Hoang Phuc Hau Luu

21 papers A* 2A 3B 1Journal 11Unranked 3
YearRankTypeTitle / Venue / Authors
2026 J jnl
DC-LA: Difference-of-Convex Langevin Algorithm.
CoRR
Hoang Phuc Hau Luu, Zhongjian Wang
2025 A conf
GRADSTOP: Early Stopping of Gradient Descent via Posterior Sampling.
ECAI
Arash Jamshidi, Lauri Seppäläinen, Katsiaryna Haitsiukevich, Hoang Phuc Hau Luu, Anton Björklund, Kai Puolamäki
2025 J jnl
GRADSTOP: Early Stopping of Gradient Descent via Posterior Sampling.
CoRR
Arash Jamshidi, Lauri Seppäläinen, Katsiaryna Haitsiukevich, Hoang Phuc Hau Luu, Anton Björklund, Kai Puolamäki
2025 A conf
Geodesic Slice Sampler for Multimodal Distributions with Strong Curvature.
UAI
Bernardo Williams, Hanlin Yu, Hoang Phuc Hau Luu, Georgios Arvanitidis, Arto Klami
2025 J jnl
Geodesic Slice Sampler for Multimodal Distributions with Strong Curvature.
CoRR
Bernardo Williams, Hanlin Yu, Hoang Phuc Hau Luu, Georgios Arvanitidis, Arto Klami
2025 A* conf
Stochastic variance-reduced Gaussian variational inference on the Bures-Wasserstein manifold.
ICLR
Hoang Phuc Hau Luu, Hanlin Yu, Bernardo Williams, Marcelo Hartmann, Arto Klami
2024 A conf
Error bounds for any regression model using Gaussian processes with gradient information.
AISTATS
Rafael Savvides, Hoang Phuc Hau Luu, Kai Puolamäki
2024 — conf
Fast and Understandable Nonlinear Supervised Dimensionality Reduction.
DS (1)
Anri Patron, Rafael Savvides, Lauri Franzon, Hoang Phuc Hau Luu, Kai Puolamäki
2024 J jnl
Gradient Boosting Mapping for Dimensionality Reduction and Feature Extraction.
CoRR
Anri Patron, Ayush Prasad, Hoang Phuc Hau Luu, Kai Puolamäki
2024 J jnl
Markov chain stochastic DCA and applications in deep learning with PDEs regularization.
Neural Networks
Hoang Phuc Hau Luu, Hoai Minh Le, Hoai An Le Thi
2024 A* conf
Non-geodesically-convex optimization in the Wasserstein space.
NeurIPS
Hoang Phuc Hau Luu, Hanlin Yu, Bernardo Williams, Petrus Mikkola, Marcelo Hartmann, Kai Puolamäki, Arto Klami
2024 J jnl
Non-geodesically-convex optimization in the Wasserstein space.
CoRR
Hoang Phuc Hau Luu, Hanlin Yu, Bernardo Williams, Petrus Mikkola, Marcelo Hartmann, Kai Puolamäki, Arto Klami
2024 J jnl
Online Stochastic DCA With Applications to Principal Component Analysis.
IEEE Trans. Neural Networks Learn. Syst.
Hoai An Le Thi, Hoang Phuc Hau Luu, Tao Pham Dinh
2024 J jnl
Stochastic variance-reduced Gaussian variational inference on the Bures-Wasserstein manifold.
CoRR
Hoang Phuc Hau Luu, Hanlin Yu, Bernardo Williams, Marcelo Hartmann, Arto Klami
2023 — conf
DCA-Based Weighted Bagging: A New Ensemble Learning Approach.
ACIIDS (2)
Van Tuan Pham, Hoai An Le Thi, Hoang Phuc Hau Luu, Pascal Damel
2022 B conf
A Block Coordinate DCA Approach for Large-Scale Kernel SVM.
ICCCI
Van Tuan Pham, Hoang Phuc Hau Luu, Hoai An Le Thi
2022 J jnl
A DC programming approach for solving a centralized group key management problem.
J. Comb. Optim.
Hoai An Le Thi, Thi Tuyet Trinh Nguyen, Hoang Phuc Hau Luu
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 J jnl
Stochastic DCA with Variance Reduction and Applications in Machine Learning.
J. Mach. Learn. Res.
Hoai An Le Thi, Hoang Phuc Hau Luu, Hoai Minh Le, Tao Pham Dinh
2022 J jnl
Stochastic Difference-of-Convex-Functions Algorithms for Nonconvex Programming.
SIAM J. Optim.
Hoai An Le Thi, Ngai Van Huynh, Tao Pham Dinh, Hoang Phuc Hau Luu
2021 — conf
Solving a Centralized Dynamic Group Key Management Problem by an Optimization Approach.
MCO
Thi Tuyet Trinh Nguyen, Hoang Phuc Hau Luu, Hoai An Le Thi