Publications

Book Chapters & Proceeding Papers

  1. R. Ramakrishna, H.-T. Wai, A. Scaglione, “A User Guide to Low-Pass Graph Signal Processing and its Applications”, IEEE Signal Processing Magazine, Nov., 2020.

  2. T.-H. Chang, M. Hong, H.-T. Wai, X. Zhang, S. Lu, “Distributed Learning in the Non-Convex World: From Batch to Streaming Data, and Beyond”, IEEE Signal Processing Magazine, May, 2020. (Equal contribution with Tsung-hui and Mingyi)

  3. S.-X. Wu, H.-T. Wai, L. Li, and A. Scaglione, “A Review of Distributed Algorithms for Principal Component Analysis”, in Proceedings of the IEEE, 2018.

  4. H.-T. Wai, A. Scaglione and E. Moulines, contributed book chapter, “Methods for decentralized signal processing with Big Data”, chapter in Cooperative and Graph Signal Processing, edited by Petar Djuric and Cedric Richard, Elsevier, June, 2018.

  5. H.-T. Wai, A. Scaglione and A. Leshem, contributed book chapter, “Active Sensing of Social Networks: Network Identification from Low Rank Data”, chapter in Cooperative and Graph Signal Processing, edited by Petar Djuric and Cedric Richard, Elsevier, June, 2018.

Journal Papers

  1. B. Turan, C. A. Uribe, H.-T. Wai, M. Alizadeh , “Resilient Primal-Dual Optimization Algorithms for Distributed Resource Allocation”, accepted by IEEE Transactions on Control of Networked Systems, to appear, 2020.

  2. T. M. Roddenberry, M. T. Schaub, H.-T. Wai, S. Segarra , “Exact Blind Community Detection from Signals on Multiple Graphs”, IEEE Transactions on Signal Processing, 2020.

  3. X. Fu, S. Ibrahim, H.-T. Wai, C. Gao, K. Huang, “Block-Randomized Stochastic Proximal Gradient for Low-Rank Tensor Factorization”, IEEE Transactions on Signal Processing, 2020.

  4. R. Wu, H.-T. Wai, and W.-K. Ma, “Hybrid Inexact BCD for Coupled Structured Matrix Factorization in Hyperspectral Super-Resolution”, IEEE Transactions on Signal Processing, 2020.

  5. H.-T. Wai, W. Shi, C. A. Uribe, A. Nedic, A. Scaglione, “Accelerating incremental gradient optimization with curvature information”, Computational Optimization and Applications, 2020.

  6. H.-T. Wai, S. Segarra, A. Ozdaglar, A. Scaglione, and A. Jadbabaie, “Blind community detection from low-rank excitations of a graph filter”, IEEE Transactions on Signal Processing, 2020.

  7. H.-T. Wai, A. Scaglione, B. Barzel and A. Leshem, “Joint Network Topology and Dynamics Recovery from Perturbed Stationary Points”, IEEE Transactions on Signal Processing, 2019.

  8. S.-X. Wu, H.-T. Wai and A. Scaglione, “Estimating Social Opinion Dynamics Models from Voting Records”, IEEE Transactions on Signal Processing, August, 2018.

  9. M. Alizadeh, H.-T. Wai, A. Goldsmith and A. Scaglione, “Retail and Wholesale Electricity Pricing Considering Electric Vehicle Mobility”, IEEE Transaction on Control of Networked Systems, March, 2019.

  10. H.-T. Wai, J. Lafond, A. Scaglione and E. Moulines, “Decentralized Frank-Wolfe Algorithm for Convex and Non-convex Optimization”, IEEE Transactions on Automatic Control, Nov., 2017.

  11. R. Gentz, S.-X. Wu, H.-T. Wai, A. Scaglione and A. Leshem, “Data injection attacks in randomized gossiping”, IEEE Transactions on Signal and Information Processing over Networks, Dec., 2016.

  12. M. Alizadeh, H.-T. Wai, M. Chowdhury, A. Scaglione, A. Goldsmith, and T. Javidi, “Optimal Pricing to Manage Electric Vehicles in Coupled Power and Transportation Networks”, IEEE Transaction on Control of Networked Systems, Dec., 2017.

  13. H.-T. Wai, Q. Li and W.-K. Ma, “Discrete Sum Rate Maximization for MISO Interference Broadcast Channels: Convex Approximations and Efficient Algorithms”, IEEE Transaction on Signal Processing, Aug., 2016.

  14. H.-T. Wai, A. Scaglione and A. Leshem, “Active Sensing of Social Networks”, IEEE Transactions on Signal and Information Processing over Networks, Sept., 2016.

  15. H.-T. Wai and A. Scaglione, “Consensus on State and Time: Decentralized Regression with Asynchronous Sampling”, IEEE Transaction on Signal Processing, June, 2015.

