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> Dean; Professor Man Cho SO
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Professor SO Man Cho
Personal Information
Position and Department
Professor
,
Department of Systems Engineering and Engineering Management
Dean
,
Graduate School
ORCiD
0000-0003-2588-7851
URL
https://www1.se.cuhk.edu.hk/~manchoso/
CUHK Research Outputs
1 of 3
Global Strong Convexity and Characterization of Critical Points of Time-of-Arrival-Based Source Localization
(
2024
)
Nonsmooth Optimization over the Stiefel Manifold and Beyond: Proximal Gradient Method and Recent Variants
(
2024
)
Riemannian Natural Gradient Methods
(
2024
)
ROCS: Robust One-Bit Compressed Sensing with Application to Direction of Arrival
(
2024
)
A Communication-Efficient Decentralized Newton’s Method with Provably Faster Convergence
(
2023
)
A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data
(
2023
)
A Linearly Convergent Optimization Framework for Learning Graphs from Smooth Signals
(
2023
)
A Unified Approach to Synchronization Problems over Subgroups of the Orthogonal Group
(
2023
)
A Unified Flow Scheduling Method for Time Sensitive Networks
(
2023
)
Integrated approach of data analytics, simulation, and system optimisation to evaluate emergency department performance in Hong Kong: abridged secondary publication
(
2023
)
Linear Convergence of a Proximal Alternating Minimization Method with Extrapolation for ℓ1-Norm Principal Component Analysis
(
2023
)
LogSpecT: Feasible Graph Learning Model from Stationary Signals with Recovery Guarantees
(
2023
)
On the Effectiveness of Parameter-Efficient Fine-Tuning
(
2023
)
Outlier-Robust Gromov-Wasserstein for Graph Data
(
2023
)
Projected Tensor Power Method for Hypergraph Community Recovery
(
2023
)
ReSync: Riemannian Subgradient-based Robust Rotation Synchronization
(
2023
)
Universal Gradient Descent Ascent Method for Nonconvex-Nonconcave Minimax Optimization
(
2023
)
Variance-Reduced Stochastic Quasi-Newton Methods for Decentralized Learning
(
2023
)
Adaptive Coordinate Sampling for Stochastic Primal–Dual Optimization
(
2022
)
Computing D-Stationary Points of ρ-Margin Loss SVM
(
2022
)
Convergence and Recovery Guarantees of the K-Subspaces Method for Subspace Clustering
(
2022
)
Distributionally Robust Graph Learning from Smooth Signals under Moment Uncertainty
(
2022
)
Exact Community Recovery over Signed Graphs
(
2022
)
Local Strong Convexity of Source Localization and Error Bound for Target Tracking under Time-of-Arrival Measurements
(
2022
)
Maximum Flow Routing Strategy with Dynamic Link Allocation for Space Information Networks under Transceiver Constraints
(
2022
)
Non-Convex Exact Community Recovery in Stochastic Block Model
(
2022
)
On the Finite-Time Complexity and Practical Computation of Approximate Stationarity Concepts of Lipschitz Functions
(
2022
)
Practical Schemes for Finding Near-Stationary Points of Convex Finite-Sums
(
2022
)
Probabilistic Simplex Component Analysis
(
2022
)
SISAL Revisited
(
2022
)
An Efficient Alternating Direction Method for Graph Learning from Smooth Signals
(
2021
)
A Newton Tracking Algorithm with Exact Linear Convergence for Decentralized Consensus Optimization
(
2021
)
A Newton Tracking Algorithm with Exact Linear Convergence Rate for Decentralized Consensus Optimization
(
2021
)
A Penalty Alternating Direction Method of Multipliers for Convex Composite Optimization Over Decentralized Networks
(
2021
)
A Theoretical Analysis of the Repetition Problem in Text Generation
(
2021
)
Dynamic Regret Bound for Moving Target Tracking Based on Online Time-of-Arrival Measurements
(
2021
)
Low-Cost Lipschitz-Independent Adaptive Importance Sampling of Stochastic Gradients
(
2021
)
Manifold Proximal Point Algorithms for Dual Principal Component Pursuit and Orthogonal Dictionary Learning
(
2021
)
Optimal Non-Convex Exact Recovery in Stochastic Block Model via Projected Power Method
(
2021
)
Sparse High-Order Portfolios via Proximal DCA and SCA
(
2021
)
Technical Elements of Machine Learning for Intellectual Property Law
(
2021
)
Voting-Based Multiagent Reinforcement Learning for Intelligent IoT
(
2021
)
Weakly Convex Optimization over Stiefel Manifold Using Riemannian Subgradient-Type Methods
(
2021
)
A Fast Proximal Point Algorithm for Generalized Graph Laplacian Learning
(
2020
)
A Nearly-Linear Time Algorithm for Exact Community Recovery in Stochastic Block Model
(
2020
)
An Efficient Augmented Lagrangian-Based Method for Linear Equality-Constrained Lasso
(
2020
)
An Integrated Approach of Machine Learning and Systems Thinking for Waiting Time Prediction in an Emergency Department
(
2020
)
A Penalty Alternating Direction Method of Multipliers for Decentralized Composite Optimization
(
2020
)
A Provably Convergent Projected Gradient-Type Algorithm for TDOA-Based Geolocation under the Quasi-Parabolic Ionosphere Model
(
2020
)
Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst Case Rates
(
2020
)
Most Relevant Area
Operations research and optimization
(
Systems engineering
)
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Last updated on 2024-30-10 at 09:24
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