
Bayesian Optimization in AlphaGo
During the development of AlphaGo, its many hyperparameters were tuned ...
12/17/2018 ∙ by Yutian Chen, et al. ∙ 128 ∙ shareread it

LargeScale Visual Speech Recognition
This work presents a scalable solution to openvocabulary visual speech ...
07/13/2018 ∙ by Brendan Shillingford, et al. ∙ 68 ∙ shareread it

TaskRelevant Adversarial Imitation Learning
We show that a critical problem in adversarial imitation from highdimen...
10/02/2019 ∙ by Konrad Zolna, et al. ∙ 35 ∙ shareread it

Metalearning of Sequential Strategies
In this report we review memorybased metalearning as a tool for buildi...
05/08/2019 ∙ by Pedro A. Ortega, et al. ∙ 16 ∙ shareread it

Making Efficient Use of Demonstrations to Solve Hard Exploration Problems
This paper introduces R2D3, an agent that makes efficient use of demonst...
09/03/2019 ∙ by Tom Le Paine, et al. ∙ 10 ∙ shareread it

Learning Compositional Neural Programs with Recursive Tree Search and Planning
We propose a novel reinforcement learning algorithm, AlphaNPI, that inco...
05/30/2019 ∙ by Thomas Pierrot, et al. ∙ 7 ∙ shareread it

Modular MetaLearning with Shrinkage
Most gradientbased approaches to metalearning do not explicitly accoun...
09/12/2019 ∙ by Yutian Chen, et al. ∙ 6 ∙ shareread it

OneShot HighFidelity Imitation: Training LargeScale Deep Nets with RL
Humans are experts at highfidelity imitation  closely mimicking a dem...
10/11/2018 ∙ by Tom Le Paine, et al. ∙ 4 ∙ shareread it

Playing hard exploration games by watching YouTube
Deep reinforcement learning methods traditionally struggle with tasks wh...
05/29/2018 ∙ by Yusuf Aytar, et al. ∙ 2 ∙ shareread it

Sample Efficient Adaptive TexttoSpeech
We present a metalearning approach for adaptive texttospeech (TTS) wi...
09/27/2018 ∙ by Yutian Chen, et al. ∙ 2 ∙ shareread it

The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously
This paper introduces the Intentional Unintentional (IU) agent. This age...
07/11/2017 ∙ by Serkan Cabi, et al. ∙ 0 ∙ shareread it

Learning to learn by gradient descent by gradient descent
The move from handdesigned features to learned features in machine lear...
06/14/2016 ∙ by Marcin Andrychowicz, et al. ∙ 0 ∙ shareread it

Neural ProgrammerInterpreters
We propose the neural programmerinterpreter (NPI): a recurrent and comp...
11/19/2015 ∙ by Scott Reed, et al. ∙ 0 ∙ shareread it

ACDC: A Structured Efficient Linear Layer
The linear layer is one of the most pervasive modules in deep learning r...
11/18/2015 ∙ by Marcin Moczulski, et al. ∙ 0 ∙ shareread it

Programmable Agents
We build deep RL agents that execute declarative programs expressed in f...
06/20/2017 ∙ by Misha Denil, et al. ∙ 0 ∙ shareread it

Learned Optimizers that Scale and Generalize
Learning to learn has emerged as an important direction for achieving ar...
03/14/2017 ∙ by Olga Wichrowska, et al. ∙ 0 ∙ shareread it

Learning to Learn without Gradient Descent by Gradient Descent
We learn recurrent neural network optimizers trained on simple synthetic...
11/11/2016 ∙ by Yutian Chen, et al. ∙ 0 ∙ shareread it

Learning to Communicate to Solve Riddles with Deep Distributed Recurrent QNetworks
We propose deep distributed recurrent Qnetworks (DDRQN), which enable t...
02/08/2016 ∙ by Jakob N. Foerster, et al. ∙ 0 ∙ shareread it

Parallel Multiscale Autoregressive Density Estimation
PixelCNN achieves stateoftheart results in density estimation for nat...
03/10/2017 ∙ by Scott Reed, et al. ∙ 0 ∙ shareread it

Learning to Perform Physics Experiments via Deep Reinforcement Learning
When encountering novel objects, humans are able to infer a wide range o...
11/06/2016 ∙ by Misha Denil, et al. ∙ 0 ∙ shareread it

Unbounded Bayesian Optimization via Regularization
Bayesian optimization has recently emerged as a popular and efficient to...
08/14/2015 ∙ by Bobak Shahriari, et al. ∙ 0 ∙ shareread it

Proceedings of the TwentyEighth Conference on Uncertainty in Artificial Intelligence (2012)
This is the Proceedings of the TwentyEighth Conference on Uncertainty i...
01/19/2013 ∙ by Nando de Freitas, et al. ∙ 0 ∙ shareread it

RaoBlackwellised Particle Filtering for Dynamic Bayesian Networks
Particle filters (PFs) are powerful samplingbased inference/learning al...
01/16/2013 ∙ by Arnaud Doucet, et al. ∙ 0 ∙ shareread it

Deep Fried Convnets
The fully connected layers of a deep convolutional neural network typica...
12/22/2014 ∙ by Zichao Yang, et al. ∙ 0 ∙ shareread it

Deep MultiInstance Transfer Learning
We present a new approach for transferring knowledge from groups to indi...
11/12/2014 ∙ by Dimitrios Kotzias, et al. ∙ 0 ∙ shareread it

