Dinh Phung - Main

Recent News

Paper Neural Topic Model via Optimal Transport has been accepted to ICLR 2021
Paper Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness has been accepted to AAAI 2021
Paper OTLDA: A Geometry-aware Optimal Transport Approach for Topic Modeling has been accepted to NeurIPS 2020
Paper Effective Unsupervised Domain Adaptation with Adversarially Trained Language Models has been accepted to EMNLP 2020
Paper Parameterized Rate-Distortion Stochastic Encoder has been accepted to ICML 2020
Paper Improving Adversarial Robustness by EnforcingLocal and Global Compactness has been accepted to ECCV 2020
Paper On Efficient Multilevel Clustering via Wasserstein Distances has been accepted to JMLR (with minor revision)
Congratulations to PhD student, Quan Hoang, on winning the ACEMS Business Analytics Prize for 2020 from the Australian Centre of Excellence in Mathematical and Statistical Frontiers on his work of using rate-distortion theory for robust machine learning.
Congratulations to Dr Trung Le, who has recently taken up a new position as Lecturer (Assistant Professor) in the Department in Data Science and AI, Monash University, Australia.
Paper A Relational Memory-based Embedding Model for Triple Classification and Search Personalization has been accepted to ACL 2020
Paper Deep Generative Models of Sparse and Overdispersed Discrete Data has been accepted to AISTATS 2020
Paper Three-Player Wasserstein GAN via Amortised Duality has been accepted to IJCAI 2019
Paper Learning How to Active Learn by Dreaming has been accepted to ACL 2019
Paper Learning Generative Adversarial Networks from Multiple Data Sources has been accepted to IJCAI 2019
Paper A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization has been accepted to NAACL 2019
Paper Robust Anomaly Detection in Videos using Multilevel Representations has been accepted to AAAI 2019
Paper Probabilistic Multilevel Clustering via Composite Transportation Distance has been accepted to AISTATS 2019
Paper Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection has been accepted to ICLR 2019
Paper Robust Bayesian Kernel Machine via Stein Variational Gradient Descent for Big Data has been accepted to KDD 2018
Paper MGAN: Training Generative Adversarial Nets with Multiple Generators has been accepted to ICLR 2018
Paper Learning Graph Representation via Frequent Subgraphs has been accepted to SDM 2018
Paper GEN: Geometric Enclosing Networks has been accepted to IJCAI 2018

