
Bayesian Optimization in AlphaGo
During the development of AlphaGo, its many hyperparameters were tuned ...
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Modular MetaLearning with Shrinkage
Most gradientbased approaches to metalearning do not explicitly accoun...
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Sample Efficient Adaptive TexttoSpeech
We present a metalearning approach for adaptive texttospeech (TTS) wi...
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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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Herding as a Learning System with EdgeofChaos Dynamics
Herding defines a deterministic dynamical system at the edge of chaos. I...
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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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Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data
Multivariate categorical data occur in many applications of machine lear...
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SublinearTime Approximate MCMC Transitions for Probabilistic Programs
Probabilistic programming languages can simplify the development of mach...
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Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior
In recent years a number of methods have been developed for automaticall...
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Austerity in MCMC Land: Cutting the MetropolisHastings Budget
Can we make Bayesian posterior MCMC sampling more efficient when faced w...
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Herded Gibbs Sampling
The Gibbs sampler is one of the most popular algorithms for inference in...
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SuperSamples from Kernel Herding
We extend the herding algorithm to continuous spaces by using the kernel...
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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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Yutian Chen
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Senior Research Scientist at DeepMind. Earned his B.E. of Electronic Engineering from Tsinghua University in China in June 2007. Then I went to the University of California, Irvine in the United States to pursue a doctoral degree.