
Deep Neural Networks as Point Estimates for Deep Gaussian Processes
Deep Gaussian processes (DGPs) have struggled for relevance in applicati...
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Learning Continuous Treatment Policy and Bipartite Embeddings for Matching with Heterogeneous Causal Effects
Causal inference methods are widely applied in the fields of medicine, p...
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Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
Probabilistic circuits (PCs) are a promising avenue for probabilistic mo...
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DynamicPPL: Stanlike Speed for Dynamic Probabilistic Models
We present the preliminary highlevel design and features of DynamicPPL....
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ResourceEfficient Neural Networks for Embedded Systems
While machine learning is traditionally a resource intensive task, embed...
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Bayesian Learning of SumProduct Networks
Sumproduct networks (SPNs) are flexible density estimators and have rec...
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Efficient and Robust Machine Learning for RealWorld Systems
While machine learning is traditionally a resource intensive task, embed...
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Probabilistic MetaRepresentations Of Neural Networks
Existing Bayesian treatments of neural networks are typically characteri...
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Automatic Bayesian Density Analysis
Making sense of a dataset in an automatic and unsupervised fashion is a ...
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Handling Incomplete Heterogeneous Data using VAEs
Variational autoencoders (VAEs), as well as other generative models, hav...
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Variational Bayesian dropout: pitfalls and fixes
Dropout, a stochastic regularisation technique for training of neural ne...
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Antithetic and Monte Carlo kernel estimators for partial rankings
In the modern age, rankings data is ubiquitous and it is useful for a va...
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Probabilistic Deep Learning using Random SumProduct Networks
Probabilistic deep learning currently receives an increased interest, as...
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Variational Measure Preserving Flows
Probabilistic modelling is a general and elegant framework to capture th...
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Gaussian Process Behaviour in Wide Deep Neural Networks
Whilst deep neural networks have shown great empirical success, there is...
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The Mirage of ActionDependent Baselines in Reinforcement Learning
Policy gradient methods are a widely used class of modelfree reinforcem...
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Weakly supervised collective feature learning from curated media
The current stateoftheart in feature learning relies on the supervise...
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Imitation networks: Fewshot learning of neural networks from scratch
In this paper, we propose imitation networks, a simple but effective met...
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Denotational validation of higherorder Bayesian inference
We present a modular semantic account of Bayesian inference algorithms f...
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Variational Gaussian Dropout is not Bayesian
Gaussian multiplicative noise is commonly used as a stochastic regularis...
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General Latent Feature Modeling for Data Exploration Tasks
This paper introduces a general Bayesian non parametric latent feature ...
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Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes
We propose a simple method that combines neural networks and Gaussian pr...
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OneShot Learning in Discriminative Neural Networks
We consider the task of oneshot learning of visual categories. In this ...
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Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks
Deep neural networks (DNNs) have excellent representative power and are ...
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Lost Relatives of the Gumbel Trick
The Gumbel trick is a method to sample from a discrete probability distr...
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General Latent Feature Models for Heterogeneous Datasets
Latent feature modeling allows capturing the latent structure responsibl...
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Interpolated Policy Gradient: Merging OnPolicy and OffPolicy Gradient Estimation for Deep Reinforcement Learning
Offpolicy modelfree deep reinforcement learning methods using previous...
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Deep Bayesian Active Learning with Image Data
Even though active learning forms an important pillar of machine learnin...
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Bayesian inference on random simple graphs with power law degree distributions
We present a model for random simple graphs with a degree distribution t...
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GPflow: A Gaussian process library using TensorFlow
GPflow is a Gaussian process library that uses TensorFlow for its core c...
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A study of the effect of JPG compression on adversarial images
Neural network image classifiers are known to be vulnerable to adversari...
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Magnetic Hamiltonian Monte Carlo
Hamiltonian Monte Carlo (HMC) exploits Hamiltonian dynamics to construct...
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The Mondrian Kernel
We introduce the Mondrian kernel, a fast random feature approximation to...
