Ziyu Wang

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Google ∙ University of Oxford ∙ The University of British Columbia

Research Scientist at Google

Hyperparameter Selection for Offline Reinforcement Learning

Offline reinforcement learning (RL purely from logged data) is an import...
07/17/2020 ∙ by Tom Le Paine, et al. ∙ 30 ∙ share

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Critic Regularized Regression

Offline reinforcement learning (RL), also known as batch RL, offers the ...
06/26/2020 ∙ by Ziyu Wang, et al. ∙ 32 ∙ share

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RL Unplugged: Benchmarks for Offline Reinforcement Learning

Offline methods for reinforcement learning have the potential to help br...
06/24/2020 ∙ by Caglar Gulcehre, et al. ∙ 10 ∙ share

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Acme: A Research Framework for Distributed Reinforcement Learning

Deep reinforcement learning has led to many recent-and groundbreaking-ad...
06/01/2020 ∙ by Matt Hoffman, et al. ∙ 22 ∙ share

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A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models

Score matching provides an effective approach to learning flexible unnor...
02/18/2020 ∙ by Ziyu Wang, et al. ∙ 9 ∙ share

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Incentive Analysis of Bitcoin-NG, Revisited

Bitcoin-NG is among the first blockchain protocols to approach the near-...
01/14/2020 ∙ by Jianyu Niu, et al. ∙ 0 ∙ share

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The Usual Suspects? Reassessing Blame for VAE Posterior Collapse

In narrow asymptotic settings Gaussian VAE models of continuous data hav...
12/23/2019 ∙ by Bin Dai, et al. ∙ 7 ∙ share

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Task-Relevant Adversarial Imitation Learning

We show that a critical problem in adversarial imitation from high-dimen...
10/02/2019 ∙ by Konrad Zolna, et al. ∙ 35 ∙ share

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A Framework for Data-Driven Robotics

We present a framework for data-driven robotics that makes use of a larg...
09/26/2019 ∙ by Serkan Cabi, et al. ∙ 0 ∙ share

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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...
09/03/2019 ∙ by Tom Le Paine, et al. ∙ 10 ∙ share

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Towards a Multi-Chain Future of Proof-of-Space

Proof-of-Space provides an intriguing alternative for consensus protocol...
07/18/2019 ∙ by Shuyang Tang, et al. ∙ 0 ∙ share

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Deep Music Analogy Via Latent Representation Disentanglement

Analogy is a key solution to automated music generation, featured by its...
06/09/2019 ∙ by Ruihan Yang, et al. ∙ 8 ∙ share

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Function Space Particle Optimization for Bayesian Neural Networks

While Bayesian neural networks (BNNs) have drawn increasing attention, t...
02/26/2019 ∙ by Ziyu Wang, et al. ∙ 24 ∙ share

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A Framework for Automated Pop-song Melody Generation with Piano Accompaniment Arrangement

We contribute a pop-song automation framework for lead melody generation...
12/28/2018 ∙ by Ziyu Wang, et al. ∙ 4 ∙ share

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Bayesian Optimization in AlphaGo

During the development of AlphaGo, its many hyper-parameters were tuned ...
12/17/2018 ∙ by Yutian Chen, et al. ∙ 128 ∙ share

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One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL

Humans are experts at high-fidelity imitation -- closely mimicking a dem...
10/11/2018 ∙ by Tom Le Paine, et al. ∙ 4 ∙ share

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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 ∙ share

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Reinforcement and Imitation Learning for Diverse Visuomotor Skills

We propose a model-free deep reinforcement learning method that leverage...
02/26/2018 ∙ by Yuke Zhu, et al. ∙ 0 ∙ share

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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...
07/11/2017 ∙ by Serkan Cabi, et al. ∙ 0 ∙ share

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Emergence of Locomotion Behaviours in Rich Environments

The reinforcement learning paradigm allows, in principle, for complex be...
07/07/2017 ∙ by Nicolas Heess, et al. ∙ 0 ∙ share

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Parallel Multiscale Autoregressive Density Estimation

PixelCNN achieves state-of-the-art results in density estimation for nat...
03/10/2017 ∙ by Scott Reed, et al. ∙ 0 ∙ share

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Bayesian Optimisation for Machine Translation

This paper presents novel Bayesian optimisation algorithms for minimum e...
12/22/2014 ∙ by Yishu Miao, et al. ∙ 0 ∙ share

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Deep Fried Convnets

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

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Heteroscedastic Treed Bayesian Optimisation

Optimising black-box functions is important in many disciplines, such as...
10/27/2014 ∙ by John-Alexander M. Assael, et al. ∙ 0 ∙ share

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Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper-Parameters

Bayesian optimisation has gained great popularity as a tool for optimisi...
06/30/2014 ∙ by Ziyu Wang, et al. ∙ 0 ∙ share

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Bayesian Multi-Scale Optimistic Optimization

Bayesian optimization is a powerful global optimization technique for ex...
02/27/2014 ∙ by Ziyu Wang, et al. ∙ 0 ∙ share

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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 ∙ share

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Self-Avoiding 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 ∙ share

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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 ∙ share

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Ziyu Wang

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