Christopher Yau
14 papers · 2014–2026 · 5 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (34) 🏃 Academic Marathon · Half 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Bronze 🐣 Hot Topic Early Bird
🏆
Keyword Champion
(2)
🧬
Topic Evolution
📈
Trend Setter
🔥
Unstoppable · Bronze
🌈
Renaissance Researcher
(5)
💎
Century Club · Bronze
🗃️
Keyword Collector
(64)
Top co-authors
Research topics
Machine Learning
(10)
Core Methods
(8)
Optimization & Theory
(6)
Artificial Intelligence
(5)
Deep Learning
(5)
Models
(5)
Bayesian & Probabilistic · Machine Learning
(4)
Bayesian & Probabilistic · Artificial Intelligence
(4)
Learning Types
(3)
Data Science & Analytics
(2)
Healthcare & Medicine
(2)
Mathematics & Optimization
(2)
Applications
(2)
Architectures
(2)
Core AI
(2)
Keywords
variational autoencoder
(5)
bayesian inference
(4)
dimensionality reduction
(4)
feature clustering
(2)
gibbs sampler
(2)
posterior inference
(2)
latent variable
(2)
latent variable model
(2)
factorial hidden markov model
(2)
latent representation
(2)
gibbs sampling
(2)
gaussian process
(1)
low-rank approximation
(1)
diffusion process
(1)
variational inference
(1)
u-net architecture
(1)
survival analysis
(1)
markov chain monte carlo
(1)
probabilistic modeling
(1)
mcmc sampling
(1)
Papers
Hybrid Restricted Master Problem for Boolean Matrix Factorisation
AAAI 2026
Mixed Type Multimorbidity Variational Autoencoder: A Deep Generative Model for Multimorbidity Analysis
MLHC 2024
A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs
NeurIPS 2022
Derivative-Based Neural Modelling of Cumulative Distribution Functions for Survival Analysis
AISTATS 2022
BasisDeVAE: Interpretable Simultaneous Dimensionality Reduction and Feature-Level Clustering with Derivative-Based Variational Autoencoders
ICML 2021
Multi-Facet Clustering Variational Autoencoders
NeurIPS 2021
BasisVAE: Translation-invariant feature-level clustering with Variational Autoencoders
AISTATS 2020
Neural Decomposition: Functional ANOVA with Variational Autoencoders
AISTATS 2020
Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models
AISTATS 2019
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models
ICML 2019
Probabilistic Boolean Tensor Decomposition
ICML 2018
Bayesian Boolean Matrix Factorisation
ICML 2017
Testing and Learning on Distributions with Symmetric Noise Invariance
NeurIPS 2017
Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models
NeurIPS 2014