),  pp. 246--249, Paris, France, July 2019. 

• Yu Xiang, Jie Ding, and Vahid Tarokh, "Evolutionary spectra based on the multitaper method with application to stationarity test'', IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),  pp. 3994--3998, Calgary, Canada, April 2018.

• Jie Ding, Yu Xiang, Lu Shen, and Vahid Tarokh, "Detecting structural changes in dependent data'', IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 750--754,  Montreal, Canada, November 2017.

• Ilya Soloveychik, Yu Xiang, and Vahid Tarokh, "Explicit symmetric pseudo-random matrices'', IEEE Information Theory Workshop (ITW), pp. 424--428, Kaohsiung, Taiwan, November 2017.

• Ilya Soloveychik, Yu Xiang, and Vahid Tarokh, "Pseudo-Wigner matrices from dual BCH codes'', IEEE International Symposium on Information Theory (ISIT), pp. 1381--1385, Aachen, Germany, July 2017. 

• Hamed Farhadi, Yu Xiang, Seongah Jeong, Xiang Li, Ning Guo, Jorge Sepulcre, Vahid Tarokh, and Quanzheng Li, "Inferring the causality network of Abeta and Tau accumulation in the aging brain: a statistical inference approach'', The Annual Meeting of the Society of Nuclear Medicine & Molecular Imaging, Denver, CO, June 2017.  (oral presentation, acceptance rate ~ 20%)

• Jie Ding, Yu Xiang, Lu Shen, and Vahid Tarokh, "Multiple change point analysis: fast implementation and strong consistency'',  International Conference on Machine Learning (ICML), Anomaly Detection Workshop, New York, NY, June 2016. 

• Yu Xiang and Young-Han Kim, "A few meta-theorems in network information theory”, IEEE Information Theory Workshop (ITW), Hobart, Australia, November 2014.

• Yu Xiang and Young-Han Kim, “Interactive hypothesis testing against independence”,  International Symposium on Information Theory (ISIT), pp. 2840--2844, Istanbul, Turkey, July 2013. 

• Yu Xiang and Young-Han Kim, “Interactive hypothesis testing with communication constraints”,  the Annual Allerton Conference on Communication, Control, and Computing, pp. 1065–-1072, Monticello,IL, October 2012.

• Yu Xiang, Lele Wang, and Young-Han Kim, “Information flooding”, the Annual Allerton Conference on Communication, Control, and Computing, pp. 45--51, Monticello, IL, September 2011.

• Yu Xiang and Young-Han Kim, “On the AWGN channel with noisy feedback and peak energy constraint”, IEEE International Symposium on Information Theory (ISIT), pp. 256--259, Austin, TX, June 2010.

Reports

• Yu Xiang, “Shape percolation”, Project of ECE 257C Stochastic wireless networks models, Spring 2012.

• Yu Xiang, “Universal decoding for finite state channels”, Project of ECE 287A Universal information processing, Spring 2011.

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