Older News

Paper Dual Discriminator Generative Adversarial Nets has been accepted to NIPS 2017
Paper Supervised Restricted Boltzmann Machines has been accepted to UAI 2017
Paper Approximation Vector Machines for Large-Scale Online Learning has been accepted to the Journal of Machine Learning Research (JMLR 2017)
Paper Multilevel Clustering via Wasserstein Means has been accepted to ICML 2017
Our PhD student, Hung Vu, won the Best Application Paper Award (Energy-Based Localized Anomaly Detection in Video Surveillance, PAKDD 2017)
Paper Discriminative Bayesian Nonparametric Clustering has been accepted to IJCAI 2017
Paper Large-scale Online Kernel Learning with Random Feature Reparameterization has been accepted to IJCAI 2017
Paper GoGP: Fast Online Regression with Gaussian Processes has been accepted to ICDM 2017
Paper Prediction of Population Health Indices from Social Media using Kernel-based Textual and Temporal Features has been accepted to WWW 2017
Paper Hierarchical semi-Markov conditional random fields for deep recursive sequential data has been accepted to AIJ 2017 (Artificial Intelligence journal).
- See my thesis (chapter 5) for an equivalent directed graphical model, which is the precusor of this work and where I had described the Assymetric Inside-Outside (AIO) algorithm in great detail. A brief version of this for directed case has also appeared in this AAAI'04's paper. The idea of semi-Markov duration modelling has also been addressed for directed case in these CVPR05 and AIJ09 papers.
Paper Column Networks for Collective Classification has been accepted to AAAI 2017.
Paper Dual Space Gradient Descent for Online Learning has been accepted to NIPS 2016.
Paper Scalable Baysian Nonparametric Multilevel Clustering has been accepted to UAI 2016.
Paper Budgeted Semi-supervised Support Vecitor Machine has been accepted to UAI 2016.
Paper Nonparametric Budgeted Stochastic Gradient Descent has been accepted to AISTATS 2016.
Paper One-pass Logistic Regression for Label-drift and Large-scale Classification on Distributed Systems has been accepted to ICDM 2016.
The Special Issue on Machine Learning journal for selected papers from ACML 2014 is now available here.
I was awarded the Australian Research Council (ARC) Discovery Grant entitled Bayesian Nonparametric Machine Learning for Modern Data Analytics to commence from 2016 (over 3 years, $410k).
Paper Tensor-variate Restricted Boltzmann Machines has been accepted to AAAI 2015.
Paper Collaborating Differently on Different Topics: A Multi-Relational Approach to Multi-Task Learning won the Best Paper Award at PAKDD 2015.
Our student's paper Stabilizing Sparse Cox Model using Statistic and Semantic Structures in Electronic Medical Records won the Best Student Paper Award Runner-Up at PAKDD 2015.
Paper Streaming Variational Inference for Dirichlet Processes has been accepted to ACML 2015.
Paper Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts has been accepted to ICML 2014.
I was awarded the Australian Research Council (ARC) Discovery Grant entitled Stay Well: Analyzing Lifestyle Data from Smart Monitoring Devices to commence from 2015 (over 3 years, $384k). This research proposal aims to develop theoretical machine learning foundations for analyzing data and signals collected from medical wearable devices.
I will be Program Chairs (with Hang Li) for the Asian Conference on Machine Learning (ACML), 2014.


