
Bayesian Optimization in AlphaGo
During the development of AlphaGo, its many hyperparameters were tuned ...
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LargeScale Visual Speech Recognition
This work presents a scalable solution to openvocabulary visual speech ...
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TaskRelevant Adversarial Imitation Learning
We show that a critical problem in adversarial imitation from highdimen...
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Metalearning of Sequential Strategies
In this report we review memorybased metalearning as a tool for buildi...
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Making Efficient Use of Demonstrations to Solve Hard Exploration Problems
This paper introduces R2D3, an agent that makes efficient use of demonst...
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Learning Compositional Neural Programs with Recursive Tree Search and Planning
We propose a novel reinforcement learning algorithm, AlphaNPI, that inco...
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Modular MetaLearning with Shrinkage
Most gradientbased approaches to metalearning do not explicitly accoun...
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OneShot HighFidelity Imitation: Training LargeScale Deep Nets with RL
Humans are experts at highfidelity imitation  closely mimicking a dem...
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Playing hard exploration games by watching YouTube
Deep reinforcement learning methods traditionally struggle with tasks wh...
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Sample Efficient Adaptive TexttoSpeech
We present a metalearning approach for adaptive texttospeech (TTS) wi...
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The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously
This paper introduces the Intentional Unintentional (IU) agent. This age...
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Learning to learn by gradient descent by gradient descent
The move from handdesigned features to learned features in machine lear...
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Neural ProgrammerInterpreters
We propose the neural programmerinterpreter (NPI): a recurrent and comp...
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ACDC: A Structured Efficient Linear Layer
The linear layer is one of the most pervasive modules in deep learning r...
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Programmable Agents
We build deep RL agents that execute declarative programs expressed in f...
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Learned Optimizers that Scale and Generalize
Learning to learn has emerged as an important direction for achieving ar...
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Learning to Learn without Gradient Descent by Gradient Descent
We learn recurrent neural network optimizers trained on simple synthetic...
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Learning to Communicate to Solve Riddles with Deep Distributed Recurrent QNetworks
We propose deep distributed recurrent Qnetworks (DDRQN), which enable t...
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Parallel Multiscale Autoregressive Density Estimation
PixelCNN achieves stateoftheart results in density estimation for nat...
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Learning to Perform Physics Experiments via Deep Reinforcement Learning
When encountering novel objects, humans are able to infer a wide range o...
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Unbounded Bayesian Optimization via Regularization
Bayesian optimization has recently emerged as a popular and efficient to...
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Proceedings of the TwentyEighth Conference on Uncertainty in Artificial Intelligence (2012)
This is the Proceedings of the TwentyEighth Conference on Uncertainty i...
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RaoBlackwellised Particle Filtering for Dynamic Bayesian Networks
Particle filters (PFs) are powerful samplingbased inference/learning al...
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Deep Fried Convnets
The fully connected layers of a deep convolutional neural network typica...
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Deep MultiInstance Transfer Learning
We present a new approach for transferring knowledge from groups to indi...
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Heteroscedastic Treed Bayesian Optimisation
Optimising blackbox functions is important in many disciplines, such as...
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Nonparametric Bayesian Logic
The Bayesian Logic (BLOG) language was recently developed for defining f...
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Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process HyperParameters
Bayesian optimisation has gained great popularity as a tool for optimisi...
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Intracluster Moves for Constrained DiscreteSpace MCMC
This paper addresses the problem of sampling from binary distributions w...
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Bayesian MultiScale Optimistic Optimization
Bayesian optimization is a powerful global optimization technique for ex...
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Learning where to Attend with Deep Architectures for Image Tracking
We discuss an attentional model for simultaneous object tracking and rec...
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Narrowing the Gap: Random Forests In Theory and In Practice
Despite widespread interest and practical use, the theoretical propertie...
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Linear and Parallel Learning of Markov Random Fields
We introduce a new embarrassingly parallel parameter learning algorithm ...
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Predicting Parameters in Deep Learning
We demonstrate that there is significant redundancy in the parameterizat...
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Exploiting correlation and budget constraints in Bayesian multiarmed bandit optimization
We address the problem of finding the maximizer of a nonlinear smooth fu...
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Consistency of Online Random Forests
As a testament to their success, the theory of random forests has long b...
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Herded Gibbs Sampling
The Gibbs sampler is one of the most popular algorithms for inference in...
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Reversible Jump MCMC Simulated Annealing for Neural Networks
We propose a novel reversible jump Markov chain Monte Carlo (MCMC) simul...
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Variational MCMC
We propose a new class of learning algorithms that combines variational ...
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Bayesian Optimization in a Billion Dimensions via Random Embeddings
Bayesian optimization techniques have been successfully applied to robot...
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Toward Practical N2 Monte Carlo: the Marginal Particle Filter
Sequential Monte Carlo techniques are useful for state estimation in non...
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Learning about individuals from group statistics
We propose a new problem formulation which is similar to, but more infor...
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Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations
This paper analyzes the problem of Gaussian process (GP) bandits with de...
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New inference strategies for solving Markov Decision Processes using reversible jump MCMC
In this paper we build on previous work which uses inferences techniques...
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Decentralized, Adaptive, LookAhead Particle Filtering
The decentralized particle filter (DPF) was proposed recently to increas...
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Regret Bounds for Deterministic Gaussian Process Bandits
This paper analyses the problem of Gaussian process (GP) bandits with de...
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Asymptotic Efficiency of Deterministic Estimators for Discrete EnergyBased Models: Ratio Matching and Pseudolikelihood
Standard maximum likelihood estimation cannot be applied to discrete ene...
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SelfAvoiding Random Dynamics on Integer Complex Systems
This paper introduces a new specialized algorithm for equilibrium Monte ...
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Bayesian Optimization for Adaptive MCMC
This paper proposes a new randomized strategy for adaptive MCMC using Ba...
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Recklessly Approximate Sparse Coding
It has recently been observed that certain extremely simple feature enco...
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Nando de Freitas
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Nando de Freitas is a computer science professor at Oxford University. He is also a Linacre College Fellow in Oxford. De Freitas is known as a body in the fields of machine learning, especially in the subfields of neural networking, Bayesian inference and optimization of Bayesian and deep learning.
Born in Zimbabwe, De Freitas. He studied his bachelor’s and MSc at Witwatersrand University and completed a PhD at Trinity College, Cambridge. From 2001 he was a professor at the University of British Columbia, in 2013 he joined the Department of Computer Science at Oxford University and worked for DeepMind of Google.
De Freitas has been recognized by the following awards for his contributions to machine learning:
Best Paper Award at the International Machine Learning Conference
Best Paper Award at the International Learning Conference
Google Research Faculty Award
Distinguished Paper Award for Artificial Intelligence at the International Joint Conference
Charles A. McDowell Award for Research Excellence
Young Researcher Award for Mathematics of Information and Complex Systems