  16. Q. Li, M. Hong, H.-T. Wai, W.-K. Ma, Y.-F. Liu, and Z.-Q. Luo, “Transmit Solutions for MIMO Wiretap Channels using Alternating Optimization and Water-Filling”, IEEE Journal on Selected Areas in Communications, vol. 31, no. 9, pp. 1714-1727, Sept. 2013.

Conference Papers (Computer Science & Machine Learning)

  1. A. Durmus, E. Moulines, A. Naumov, S. Samsonov, H.-T. Wai, “On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning”, in COLT 2021. (Equal Contribution)

  2. R. Wu, A. Scaglione, H.-T. Wai, N. Karakoc, K. Hreinsson, W.-K. Ma, “Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models”, in AAAI 2021.

  3. H.-T. Wai, Z. Yang, Z. Wang, M. Hong, “Provably Efficient Neural GTD for Off-Policy Learning”, in NeurIPS 2020.

  4. G. Fort, E. Moulines, H.-T. Wai, “A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm”, in NeurIPS 2020. (Equal Contribution)

  5. M. Kaledin, E. Moulines, A. Naumov, V. Tadic and H.-T. Wai, “Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise”, in COLT 2020. (Equal Contribution)

  6. B. Karimi, H.-T. Wai, E. Moulines and M. Lavielle, “On the Global Convergence of (Fast) Incremental Expectation Maximization Methods”, in NeurIPS 2019. (Equal Contribution with B. Karimi)

  7. H.-T. Wai, Z. Yang, Z. Wang, M. Hong and X. Tang, “Variance Reduced Policy Evaluation with Smooth Function Approximation”, in NeurIPS 2019.

  8. B. Karimi, B. Miasojedow, E. Moulines and H.-T. Wai, “Non-asymptotic Analysis of Biased Stochastic Approximation Scheme”, in COLT 2019. (Equal Contribution)

  9. G. Robin, H.-T. Wai, J. Josse, O. Klopp and E. Moulines, “Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames”, in NeurIPS 2018.

  10. H.-T. Wai, Z. Yang, Z. Wang and M. Hong, “Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization”, in NeurIPS 2018. (Codes)

Conference Papers (Signal Process & Control, Selected)

  1. B. Turan, C. A. Uribe, H.-T. Wai, M. Alizadeh , “On Robustness of the Normalized Subgradient Method with Randomly Corrupted Subgradients”, in ACC, 2021.

  2. Y. He and H.-T. Wai, “Idenftifying First-Order Lowpass Graph Signals Using Perron Frobenius Theorem”, in ICASSP 2021.

  3. Y. He and H.-T. Wai, “Provably Fast Asynchronous and Distributed Algorithms for PageRank Centrality Computation”, in ICASSP 2021.

  4. G. Fort, E. Moulines, H.-T. Wai, “GEOM-SPIDER-EM: Faster Variance Reduced Stochastic Expectation Maximization for Nonconvex Finite-sum Optimization”, in ICASSP 2021.

  5. H.-T. Wai, “On the Convergence of Consensus Algorithms with Markovian Noise and Gradient Bias”, in CDC 2020.

  6. Y. He and H.-T. Wai, “Estimating Centrality Blindly from Low-pass Filtered Graph Signals”, in ICASSP 2020.

  7. C. A. Uribe, H.-T. Wai, and M. Alizadeh, “Resilient Distributed Optimization Algorithms for Resource Allocation”, in CDC 2019. (Equal Contribution with C. A. Uribe)

  8. M. Schaub, S. Segarra and H.-T. Wai, “Spectral Partitioning of Time-Varying Networks with Unobserved Edges”, in ICASSP 2019. (Equal Contribution)

  9. H.-T. Wai, Y. Eldar, A. Ozdaglar and A. Scaglione, “Community Inference from Graph Signals with Hidden Nodes”, in ICASSP 2019.

  10. H.-T. Wai, N. Freris, A. Nedić and A. Scaglione, “SUCAG: Stochastic Unbiased Curvature-aided Gradient Method for Distributed Optimization”, Invited paper, in CDC 2018.

  11. H.-T. Wai, S. Segarra, A. Ozdaglar, A. Scaglione and A. Jadbabaie, “Community Detection from Low Rank Excitations of a Graph Filter”, in Proc. ICASSP 2018. (Best student paper)

  12. H.-T. Wai, A. Ozdaglar and A. Scaglione, “Identifying Susceptible Agents in Time Varying Opinion Dynamics through Compressive Measurements”, in Proc. ICASSP 2018.

  13. H.-T. Wai, W. Shi, A. Nedić and A. Scaglione, “Curvature-aided Incremental Aggregated Gradient Method”, Invited paper, Allerton Conference, Oct. 2017.