Publications

Book Chapters & Proceeding Papers

1. S. Gannot, Z.-H. Tan, M. Haardt, N. F. Chan, H.-T. Wai, I. Tashev, W. Kellermann, J. Dauwels, “Data Science Education: The Signal Processing Perspective”, IEEE Signal Processing Magazine, 2023.

2. 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.

3. 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)

4. 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.

5. 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.

6. 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. C.-Y. Yau, H.-T. Wai, “DoCoM: Compressed Decentralized Optimization with Near-Optimal Sample Complexity”, Transactions on Machine Learning Research, 2023.

2. A. Dieuleveut, G. Fort, E. Moulines, H.-T. Wai, “Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning”, IEEE Transactions on Signal Processing (Overview Paper), 2023. (Equal Contribution)

3. Y. He, H.-T. Wai, “Online Inference for Mixture Model of Streaming Graph Signals with Sparse Excitation”, IEEE Transactions on Signal Processing, 2023.

4. M. Hong, H.-T. Wai, Z. Yang, Z. Wang, “A two-timescale framework for bilevel optimization: Complexity analysis and application to actor-critic”, SIAM Journal on Optimization, 2023. (Equal contribution)

5. B. Turan, C. A. Uribe, H.-T. Wai, M. Alizadeh, “Robust Distributed Optimization With Randomly Corrupted Gradients”, IEEE Transactions on Signal Processing, 2022.

6. Y. He, H.-T. Wai, “Detecting Central Nodes from Low-rank Excited Graph Signals via Structured Factor Analysis”, IEEE Transactions on Signal Processing, 2022.

7. H.-T. Wai, Y. Eldar, A. Ozdaglar, A. Scaglione , “Community Inference from Partially Observed Graph Signals: Algorithms and Analysis”, IEEE Transactions on Signal Processing, 2022.

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

9. 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.

10. 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.

11. 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.

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

13. 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.

14. 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.

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

16. 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.

17. 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.

18. 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.

19. 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.

20. 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.

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

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

23. 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. X. Wang, C.-Y. Yau, H.-T. Wai, “Network Effects on Performative Prediction Games”, in ICML 2023.

2. X. Wang, Y. Jiao, H.-T. Wai, Y. Gu, “Incremental Aggregated Riemannian Gradient Method for Distributed PCA”, in AISTATS 2023.

3. Q. Li, C.-Y. Yau, H.-T. Wai, “Multi-agent Performative Prediction with Greedy Deployment and Consensus Seeking Agents”, in NeurIPS 2022.

4. B. Song, I. Tsaknakis, C.-Y. Yau, H.-T. Wai, M. Hong, “Distributed Optimization for Overparameterized Problems: Achieving Optimal Dimension Independent Communication Complexity”, in NeurIPS 2022.

5. B. Liu, J. Li, Z. Yang, H.-T. Wai, M. Hong, Y. Nie, Z. Wang, “Inducing Equilibria via Incentives: Simultaneous Design-and-Play Ensures Global Convergence”, in NeurIPS 2022.

6. P. Khanduri, H. Yang, M. Hong, J. Liu, H.-T. Wai, S. Liu, “Decentralized Learning for Overparameterized Problems: A Multi-Agent Kernel Approximation Approach", in ICLR 2022.

7. Q. Li, H.-T. Wai, “State Dependent Performative Prediction with Stochastic Approximation”, in AISTATS 2022.

8. B. Karimi, H.-T. Wai, E. Moulines, P. Li “MISSO: Minimization by Incremental Stochastic Surrogate Optimization for Large Scale Nonconvex Problems”, in ALT 2022. (Equal Contribution with B. Karimi)

9. A. Durmus, E. Moulines, A. Naumov, S. Samsonov, K. Scaman, H.-T. Wai, “Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize”, in NeurIPS 2021. (Equal Contribution)

10. P. Khanduri, S. Zeng, M. Hong, H.-T. Wai, Z. Wang, Z. Yang, "A near-optimal algorithm for stochastic bilevel optimization via double-momentum”, in NeurIPS 2021.

11. 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)

12. 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.

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

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

15. 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)

16. 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)

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

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

19. 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.

20. 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. C. Zhang and H.-T. Wai, “Learning Multiplex Graph with Inter-layer Coupling”, in IEEE ICASSP 2024.

2. C.-Y. Yau, H.-T. Wai, “Fully Stochastic Distributed Convex Optimization on Time-Varying Graph with Compression”, in CDC 2023.

3. X. Wang, J. Cheng, H.-T. Wai, Y. Gu, “Linear Speedup of Incremental Aggregated Gradient Methods on Streaming Data”, in CDC 2023.

4. C. Zhang, Y. He and H.-T. Wai, “Product Graph Learning from Multi-attribute Graph Signals with Inter-layer Coupling”, in IEEE ICASSP 2023.

5. Y. He and H.-T. Wai, “Central Nodes Detection from Partially Observed Graph Signals”, in IEEE ICASSP 2023.

6. Q. Li and H.-T. Wai, “On the Role of Data Homogeneity in Multi-Agent Non-convex Stochastic Optimization”, in IEEE CDC 2022.

7. Y. Huang, J. Xu, W. Meng and H.-T. Wai, “Stochastic Gradient Tracking Methods for Distributed Personalized Optimization over Networks”, in IEEE CDC 2022.

8. H.-S. Nguyen, Y. He and H.-T. Wai, “On the Stability of Low Pass Graph Filter With a Large Number of Edge Rewires”, in IEEE ICASSP 2022.

9. Y. He and H.-T. Wai, “Joint Centrality Estimation and Graph Identification from Mixture of Low Pass Graph Signals”, in IEEE ICASSP 2022.

10. A. Ashok Rao, H.-T. Wai, “An Empirical Study on Compressed Decentralized Stochastic Gradient Algorithms with Overparameterized Models”, in APSIPA 2021.

11. B. Turan, C.A. Uribe, H.-T. Wai, M. Alizadeh, “On Robustness of the Normalized Random Block Coordinate Method for Non-Convex Optimization”, in IEEE CDC 2021.

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

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

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

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

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

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

18. 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)

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

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

21. 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.

22. 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)

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

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