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Distributed Flexible Nonlinear Tensor Factorization
Tensor factorization is a powerful tool to analyse multiway data. Compa...
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A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
Recurrent neural networks (RNNs) stand at the forefront of many recent d...
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A General Framework for Constrained Bayesian Optimization using Informationbased Search
We present an informationtheoretic framework for solving global blackb...
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Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions
We develop parallel predictive entropy search (PPES), a novel algorithm ...
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Sandwiching the marginal likelihood using bidirectional Monte Carlo
Computing the marginal likelihood (ML) of a model requires marginalizing...
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Dirichlet Fragmentation Processes
Tree structures are ubiquitous in data across many domains, and many dat...
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Scalable Discrete Sampling as a MultiArmed Bandit Problem
Drawing a sample from a discrete distribution is one of the building com...
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An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process
Stochastic variational inference (SVI) is emerging as the most promising...
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MCMC for Variationally Sparse Gaussian Processes
Gaussian process (GP) models form a core part of probabilistic machine l...
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Neural Adaptive Sequential Monte Carlo
Sequential Monte Carlo (SMC), or particle filtering, is a popular class ...
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Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference
Convolutional neural networks (CNNs) work well on large datasets. But la...
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Dropout as a Bayesian Approximation: Appendix
We show that a neural network with arbitrary depth and nonlinearities, ...
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Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Deep learning tools have gained tremendous attention in applied machine ...
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Training generative neural networks via Maximum Mean Discrepancy optimization
We consider training a deep neural network to generate samples from an u...
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A LinearTime Particle Gibbs Sampler for Infinite Hidden Markov Models
Infinite Hidden Markov Models (iHMM's) are an attractive, nonparametric ...
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On Sparse variational methods and the KullbackLeibler divergence between stochastic processes
The variational framework for learning inducing variables (Titsias, 2009...
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Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data
Multivariate categorical data occur in many applications of machine lear...
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Zoubin Ghahramani
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Zoubin Ghahramani FRS is a BritishIranian researcher at Cambridge University and Professor of Information Engineering. He is jointly appointed to University College London as well as to the Alan Turing Institute. And since 2009, he has been a fellow of St John’s College, Cambridge. From 2003 to 2012, he was Associate Professor of Research at the Carnegie Mellon School of Computing Science. He is also the Chief Scientist of Uber and the Vice Director of the Leverhulme Centre.
Ghahramani received a degree in cognitive science and computer science from the American school of Madrid in Spain and the University of Pennsylvania in 1990. He obtained his PhD from Michael I. Jordan and Tomaso Poggio’s Department of Brain and Cognitive Science at the Massachusetts Institute of Technology.
After his PhD, Ghahramani moved to the University of Toronto in 1995, working with Geoffrey Hinton, where he was an ITRC Postdoctoral Fellow at the Artificial Intelligence Lab. He was a faculty member at the Gatsby Computational Neuroscience Unit at University College London from 1998 to 2005.
In the field of Bayesian machine learning, Ghahramani made significant contributions as well as in graphical models and computer science. His current research focuses on Bayesian nonparametric modeling and statistical machine learning. He has also been working on artificial intelligence, information collection, bioinformatics and statistics, which are the basis for the management of uncertainty, decisionmaking and the design of learning systems. He has publicated more than 200 documents, receiving over 30,000 quotes. In 2014, he and Gary Marcus, Doug Bemis and Ken Stanley cofounded the Geometric Intelligence Company. In 2016, he moved to Uber’s A.I. Labs after Uber had acquired the startup. He became Chief Scientist just after four months, replacing Gary Marcus.
In 2015, Ghahramani was elected Royal Society Fellow. His election certificate reads:
Zoubin Ghahramani is a world leader in machine learning, which makes significant progress in algorithms that can learn from data. In particular, it is known for its fundamental contributions in probabilistic modeling and bayesian nonparametric approaches to machine learning systems and the development of approximate algorithms for scalable learning. He is a pioneer of SML methods, active learning algorithms and sparse Gaussian processes. His development of novel nonparametric dimensional models, such as the infinite latent model, was highly influential.