Dinh Phung, PhD Professor (Machine Learning) Monash University, Australia E:dinh.phung@monash.edu T:+61 3 9905 6955 Home News Research Projects/Grants Supervision Tutorials & Talks Data & Codes Research Lab Publications Preprints Selected Papers By Years By Topics All Papers Teaching Contact Disclaimer: This page, its content and style, are the responsibility of the author and do not necessarily represent the views, policies or opinions of Monash University.	Recent News Paper Neural Topic Model via Optimal Transport has been accepted to ICLR 2021 Paper Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness has been accepted to AAAI 2021 Paper OTLDA: A Geometry-aware Optimal Transport Approach for Topic Modeling has been accepted to NeurIPS 2020 Paper Effective Unsupervised Domain Adaptation with Adversarially Trained Language Models has been accepted to EMNLP 2020 Paper Parameterized Rate-Distortion Stochastic Encoder has been accepted to ICML 2020 Paper Improving Adversarial Robustness by EnforcingLocal and Global Compactness has been accepted to ECCV 2020 Paper On Efficient Multilevel Clustering via Wasserstein Distances has been accepted to JMLR (with minor revision) Congratulations to PhD student, Quan Hoang, on winning the ACEMS Business Analytics Prize for 2020 from the Australian Centre of Excellence in Mathematical and Statistical Frontiers on his work of using rate-distortion theory for robust machine learning. Congratulations to Dr Trung Le, who has recently taken up a new position as Lecturer (Assistant Professor) in the Department in Data Science and AI, Monash University, Australia. Paper A Relational Memory-based Embedding Model for Triple Classification and Search Personalization has been accepted to ACL 2020 Paper Deep Generative Models of Sparse and Overdispersed Discrete Data has been accepted to AISTATS 2020 Paper Three-Player Wasserstein GAN via Amortised Duality has been accepted to IJCAI 2019 Paper Learning How to Active Learn by Dreaming has been accepted to ACL 2019 Paper Learning Generative Adversarial Networks from Multiple Data Sources has been accepted to IJCAI 2019 Paper A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization has been accepted to NAACL 2019 Paper Robust Anomaly Detection in Videos using Multilevel Representations has been accepted to AAAI 2019 Paper Probabilistic Multilevel Clustering via Composite Transportation Distance has been accepted to AISTATS 2019 Paper Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection has been accepted to ICLR 2019 Paper Robust Bayesian Kernel Machine via Stein Variational Gradient Descent for Big Data has been accepted to KDD 2018 Paper MGAN: Training Generative Adversarial Nets with Multiple Generators has been accepted to ICLR 2018 Paper Learning Graph Representation via Frequent Subgraphs has been accepted to SDM 2018 Paper GEN: Geometric Enclosing Networks has been accepted to IJCAI 2018 Older News Paper Dual Discriminator Generative Adversarial Nets has been accepted to NIPS 2017 Paper Supervised Restricted Boltzmann Machines has been accepted to UAI 2017 Paper Approximation Vector Machines for Large-Scale Online Learning has been accepted to the Journal of Machine Learning Research (JMLR 2017) Paper Multilevel Clustering via Wasserstein Means has been accepted to ICML 2017 Our PhD student, Hung Vu, won the Best Application Paper Award (Energy-Based Localized Anomaly Detection in Video Surveillance, PAKDD 2017) Paper Discriminative Bayesian Nonparametric Clustering has been accepted to IJCAI 2017 Paper Large-scale Online Kernel Learning with Random Feature Reparameterization has been accepted to IJCAI 2017 Paper GoGP: Fast Online Regression with Gaussian Processes has been accepted to ICDM 2017 Paper Prediction of Population Health Indices from Social Media using Kernel-based Textual and Temporal Features has been accepted to WWW 2017 Paper Hierarchical semi-Markov conditional random fields for deep recursive sequential data has been accepted to AIJ 2017 (Artificial Intelligence journal). See my thesis (chapter 5) for an equivalent directed graphical model, which is the precusor of this work and where I had described the Assymetric Inside-Outside (AIO) algorithm in great detail. A brief version of this for directed case has also appeared in this AAAI'04's paper. The idea of semi-Markov duration modelling has also been addressed for directed case in these CVPR05 and AIJ09 papers. Paper Column Networks for Collective Classification has been accepted to AAAI 2017. Paper Dual Space Gradient Descent for Online Learning has been accepted to NIPS 2016. Paper Scalable Baysian Nonparametric Multilevel Clustering has been accepted to UAI 2016. Paper Budgeted Semi-supervised Support Vecitor Machine has been accepted to UAI 2016. Paper Nonparametric Budgeted Stochastic Gradient Descent has been accepted to AISTATS 2016. Paper One-pass Logistic Regression for Label-drift and Large-scale Classification on Distributed Systems has been accepted to ICDM 2016. The Special Issue on Machine Learning journal for selected papers from ACML 2014 is now available here. I was awarded the Australian Research Council (ARC) Discovery Grant entitled Bayesian Nonparametric Machine Learning for Modern Data Analytics to commence from 2016 (over 3 years, $410k). Paper Tensor-variate Restricted Boltzmann Machines has been accepted to AAAI 2015. Paper Collaborating Differently on Different Topics: A Multi-Relational Approach to Multi-Task Learning won the Best Paper Award at PAKDD 2015. Our student's paper Stabilizing Sparse Cox Model using Statistic and Semantic Structures in Electronic Medical Records won the Best Student Paper Award Runner-Up at PAKDD 2015. Paper Streaming Variational Inference for Dirichlet Processes has been accepted to ACML 2015. Paper Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts has been accepted to ICML 2014. I was awarded the Australian Research Council (ARC) Discovery Grant entitled Stay Well: Analyzing Lifestyle Data from Smart Monitoring Devices to commence from 2015 (over 3 years, $384k). This research proposal aims to develop theoretical machine learning foundations for analyzing data and signals collected from medical wearable devices. I will be Program Chairs (with Hang Li) for the Asian Conference on Machine Learning (ACML), 2014.