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> Associate Professor Hoi To WAI
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韋凱滔教授
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副教授
,
系統工程與工程管理學系
ORCiD
0000-0003-4796-4483
中大研究成果
1/2
Learning Multiplex Graph With Inter-Layer Coupling
(
2024
)
A Two-Timescale Stochastic Algorithm Framework for Bilevel Optimization: Complexity Analysis and Application to Actor-Critic
(
2023
)
Central Nodes Detection from Partially Observed Graph Signals
(
2023
)
Data Science Education: The Signal Processing Perspective [SP Education]
(
2023
)
Incremental Aggregated Riemannian Gradient Method for Distributed PCA
(
2023
)
Network Effects in Performative Prediction Games
(
2023
)
Product Graph Learning From Multi-Attribute Graph Signals with Inter-Layer Coupling
(
2023
)
Secure Integrated Sensing and Communication Downlink Beamforming: A Semidefinite Relaxation Approach with Tightness Guaranteed
(
2023
)
Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning
(
2023
)
Community Inference From Partially Observed Graph Signals: Algorithms and Analysis
(
2022
)
Decentralized Learning for Overparameterized Problems: A Multi-Agent Kernel Approximation Approach
(
2022
)
Detecting central nodes from low-rank excited graph signals via structured factor analysis
(
2022
)
Distributed Optimization for Overparameterized Problems: Achieving Optimal Dimension Independent Communication Complexity
(
2022
)
Inducing Equilibria via Incentives: Simultaneous Design-and-Play Ensures Global Convergence
(
2022
)
Joint Centrality Estimation and Graph Identification from Mixture of Low Pass Graph Signals
(
2022
)
Minimization by Incremental Stochastic Surrogate Optimization for Large Scale Nonconvex Problems
(
2022
)
Multi-agent Performative Prediction with Greedy Deployment and Consensus Seeking Agents
(
2022
)
Online Inference for Mixture Model of Streaming Graph Signals with Sparse Excitation
(
2022
)
On the Role of Data Homogeneity in Multi-Agent Non-convex Stochastic Optimization
(
2022
)
On the Stability of Low Pass Graph Filter with a Large Number of Edge Rewires
(
2022
)
Robust Distributed Optimization With Randomly Corrupted Gradients
(
2022
)
State Dependent Performative Prediction with Stochastic Approximation
(
2022
)
Stochastic Gradient Tracking Methods for Distributed Personalized Optimization over Networks
(
2022
)
A near-optimal algorithm for stochastic bilevel optimization via double-momentum
(
2021
)
An Empirical Study on Compressed Decentralized Stochastic Gradient Algorithms with Overparameterized Models
(
2021
)
Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models
(
2021
)
GEOM-SPIDER-EM: Faster Variance Reduced Stochastic Expectation Maximization for Nonconvex Finite-Sum Optimization
(
2021
)
Identifying First-Order Lowpass Graph Signals Using Perron Frobenius Theorem
(
2021
)
On Robustness of the Normalized Random Block Coordinate Method for Non-Convex Optimization
(
2021
)
On Robustness of the Normalized Subgradient Method with Randomly Corrupted Subgradients
(
2021
)
On the stability of random matrix product with Markovian noise: Application to linear stochastic approximation and TD learning
(
2021
)
Provably Fast Asynchronous And Distributed Algorithms For Pagerank Centrality Computation
(
2021
)
Resilient Primal-Dual Optimization Algorithms for Distributed Resource Allocation
(
2021
)
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize
(
2021
)
Accelerating incremental gradient optimization with curvature information
(
2020
)
A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm
(
2020
)
A User Guide to Low-Pass Graph Signal Processing and Its Applications: Tools and Applications
(
2020
)
Blind Community Detection From Low-Rank Excitations of a Graph Filter
(
2020
)
Block-Randomized Stochastic Proximal Gradient for Low-Rank Tensor Factorization
(
2020
)
Distributed Learning in the Non-Convex World: From Batch to Streaming Data, and Beyond
(
2020
)
Estimating Centrality Blindly From Low-Pass Filtered Graph Signals
(
2020
)
Exact blind community detection from signals on multiple graphs
(
2020
)
Finite time analysis of linear two-timescale stochastic approximation with Markovian noise
(
2020
)
Hybrid Inexact BCD for Coupled Structured Matrix Factorization in Hyperspectral Super-Resolution
(
2020
)
On the Convergence of Consensus Algorithms with Markovian noise and Gradient Bias
(
2020
)
Provably Efficient Neural GTD for Off-Policy Learning
(
2020
)
Block-randomized Stochastic Proximal Gradient for Constrained Low-rank Tensor Factorization
(
2019
)
Community Inference from Graph Signals with Hidden Nodes
(
2019
)
Joint Network Topology and Dynamics Recovery from Perturbed Stationary Points
(
2019
)
Non-asymptotic Analysis of Biased Stochastic Approximation Scheme
(
2019
)
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