Raed AL Kontar
7 papers · 2020–2025 · 4 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
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🌍 Conference Polyglot · Bronze 🏃 Academic Marathon · Sprint 🌉 Interdisciplinary Bridge · Silver 🗺️ Taxonomy Completionist (26) 🏆 Keyword Champion (2)
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The Questioner
Venues
Top co-authors
Research topics
Machine Learning
(7)
Mathematics & Optimization
(5)
Optimization
(5)
Artificial Intelligence
(4)
Core Methods
(4)
Optimization & Theory
(4)
Bayesian & Probabilistic · Artificial Intelligence
(3)
Learning Paradigms · Machine Learning
(2)
Learning Types
(2)
Computer Vision
(1)
Healthcare & Medicine
(1)
Analysis
(1)
Bayesian & Probabilistic · Machine Learning
(1)
Clinical
(1)
Learning Paradigms · Artificial Intelligence
(1)
Keywords
hyperparameter estimation
(2)
gaussian process
(2)
kernel methods
(2)
feature extraction
(2)
federated learning
(2)
transfer learning
(1)
anomaly detection
(1)
principal component analysis
(1)
matrix factorization
(1)
video segmentation
(1)
sparse representation
(1)
hyperparameter optimization
(1)
convergence analysis
(1)
distributed optimization
(1)
dynamic programming
(1)
alternating minimization
(1)
bayesian optimization
(1)
multi-armed bandit
(1)
convergence guarantee
(1)
heterogeneous data
(1)
Papers
FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning
AAAI 2025
Personalized PCA: Decoupling Shared and Unique Features
JMLR 2024
Triple Component Matrix Factorization: Untangling Global, Local, and Noisy Components
JMLR 2024
Personalized Dictionary Learning for Heterogeneous Datasets
NeurIPS 2023
Gaussian Process Parameter Estimation Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits
JMLR 2022
Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes
NeurIPS 2020
Why Non-myopic Bayesian Optimization is Promising and How Far Should We Look-ahead? A Study via Rollout
AISTATS 2020