
Fork or Fail: CycleConsistent Training with ManytoOne Mappings
Cycleconsistent training is widely used for jointly learning a forward ...
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Offline Learning from Demonstrations and Unlabeled Experience
Behavior cloning (BC) is often practical for robot learning because it a...
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Further Analysis of Outlier Detection with Deep Generative Models
The recent, counterintuitive discovery that deep generative models (DGM...
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POP909: A Popsong Dataset for Music Arrangement Generation
Music arrangement generation is a subtask of automatic music generation,...
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Learning Interpretable Representation for Controllable Polyphonic Music Generation
While deep generative models have become the leading methods for algorit...
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PIANOTREE VAE: Structured Representation Learning for Polyphonic Music
The dominant approach for music representation learning involves the dee...
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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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A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models
Score matching provides an effective approach to learning flexible unnor...
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Incentive Analysis of BitcoinNG, Revisited
BitcoinNG is among the first blockchain protocols to approach the near...
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The Usual Suspects? Reassessing Blame for VAE Posterior Collapse
In narrow asymptotic settings Gaussian VAE models of continuous data hav...
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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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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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Towards a MultiChain Future of ProofofSpace
ProofofSpace provides an intriguing alternative for consensus protocol...
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Deep Music Analogy Via Latent Representation Disentanglement
Analogy is a key solution to automated music generation, featured by its...
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Function Space Particle Optimization for Bayesian Neural Networks
While Bayesian neural networks (BNNs) have drawn increasing attention, t...
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A Framework for Automated Popsong Melody Generation with Piano Accompaniment Arrangement
We contribute a popsong automation framework for lead melody generation...
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Bayesian Optimization in AlphaGo
During the development of AlphaGo, its many hyperparameters were tuned ...
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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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Reinforcement and Imitation Learning for Diverse Visuomotor Skills
We propose a modelfree deep reinforcement learning method that leverage...
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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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Emergence of Locomotion Behaviours in Rich Environments
The reinforcement learning paradigm allows, in principle, for complex be...
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Parallel Multiscale Autoregressive Density Estimation
PixelCNN achieves stateoftheart results in density estimation for nat...
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Bayesian Optimisation for Machine Translation
This paper presents novel Bayesian optimisation algorithms for minimum e...
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Deep Fried Convnets
The fully connected layers of a deep convolutional neural network typica...
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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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Bayesian Optimization in a Billion Dimensions via Random Embeddings
Bayesian optimization techniques have been successfully applied to robot...
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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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Ziyu Wang
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Research Scientist at Google