Zoubin Ghahramani
Zoubin Ghahramani is Chief Scientist of Uber and a world leader in the field of machine learning, significantly advancing the state-of-the-art in algorithms that can learn from data. He is known in particular for fundamental contributions to probabilistic modeling and Bayesian approaches to machine learning systems and AI. Zoubin also maintains his roles as Professor of Information Engineering at the University of Cambridge and Deputy Director of the Leverhulme Centre for the Future of Intelligence. He was one of the founding directors of the Alan Turing Institute (the UK's national institute for Data Science and AI), and is a Fellow of St John's College Cambridge and of the Royal Society.
Engineering Blog Articles
First Uber Science Symposium: Discussing the Next Generation of RL, NLP, ConvAI, and DL
The Uber Science Symposium featured talks from members of the broader scientific community about the the latest innovations in RL, NLP, and other fields.
Announcing the 2019 Uber AI Residency
The Uber AI Residency is a 12-month training program for academics and professionals interested in becoming an AI researcher with Uber AI Labs or Uber ATG.
Introducing the Uber AI Residency
Interested in accelerating your career by tackling some of Uber’s most challenging AI problems? Apply for the Uber AI Residency, a research fellowship dedicated to fostering the next generation of AI talent.
Welcoming Peter Dayan to Uber AI Labs
Arriving now: Uber's Chief Scientist Zoubin Ghahramani introduces Uber AI Labs' newest team member, award-winning neuroscientist Peter Dayan.
Research Papers
Variational Gaussian Dropout is not Bayesian
J. Hron, A. Matthews, Z. Ghahramani
Gaussian multiplicative noise is commonly used as a stochastic regularisation technique in training of deterministic neural networks. A recent paper reinterpreted the technique as a specific algorithm for approximate inference in Bayesian neural networks; several extensions ensued. [...] [PDF]
Advances in Neural Information Processing Systems (NIPS), 2017
Gaussian multiplicative noise is commonly used as a stochastic regularisation technique in training of deterministic neural networks. A recent paper reinterpreted the technique as a specific algorithm for approximate inference in Bayesian neural networks; several extensions ensued. [...] [PDF]
Advances in Neural Information Processing Systems (NIPS), 2017
Lost Relatives of the Gumbel Trick
M. Balog, N. Tripuraneni, Z. Ghahramani, A. Weller
The Gumbel trick is a method to sample from a discrete probability distribution, or to estimate its normalizing partition function. The method relies on repeatedly applying a random perturbation to the distribution in a particular way, each time solving for the most likely configuration. [...] [PDF]
International Conference on Machine Learning (ICML), 2017
The Gumbel trick is a method to sample from a discrete probability distribution, or to estimate its normalizing partition function. The method relies on repeatedly applying a random perturbation to the distribution in a particular way, each time solving for the most likely configuration. [...] [PDF]
International Conference on Machine Learning (ICML), 2017
A birth-death process for feature allocation
K. Palla, D. Knowles, Z. Ghahramani
We propose a Bayesian nonparametric prior over feature allocations for sequential data, the birthdeath feature allocation process (BDFP). The BDFP models the evolution of the feature allocation of a set of N objects across a covariate (e.g. time) by creating and deleting features. [...] [PDF]
International Conference on Machine Learning (ICML), 2017
We propose a Bayesian nonparametric prior over feature allocations for sequential data, the birthdeath feature allocation process (BDFP). The BDFP models the evolution of the feature allocation of a set of N objects across a covariate (e.g. time) by creating and deleting features. [...] [PDF]
International Conference on Machine Learning (ICML), 2017
Automatic Discovery of the Statistical Types of Variables in a Dataset
I. Valera, Z. Ghahramani
A common practice in statistics and machine learning is to assume that the statistical data types (e.g., ordinal, categorical or real-valued) of variables, and usually also the likelihood model, is known. However, as the availability of real-world data increases, this assumption becomes too restrictive. [...] [PDF]
International Conference on Machine Learning (ICML), 2017
