← Authors

Dinh Phung

93 papers · 2008–2025 · 20 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (32) 🐝 Cross-Pollinator (11) 🧭 Keyword Pioneer πŸƒ Academic Marathon (17) 🌍 Conference Polyglot (20) 🌈 Renaissance Researcher (7) πŸŒ‰ Interdisciplinary Bridge 🐣 Hot Topic Early Bird πŸ”¬ Deep Specialist (13) πŸ‘‘ Triple Crown πŸ† Keyword Champion (4) πŸ† Grand Slam 🀝 Dynamic Duo (53) πŸ’Ž Century Club (93) ❓ The Questioner πŸš€ Conference Pioneer ⚑ Prolific Year (13) πŸ”₯ Unstoppable (15) πŸ—ƒοΈ Keyword Collector (91) πŸ“ˆ Trend Setter

Conferences

NIPS (13) ICML (12) ACML (9) ICLR (9) IJCAI (8) AISTATS (7) CVPR (7) ACL (6) EMNLP (4) WACV (2) UAI (2) NAACL (2) JMLR (2) ICCV (2) ECCV (2) AAAI (2) INTERSPEECH (1) MLHC (1) CONLL (1) COLING (1)

Top co-authors

Trung Le (53) He Zhao (17) Tu Dinh Nguyen (15) nhαΊ­t Hα»“ (10) Hung Bui (10) Svetha Venkatesh (9) Thuy-Trang Vu (9) Viet Huynh (8) Gholamreza Haffari (8) Jianfei Cai (8)

Keywords

optimal transport (13) domain adaptation (11) wasserstein distance (8) knowledge distillation (6) large language model (5) probability measure (5) bayesian nonparametric (5) question answering (5) image generation (5) domain generalization (5) neural network (4) neural machine translation (4) unsupervised learning (4) unsupervised domain adaptation (4) generative adversarial network (4) adversarial example (4) multilevel clustering (4) model compression (3) stochastic gradient descent (3) transfer learning (3)

