
Learning Compositional Neural Programs for Continuous Control
We propose a novel solution to challenging sparsereward, continuous con...
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Hyperparameter Selection for Offline Reinforcement Learning
Offline reinforcement learning (RL purely from logged data) is an import...
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Critic Regularized Regression
Offline reinforcement learning (RL), also known as batch RL, offers the ...
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RL Unplugged: Benchmarks for Offline Reinforcement Learning
Offline methods for reinforcement learning have the potential to help br...
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Acme: A Research Framework for Distributed Reinforcement Learning
Deep reinforcement learning has led to many recentand groundbreakingad...
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TaskRelevant Adversarial Imitation Learning
We show that a critical problem in adversarial imitation from highdimen...
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A Framework for DataDriven Robotics
We present a framework for datadriven robotics that makes use of a larg...
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Modular MetaLearning with Shrinkage
Most gradientbased approaches to metalearning do not explicitly accoun...
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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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Metalearning of Sequential Strategies
In this report we review memorybased metalearning as a tool for buildi...
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Bayesian Optimization in AlphaGo
During the development of AlphaGo, its many hyperparameters were tuned ...
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Social Influence as Intrinsic Motivation for MultiAgent Deep Reinforcement Learning
We propose a unified mechanism for achieving coordination and communicat...
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Intrinsic Social Motivation via Causal Influence in MultiAgent RL
We derive a new intrinsic social motivation for multiagent reinforcemen...
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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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Sample Efficient Adaptive TexttoSpeech
We present a metalearning approach for adaptive texttospeech (TTS) wi...
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LargeScale Visual Speech Recognition
This work presents a scalable solution to openvocabulary visual speech ...
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Playing hard exploration games by watching YouTube
Deep reinforcement learning methods traditionally struggle with tasks wh...
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Hyperbolic Attention Networks
We introduce hyperbolic attention networks to endow neural networks with...
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Learning Awareness Models
We consider the setting of an agent with a fixed body interacting with a...
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Compositional Obverter Communication Learning From Raw Visual Input
One of the distinguishing aspects of human language is its compositional...
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Reinforcement and Imitation Learning for Diverse Visuomotor Skills
We propose a modelfree deep reinforcement learning method that leverage...
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Cortical microcircuits as gatedrecurrent neural networks
Cortical circuits exhibit intricate recurrent architectures that are rem...
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Fewshot Autoregressive Density Estimation: Towards Learning to Learn Distributions
Deep autoregressive models have shown stateoftheart performance in de...
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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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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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Parallel Multiscale Autoregressive Density Estimation
PixelCNN achieves stateoftheart results in density estimation for nat...
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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 Perform Physics Experiments via Deep Reinforcement Learning
When encountering novel objects, humans are able to infer a wide range o...
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LipNet: EndtoEnd Sentencelevel Lipreading
Lipreading is the task of decoding text from the movement of a speaker's...
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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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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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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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Unbounded Bayesian Optimization via Regularization
Bayesian optimization has recently emerged as a popular and efficient to...
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Deep Fried Convnets
The fully connected layers of a deep convolutional neural network typica...
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Extraction of Salient Sentences from Labelled Documents
We present a hierarchical convolutional document model with an architect...
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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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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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Bayesian MultiScale Optimistic Optimization
Bayesian optimization is a powerful global optimization technique for ex...
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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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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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Herded Gibbs Sampling
The Gibbs sampler is one of the most popular algorithms for inference in...
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RaoBlackwellised Particle Filtering for Dynamic Bayesian Networks
Particle filters (PFs) are powerful samplingbased inference/learning al...
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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