A common practice in statistics and machine learning is to assume that the statistical data types (e.g., ordinal, categorical or real-valued) of variables, and usually also the likelihood model, is known. However, as the availability of real-world data increases, this assumption becomes too restrictive. [...] [PDF]
International Conference on Machine Learning (ICML), 2017
General Latent Feature Modeling for Data Exploration Tasks
I. Valera, M. Pradier, Z. Ghahramani
This paper introduces a general Bayesian non- parametric latent feature model suitable to per- form automatic exploratory analysis of heterogeneous datasets, where the attributes describing each object can be either discrete, continuous or mixed variables. The proposed model presents several important properties. [...] [PDF]
ICML Workshop on Human Interpretability in Machine Learning (ICML), 2017
This paper introduces a general Bayesian non- parametric latent feature model suitable to per- form automatic exploratory analysis of heterogeneous datasets, where the attributes describing each object can be either discrete, continuous or mixed variables. The proposed model presents several important properties. [...] [PDF]
ICML Workshop on Human Interpretability in Machine Learning (ICML), 2017
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning
S. Gu, T. Lillicrap, R. Turner, Z. Ghahramani, B. Schölkopf, S. Levine
Off-policy model-free deep reinforcement learning methods using previously collected data can improve sample efficiency over on-policy policy gradient techniques. On the other hand, on-policy algorithms are often more stable and easier to use. [...] [PDF]
Supplemental: [LINK]
Advances in Neural Information Processing Systems (NIPS), 2017
Off-policy model-free deep reinforcement learning methods using previously collected data can improve sample efficiency over on-policy policy gradient techniques. On the other hand, on-policy algorithms are often more stable and easier to use. [...] [PDF]
Supplemental: [LINK]
Advances in Neural Information Processing Systems (NIPS), 2017
Bayesian inference on random simple graphs with power law degree distributions
J. Lee, C. Heaukulani, Z. Ghahramani, L. James, S. Choi
We present a model for random simple graphs with a degree distribution that obeys a power law (i.e., is heavy-tailed). To attain this behavior, the edge probabilities in the graph are constructed from Bertoin-Fujita-Roynette-Yor (BFRY) random variables, which have been recently utilized in Bayesian statistics for the construction of power law models in several applications. [...] [PDF]
International Conference on Machine Learning (ICML), 2017
We present a model for random simple graphs with a degree distribution that obeys a power law (i.e., is heavy-tailed). To attain this behavior, the edge probabilities in the graph are constructed from Bertoin-Fujita-Roynette-Yor (BFRY) random variables, which have been recently utilized in Bayesian statistics for the construction of power law models in several applications. [...] [PDF]
International Conference on Machine Learning (ICML), 2017
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic
S. Gu, T. Lillicrap, Z. Ghahramani, R. Turner, S. Levine
Model-free deep reinforcement learning (RL) methods have been successful in a wide variety of simulated domains. However, a major obstacle facing deep RL in the real world is their high sample complexity. [...] [PDF]
International Conference on Learning Representations (ICLR), 2017
Model-free deep reinforcement learning (RL) methods have been successful in a wide variety of simulated domains. However, a major obstacle facing deep RL in the real world is their high sample complexity. [...] [PDF]
International Conference on Learning Representations (ICLR), 2017
Magnetic Hamiltonian Monte Carlo
N. Tripuraneni, M. Rowland, Z. Ghahramani, R. Turner
Hamiltonian Monte Carlo (HMC) exploits Hamiltonian dynamics to construct efficient proposals for Markov chain Monte Carlo (MCMC). In this paper, we present a generalization of HMC which exploits \textit{non-canonical} Hamiltonian dynamics. [...] [PDF]
International Conference on Machine Learning (ICML), 2017
Hamiltonian Monte Carlo (HMC) exploits Hamiltonian dynamics to construct efficient proposals for Markov chain Monte Carlo (MCMC). In this paper, we present a generalization of HMC which exploits \textit{non-canonical} Hamiltonian dynamics. [...] [PDF]
International Conference on Machine Learning (ICML), 2017