Papers

PanSplat: 4K Panorama Synthesis with Feed-Forward Gaussian Splatting CVPR 2025
Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models ICML 2025
Planning for Success: Exploring LLM Long-term Planning Capabilities in Table Understanding ACL 2025
Fantastic Targets for Concept Erasure in Diffusion Models and Where To Find Them ICLR 2025
PaRa: Personalizing Text-to-Image Diffusion via Parameter Rank Reduction ICLR 2025
Improving Generalization with Flat Hilbert Bayesian Inference ICML 2025
Enhancing Dataset Distillation via Non-Critical Region Refinement CVPR 2025
Neural Topic Modeling with Large Language Models in the Loop ACL 2025
Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation CVPR 2025
Beyond Losses Reweighting: Empowering Multi-Task Learning via the Generalization Perspective ICCV 2025
Boosting Multiple Views for pretrained-based Continual Learning ICLR 2025
Planning for Success: Exploring LLM Long-term Planning Capabilities in Table Understanding CONLL 2025
NAYER: Noisy Layer Data Generation for Efficient and Effective Data-free Knowledge Distillation CVPR 2024
Taming Stable Diffusion for Text to 360 Panorama Image Generation CVPR 2024
Optimal Transport for Structure Learning Under Missing Data ICML 2024
Frequency Attention for Knowledge Distillation WACV 2024
Explicit Eigenvalue Regularization Improves Sharpness-Aware Minimization NIPS 2024
Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation NIPS 2024
Parameter Estimation in DAGs from Incomplete Data via Optimal Transport ICML 2024
Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs ACL 2024
Revisiting Deep Audio-Text Retrieval Through the Lens of Transportation ICLR 2024
MetaAug: Meta-Data Augmentation for Post-Training Quantization ECCV 2024
Text-Enhanced Data-free Approach for Federated Class-Incremental Learning CVPR 2024
An Additive Instance-Wise Approach to Multi-class Model Interpretation ICLR 2023
Flat Seeking Bayesian Neural Networks NIPS 2023
Systematic Assessment of Factual Knowledge in Large Language Models EMNLP 2023
Global-Local Regularization Via Distributional Robustness AISTATS 2023
Optimal Transport Model Distributional Robustness NIPS 2023
Vector Quantized Wasserstein Auto-Encoder ICML 2023
Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning NIPS 2023
Adversarial Local Distribution Regularization for Knowledge Distillation WACV 2023
On Global-view Based Defense via Adversarial Attack and Defense Risk Guaranteed Bounds AISTATS 2022
Stochastic Multiple Target Sampling Gradient Descent NIPS 2022
MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generation NIPS 2022
Domain Generalisation of NMT: Fusing Adapters with Leave-One-Domain-Out Training ACL 2022
Particle-based Adversarial Local Distribution Regularization AISTATS 2022
Sobolev Transport: A Scalable Metric for Probability Measures with Graph Metrics AISTATS 2022
Bridging Global Context Interactions for High-Fidelity Image Completion CVPR 2022
Can Domains Be Transferred across Languages in Multi-Domain Multilingual Neural Machine Translation? EMNLP 2022
A Unified Wasserstein Distributional Robustness Framework for Adversarial Training ICLR 2022
On Transportation of Mini-batches: A Hierarchical Approach ICML 2022
A Vietnamese-English Neural Machine Translation System INTERSPEECH 2022
Cycle class consistency with distributional optimal transport and knowledge distillation for unsupervised domain adaptation UAI 2022
Neural Topic Model via Optimal Transport ICLR 2021
Most: multi-source domain adaptation via optimal transport for student-teacher learning UAI 2021
LAMDA: Label Matching Deep Domain Adaptation ICML 2021
On efficient multilevel Clustering via Wasserstein distances JMLR 2021
Optimal Transport for Deep Generative Models: State of the Art and Research Challenges IJCAI 2021
Topic Modelling Meets Deep Neural Networks: A Survey IJCAI 2021
Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection EMNLP 2021
TIDOT: A Teacher Imitation Learning Approach for Domain Adaptation with Optimal Transport IJCAI 2021
Quaternion Graph Neural Networks ACML 2021
STEM: An Approach to Multi-Source Domain Adaptation With Guarantees ICCV 2021
Exploiting Domain-Specific Features to Enhance Domain Generalization NIPS 2021
On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources NIPS 2021
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness AAAI 2021
OTLDA: A Geometry-aware Optimal Transport Approach for Topic Modeling NIPS 2020
Improving Adversarial Robustness by Enforcing Local and Global Compactness ECCV 2020
Effective Unsupervised Domain Adaptation with Adversarially Trained Language Models EMNLP 2020
A Relational Memory-based Embedding Model for Triple Classification and Search Personalization ACL 2020
Parameterized Rate-Distortion Stochastic Encoder ICML 2020
Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering COLING 2020
Variational Autoencoders for Sparse and Overdispersed Discrete Data AISTATS 2020
Learning How to Active Learn by Dreaming ACL 2019
Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection ICLR 2019
Robust Anomaly Detection in Videos Using Multilevel Representations AAAI 2019
Three-Player Wasserstein GAN via Amortised Duality IJCAI 2019
Learning Generative Adversarial Networks from Multiple Data Sources IJCAI 2019
A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization NAACL 2019
Probabilistic Multilevel Clustering via Composite Transportation Distance AISTATS 2019
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network NAACL 2018
MGAN: Training Generative Adversarial Nets with Multiple Generators ICLR 2018
Batch Normalized Deep Boltzmann Machines ACML 2018
Geometric Enclosing Networks IJCAI 2018
Clustering Induced Kernel Learning ACML 2018
Large-scale Online Kernel Learning with Random Feature Reparameterization IJCAI 2017
Multilevel Clustering via Wasserstein Means ICML 2017
Discriminative Bayesian Nonparametric Clustering IJCAI 2017
Dual Discriminator Generative Adversarial Nets NIPS 2017
Approximation Vector Machines for Large-scale Online Learning JMLR 2017
Dual Space Gradient Descent for Online Learning NIPS 2016
Multiple Kernel Learning with Data Augmentation ACML 2016
Preterm Birth Prediction: Stable Selection of Interpretable Rules from High Dimensional Data MLHC 2016
Nonparametric Budgeted Stochastic Gradient Descent AISTATS 2016
Streaming Variational Inference for Dirichlet Process Mixtures ACML 2015
Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts ICML 2014
Factorial Multi-Task Learning : A Bayesian Nonparametric Approach ICML 2013
Learning Parts-based Representations with Nonnegative Restricted Boltzmann Machine ACML 2013
Thurstonian Boltzmann Machines: Learning from Multiple Inequalities ICML 2013
Cumulative Restricted Boltzmann Machines for Ordinal Matrix Data Analysis ACML 2012
Learning From Ordered Sets and Applications in Collaborative Ranking ACML 2012
Mixed-Variate Restricted Boltzmann Machines ACML 2011
Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data NIPS 2008