Solving a Centralized Dynamic Group Key Management Problem by an Optimization Approach. - Thi Tuyet Trinh Nguyen; Hoang Phuc Hau Luu; Hoai An Le Thi (MCO, 2021)

A Block Coordinate DCA Approach for Large-Scale Kernel SVM. - Van Tuan Pham; Hoang Phuc Hau Luu; Hoai An Le Thi (ICCCI, 2022)

A DC programming approach for solving a centralized group key management problem. - Hoai An Le Thi; Thi Tuyet Trinh Nguyen; Hoang Phuc Hau Luu (J. Comb. Optim., 2022)

Stochastic DCA with Variance Reduction and Applications in Machine Learning. - Hoai An Le Thi; Hoang Phuc Hau Luu; Hoai Minh Le; Tao Pham Dinh (J. Mach. Learn. Res., 2022)

Advanced machine learning techniques based on DCA and applications to predictive maintenance. (Techniques avancées d'apprentissage automatique basées sur DCA et applications à la maintenance prédictive). - Hoang Phuc Hau Luu (2022)

Stochastic Difference-of-Convex-Functions Algorithms for Nonconvex Programming. - Hoai An Le Thi; Ngai Van Huynh; Tao Pham Dinh; Hoang Phuc Hau Luu (SIAM J. Optim., 2022)

DCA-Based Weighted Bagging: A New Ensemble Learning Approach. - Van Tuan Pham; Hoai An Le Thi; Hoang Phuc Hau Luu; Pascal Damel (ACIIDS (2), 2023)

Error bounds for any regression model using Gaussian processes with gradient information. - Rafael Savvides; Hoang Phuc Hau Luu; Kai Puolamäki (AISTATS, 2024)

Fast and Understandable Nonlinear Supervised Dimensionality Reduction. - Anri Patron; Rafael Savvides; Lauri Franzon; Hoang Phuc Hau Luu; Kai Puolamäki (DS (1), 2024)

Non-geodesically-convex optimization in the Wasserstein space. - Hoang Phuc Hau Luu; Hanlin Yu; Bernardo Williams; Petrus Mikkola; Marcelo Hartmann; Kai Puolamäki; Arto Klami (NeurIPS, 2024)

Gradient Boosting Mapping for Dimensionality Reduction and Feature Extraction. - Anri Patron; Ayush Prasad; Hoang Phuc Hau Luu; Kai Puolamäki (CoRR, 2024)

Markov chain stochastic DCA and applications in deep learning with PDEs regularization. - Hoang Phuc Hau Luu; Hoai Minh Le; Hoai An Le Thi (Neural Networks, 2024)

Online Stochastic DCA With Applications to Principal Component Analysis. - Hoai An Le Thi; Hoang Phuc Hau Luu; Tao Pham Dinh (IEEE Trans. Neural Networks Learn. Syst., 2024)

Stochastic variance-reduced Gaussian variational inference on the Bures-Wasserstein manifold. - Hoang Phuc Hau Luu; Hanlin Yu; Bernardo Williams; Marcelo Hartmann; Arto Klami (ICLR, 2025)

Geodesic Slice Sampler for Multimodal Distributions with Strong Curvature. - Bernardo Williams; Hanlin Yu; Hoang Phuc Hau Luu; Georgios Arvanitidis; Arto Klami (UAI, 2025)

GRADSTOP: Early Stopping of Gradient Descent via Posterior Sampling. - Arash Jamshidi; Lauri Seppäläinen; Katsiaryna Haitsiukevich; Hoang Phuc Hau Luu; Anton Björklund; Kai Puolamäki (CoRR, 2025)