Hung Bui
26 papers · 2008–2022 · 9 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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πΊοΈ Taxonomy Completionist (47) π Conference Polyglot Β· Silver π Academic Marathon Β· Half π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
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(10)
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Prolific Year Β· Productive
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Century Club Β· Bronze
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Conference Pioneer
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Renaissance Researcher
(6)
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Top co-authors
Research topics
Machine Learning
(23)
Optimization & Theory
(16)
Core Methods
(14)
Deep Learning
(12)
Models
(10)
Artificial Intelligence
(7)
Bayesian & Probabilistic
(6)
Mathematics & Optimization
(5)
Learning Types
(5)
Optimization
(5)
Application Areas
(4)
Core AI
(4)
Architectures
(3)
Computer Vision
(2)
Reinforcement Learning
(2)
Keywords
optimal transport
(5)
variational inference
(4)
latent space
(4)
domain adaptation
(3)
optimal control
(3)
representation learning
(3)
locally-linear control
(3)
wasserstein distance
(3)
generative adversarial network
(3)
probability measure
(3)
dynamical system
(2)
transfer learning
(2)
variational autoencoder
(2)
hidden markov model
(2)
entropic regularization
(2)
image generation
(2)
sinkhorn algorithm
(2)
bayesian nonparametrics
(2)
gibbs sampling
(2)
computational complexity
(2)
Papers
On Transportation of Mini-batches: A Hierarchical Approach
ICML 2022
Distributional Sliced-Wasserstein and Applications to Generative Modeling
ICLR 2021
Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein
ICLR 2021
LAMDA: Label Matching Deep Domain Adaptation
ICML 2021
Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior
AAAI 2021
On efficient multilevel Clustering via Wasserstein distances
JMLR 2021
On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources
NeurIPS 2021
On Robust Optimal Transport: Computational Complexity and Barycenter Computation
NeurIPS 2021
Structured Dropout Variational Inference for Bayesian Neural Networks
NeurIPS 2021
Temporal Predictive Coding For Model-Based Planning In Latent Space
ICML 2021
Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems
ICML 2020
On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm
ICML 2020
Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control
ICLR 2020
Predictive Coding for Locally-Linear Control
ICML 2020
Vec2Face: Unveil Human Faces From Their Blackbox Features in Face Recognition
CVPR 2020
Learning Generative Adversarial Networks from Multiple Data Sources
IJCAI 2019
Three-Player Wasserstein GAN via Amortised Duality
IJCAI 2019
Training Variational Autoencoders with Buffered Stochastic Variational Inference
AISTATS 2019
A DIRT-T Approach to Unsupervised Domain Adaptation
ICLR 2018
Robust Locally-Linear Controllable Embedding
AISTATS 2018
The Context-Dependent Additive Recurrent Neural Net
NAACL 2018
Discriminative Bayesian Nonparametric Clustering
IJCAI 2017
Large-scale Online Kernel Learning with Random Feature Reparameterization
IJCAI 2017
Practical Linear Models for Large-Scale One-Class Collaborative Filtering
IJCAI 2016
Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts
ICML 2014
Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data
NeurIPS 2008