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Zoubin Ghahramani

87 papers · 2005–2024 · 9 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (34) 🐝 Cross-Pollinator (14) πŸƒ Academic Marathon (19) 🌈 Renaissance Researcher (7) 🌍 Conference Polyglot (9) πŸŒ‰ Interdisciplinary Bridge 🐣 Hot Topic Early Bird 🧭 Keyword Pioneer 🌟 Keyword Trendsetter Combo (6) 🏠 Conference Loyalist (28) πŸ‘‘ Domain Dominant (35) πŸ† Keyword Champion πŸ† Grand Slam πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (17) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (189) πŸš€ Conference Pioneer πŸ”₯ Unstoppable (17) πŸ’Ž Century Club (87) ⚑ Prolific Year (8)

Conferences

ICML (28) NIPS (28) AISTATS (15) JMLR (10) AAAI (2) CONLL (1) EMNLP (1) ICLR (1) UAI (1)

Top co-authors

JosΓ© Miguel HernΓ‘ndez-Lobato (8) Yarin Gal (5) Neil Houlsby (5) Isabel Valera (5) Robert Peharz (5) James Hensman (5) Hong Ge (4) Amar Shah (4) David Knowles (4) Antonio Vergari (3)

Keywords

bayesian inference (25) gaussian process (18) variational inference (15) bayesian nonparametrics (10) markov chain monte carlo (10) indian buffet process (9) bayesian nonparametric (8) bayesian optimization (7) nonparametric bayesian (7) active learning (6) graphical model (5) probabilistic modeling (5) predictive entropy search (4) black-box optimization (4) dimensionality reduction (4) probabilistic model (4) matrix factorization (4) latent feature model (4) partition function (3) parallel computing (3)

Papers

Resource-Efficient Neural Networks for Embedded Systems JMLR 2024
Pre-trained Gaussian Processes for Bayesian Optimization JMLR 2024
Neural Diffusion Processes ICML 2023
Deep Neural Networks as Point Estimates for Deep Gaussian Processes NIPS 2021
General Latent Feature Models for Heterogeneous Datasets JMLR 2020
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits ICML 2020
Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning UAI 2019
Bayesian Learning of Sum-Product Networks NIPS 2019
One-Network Adversarial Fairness AAAI 2019
Automatic Bayesian Density Analysis AAAI 2019
MetaGAN: An Adversarial Approach to Few-Shot Learning NIPS 2018
Gaussian Process Behaviour in Wide Deep Neural Networks ICLR 2018
The Mirage of Action-Dependent Baselines in Reinforcement Learning ICML 2018
Variational Bayesian dropout: pitfalls and fixes ICML 2018
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models ICML 2018
Turing: A Language for Flexible Probabilistic Inference AISTATS 2018
Deep Bayesian Active Learning with Image Data ICML 2017
Automatic Discovery of the Statistical Types of Variables in a Dataset ICML 2017
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning NIPS 2017
Magnetic Hamiltonian Monte Carlo ICML 2017
A Birth-Death Process for Feature Allocation ICML 2017
Bayesian inference on random simple graphs with power law degree distributions ICML 2017
Lost Relatives of the Gumbel Trick ICML 2017
GPflow: A Gaussian Process Library using TensorFlow JMLR 2017
Distributed Flexible Nonlinear Tensor Factorization NIPS 2016
Bayesian Generalised Ensemble Markov Chain Monte Carlo AISTATS 2016
A General Framework for Constrained Bayesian Optimization using Information-based Search JMLR 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
Pareto Frontier Learning with Expensive Correlated Objectives ICML 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning ICML 2016
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks NIPS 2016
Scalable Variational Gaussian Process Classification AISTATS 2015
Predictive Entropy Search for Bayesian Optimization with Unknown Constraints ICML 2015
Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions NIPS 2015
MCMC for Variationally Sparse Gaussian Processes NIPS 2015
Neural Adaptive Sequential Monte Carlo NIPS 2015
Statistical Model Criticism using Kernel Two Sample Tests NIPS 2015
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data ICML 2015
A Probabilistic Model for Dirty Multi-task Feature Selection ICML 2015
Linear Dimensionality Reduction: Survey, Insights, and Generalizations JMLR 2015
An Empirical Study of Stochastic Variational Inference Algorithms for the Beta Bernoulli Process ICML 2015
Distributed Inference for Dirichlet Process Mixture Models ICML 2015
Particle Gibbs for Infinite Hidden Markov Models NIPS 2015
Randomized Nonlinear Component Analysis ICML 2014
Gaussian Process Volatility Model NIPS 2014
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions NIPS 2014
General Table Completion using a Bayesian Nonparametric Model NIPS 2014
A Non-parametric Conditional Factor Regression Model for Multi-Dimensional Input and Response AISTATS 2014
Avoiding pathologies in very deep networks AISTATS 2014
Student-t Processes as Alternatives to Gaussian Processes AISTATS 2014
Pitfalls in the use of Parallel Inference for the Dirichlet Process ICML 2014
Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications ICML 2014
Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices ICML 2014
Cold-start Active Learning with Robust Ordinal Matrix Factorization ICML 2014
Probabilistic Matrix Factorization with Non-random Missing Data ICML 2014
Beta Diffusion Trees ICML 2014
A reversible infinite HMM using normalised random measures ICML 2014
Dynamic Probabilistic Models for Latent Feature Propagation in Social Networks ICML 2013
Active Learning for Interactive Visualization AISTATS 2013
Active Learning of Model Evidence Using Bayesian Quadrature NIPS 2012
Flexible Martingale Priors for Deep Hierarchies AISTATS 2012
A Nonparametric Bayesian Model for Multiple Clustering with Overlapping Feature Views AISTATS 2012
Bayesian Classifier Combination AISTATS 2012
Gaussian Processes for time-marked time-series data AISTATS 2012
Continuous Relaxations for Discrete Hamiltonian Monte Carlo NIPS 2012
A nonparametric variable clustering model NIPS 2012
Random function priors for exchangeable arrays with applications to graphs and relational data NIPS 2012
Collaborative Gaussian Processes for Preference Learning NIPS 2012
Testing a Bayesian Measure of Representativeness Using a Large Image Database NIPS 2011
Approximate inference for the loss-calibrated Bayesian AISTATS 2011
The Indian Buffet Process: An Introduction and Review JMLR 2011
Copula Processes NIPS 2010
(Invited Talk) Bayesian Hidden Markov Models and Extensions CONLL 2010
Dependent Indian Buffet Processes AISTATS 2010
Learning the Structure of Deep Sparse Graphical Models AISTATS 2010
Kronecker Graphs: An Approach to Modeling Networks JMLR 2010
Tree-Structured Stick Breaking for Hierarchical Data NIPS 2010
The infinite HMM for unsupervised PoS tagging EMNLP 2009
Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process NIPS 2009
The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models JMLR 2009
Bayesian Exponential Family PCA NIPS 2008
The Infinite Factorial Hidden Markov Model NIPS 2008
Hidden Common Cause Relations in Relational Learning NIPS 2007
Relational Learning with Gaussian Processes NIPS 2006
Modeling Dyadic Data with Binary Latent Factors NIPS 2006
Gaussian Processes for Ordinal Regression JMLR 2005