Heteroscedastic Treed Bayesian Optimisation
Optimising blackbox functions is important in many disciplines, such as...
10/27/2014 ∙ by JohnAlexander M. Assael, et al. ∙ 0 ∙ shareread it

Nonparametric Bayesian Logic
The Bayesian Logic (BLOG) language was recently developed for defining f...
07/04/2012 ∙ by Peter Carbonetto, et al. ∙ 0 ∙ shareread it

Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process HyperParameters
Bayesian optimisation has gained great popularity as a tool for optimisi...
06/30/2014 ∙ by Ziyu Wang, et al. ∙ 0 ∙ shareread it

Intracluster Moves for Constrained DiscreteSpace MCMC
This paper addresses the problem of sampling from binary distributions w...
03/15/2012 ∙ by Firas Hamze, et al. ∙ 0 ∙ shareread it

Bayesian MultiScale Optimistic Optimization
Bayesian optimization is a powerful global optimization technique for ex...
02/27/2014 ∙ by Ziyu Wang, et al. ∙ 0 ∙ shareread it

Learning where to Attend with Deep Architectures for Image Tracking
We discuss an attentional model for simultaneous object tracking and rec...
09/16/2011 ∙ by Misha Denil, et al. ∙ 0 ∙ shareread it

Narrowing the Gap: Random Forests In Theory and In Practice
Despite widespread interest and practical use, the theoretical propertie...
10/04/2013 ∙ by Misha Denil, et al. ∙ 0 ∙ shareread it

Linear and Parallel Learning of Markov Random Fields
We introduce a new embarrassingly parallel parameter learning algorithm ...
08/29/2013 ∙ by Yariv Dror Mizrahi, et al. ∙ 0 ∙ shareread it

Predicting Parameters in Deep Learning
We demonstrate that there is significant redundancy in the parameterizat...
06/03/2013 ∙ by Misha Denil, et al. ∙ 0 ∙ shareread it

Exploiting correlation and budget constraints in Bayesian multiarmed bandit optimization
We address the problem of finding the maximizer of a nonlinear smooth fu...
03/27/2013 ∙ by Matthew W. Hoffman, et al. ∙ 0 ∙ shareread it

Consistency of Online Random Forests
As a testament to their success, the theory of random forests has long b...
02/20/2013 ∙ by Misha Denil, et al. ∙ 0 ∙ shareread it

Herded Gibbs Sampling
The Gibbs sampler is one of the most popular algorithms for inference in...
01/17/2013 ∙ by Luke Bornn, et al. ∙ 0 ∙ shareread it

Reversible Jump MCMC Simulated Annealing for Neural Networks
We propose a novel reversible jump Markov chain Monte Carlo (MCMC) simul...
01/16/2013 ∙ by Christophe Andrieu, et al. ∙ 0 ∙ shareread it

Variational MCMC
We propose a new class of learning algorithms that combines variational ...
01/10/2013 ∙ by Nando de Freitas, et al. ∙ 0 ∙ shareread it

Bayesian Optimization in a Billion Dimensions via Random Embeddings
Bayesian optimization techniques have been successfully applied to robot...
01/09/2013 ∙ by Ziyu Wang, et al. ∙ 0 ∙ shareread it

Toward Practical N2 Monte Carlo: the Marginal Particle Filter
Sequential Monte Carlo techniques are useful for state estimation in non...
07/04/2012 ∙ by Mike Klaas, et al. ∙ 0 ∙ shareread it

Learning about individuals from group statistics
We propose a new problem formulation which is similar to, but more infor...
07/04/2012 ∙ by Hendrik Kuck, et al. ∙ 0 ∙ shareread it

Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations
This paper analyzes the problem of Gaussian process (GP) bandits with de...
06/27/2012 ∙ by Nando de Freitas, et al. ∙ 0 ∙ shareread it

New inference strategies for solving Markov Decision Processes using reversible jump MCMC
In this paper we build on previous work which uses inferences techniques...
05/09/2012 ∙ by Matthias Hoffman, et al. ∙ 0 ∙ shareread it

Decentralized, Adaptive, LookAhead Particle Filtering
The decentralized particle filter (DPF) was proposed recently to increas...
03/12/2012 ∙ by Mohamed Osama Ahmed, et al. ∙ 0 ∙ shareread it

Regret Bounds for Deterministic Gaussian Process Bandits
This paper analyses the problem of Gaussian process (GP) bandits with de...
03/09/2012 ∙ by Nando de Freitas, et al. ∙ 0 ∙ shareread it

Asymptotic Efficiency of Deterministic Estimators for Discrete EnergyBased Models: Ratio Matching and Pseudolikelihood
Standard maximum likelihood estimation cannot be applied to discrete ene...
02/14/2012 ∙ by Benjamin Marlin, et al. ∙ 0 ∙ shareread it

SelfAvoiding Random Dynamics on Integer Complex Systems
This paper introduces a new specialized algorithm for equilibrium Monte ...
11/23/2011 ∙ by Firas Hamze, et al. ∙ 0 ∙ shareread it

Bayesian Optimization for Adaptive MCMC
This paper proposes a new randomized strategy for adaptive MCMC using Ba...
10/29/2011 ∙ by Nimalan Mahendran, et al. ∙ 0 ∙ shareread it

Recklessly Approximate Sparse Coding
It has recently been observed that certain extremely simple feature enco...
08/04/2012 ∙ by Misha Denil, et al. ∙ 0 ∙ shareread it
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