Zoubin Ghahramani
98 papers · 1999–2026 · 10 venues · across top CS/AI venues
This profile combines several different researchers. Authors are keyed by name, and this name's papers fall into 32 separate collaboration groups - the largest holds only 33% of them. Pick the group below that matches the researcher you mean. How a shared name is split.
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Which Zoubin Ghahramani?
4 groups of papers that share co-authors internally but none with each other - each most likely a different researcher. Pick one to see its papers.
with Isabel Valera, James Hensman, Yarin Gal
32 papers
· 2014–2024
· ICML, NeurIPS, JMLR
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Resource-Efficient Neural Networks for Embedded Systems
JMLR 2024
Neural Diffusion Processes
ICML 2023
Deep Neural Networks as Point Estimates for Deep Gaussian Processes
NeurIPS 2021
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
ICML 2020
General Latent Feature Models for Heterogeneous Datasets
JMLR 2020
Automatic Bayesian Density Analysis
AAAI 2019
Bayesian Learning of Sum-Product Networks
NeurIPS 2019
One-Network Adversarial Fairness
AAAI 2019
Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning
UAI 2019
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models
ICML 2018
Gaussian Process Behaviour in Wide Deep Neural Networks
ICLR 2018
The Mirage of Action-Dependent Baselines in Reinforcement Learning
ICML 2018
Turing: A Language for Flexible Probabilistic Inference
AISTATS 2018
Variational Bayesian dropout: pitfalls and fixes
ICML 2018
Automatic Discovery of the Statistical Types of Variables in a Dataset
ICML 2017
Deep Bayesian Active Learning with Image Data
ICML 2017
GPflow: A Gaussian Process Library using TensorFlow
JMLR 2017
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning
NeurIPS 2017
Lost Relatives of the Gumbel Trick
ICML 2017
Magnetic Hamiltonian Monte Carlo
ICML 2017
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
NeurIPS 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
ICML 2016
On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes
AISTATS 2016
Scalable Discrete Sampling as a Multi-Armed Bandit Problem
ICML 2016
Distributed Inference for Dirichlet Process Mixture Models
ICML 2015
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data
ICML 2015
MCMC for Variationally Sparse Gaussian Processes
NeurIPS 2015
Neural Adaptive Sequential Monte Carlo
NeurIPS 2015
Particle Gibbs for Infinite Hidden Markov Models
NeurIPS 2015
Scalable Variational Gaussian Process Classification
AISTATS 2015
General Table Completion using a Bayesian Nonparametric Model
NeurIPS 2014
Pitfalls in the use of Parallel Inference for the Dirichlet Process
ICML 2014
28 papers whose co-authors appear nowhere else under this name
Efficiently Reconstructing Dynamic Scenes One D4RT at a Time
CVPR 2026
Pre-trained Gaussian Processes for Bayesian Optimization
JMLR 2024
MetaGAN: An Adversarial Approach to Few-Shot Learning
NeurIPS 2018
Bayesian Generalised Ensemble Markov Chain Monte Carlo
AISTATS 2016
Distributed Flexible Nonlinear Tensor Factorization
NeurIPS 2016
Linear Dimensionality Reduction: Survey, Insights, and Generalizations
JMLR 2015
Statistical Model Criticism using Kernel Two Sample Tests
NeurIPS 2015
A Non-parametric Conditional Factor Regression Model for Multi-Dimensional Input and Response
AISTATS 2014
Randomized Nonlinear Component Analysis
ICML 2014
Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications
ICML 2014
A Nonparametric Bayesian Model for Multiple Clustering with Overlapping Feature Views
AISTATS 2012
Active Learning of Model Evidence Using Bayesian Quadrature
NeurIPS 2012
Bayesian Classifier Combination
AISTATS 2012
Continuous Relaxations for Discrete Hamiltonian Monte Carlo
NeurIPS 2012
Flexible Martingale Priors for Deep Hierarchies
AISTATS 2012
Gaussian Processes for time-marked time-series data
AISTATS 2012
(Invited Talk) Bayesian Hidden Markov Models and Extensions
CoNLL 2010
Copula Processes
NeurIPS 2010
Kronecker Graphs: An Approach to Modeling Networks
JMLR 2010
The infinite HMM for unsupervised PoS tagging
EMNLP 2009
The Infinite Factorial Hidden Markov Model
NeurIPS 2008
Modeling Dyadic Data with Binary Latent Factors
NeurIPS 2006
A kernel method for unsupervised structured network inference
AISTATS 2005
Choosing a Variable to Clamp
AISTATS 2005
The Block Diagonal Infinite Hidden Markov Model
AISTATS 2005
Tree-Based Inference for Dirichlet Process Mixtures
AISTATS 2005
Local and global sparse Gaussian process approximations
AISTATS 1999
Stick-breaking Construction for the Indian Buffet Process
AISTATS 1999