b'Ziyu Wang'

research

∙ 05/22/2022

Fast Instrument Learning with Faster Rates

We investigate nonlinear instrumental variable (IV) regression given hig...

Ziyu Wang, et al. ∙

research

∙ 03/08/2022

NeReF: Neural Refractive Field for Fluid Surface Reconstruction and Implicit Representation

Existing neural reconstruction schemes such as Neural Radiance Field (Ne...

Ziyu Wang, et al. ∙

research

∙ 01/28/2022

DynaMixer: A Vision MLP Architecture with Dynamic Mixing

Recently, MLP-like vision models have achieved promising performances on...

Ziyu Wang, et al. ∙

research

∙ 12/30/2021

Audio-to-symbolic Arrangement via Cross-modal Music Representation Learning

Could we automatically derive the score of a piano accompaniment based o...

Ziyu Wang, et al. ∙

research

∙ 10/26/2021

C^2SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction

Recent development in brain-machine interface technology has made seizur...

Di Wu, et al. ∙

research

∙ 08/12/2021

iButter: Neural Interactive Bullet Time Generator for Human Free-viewpoint Rendering

Generating “bullet-time” effects of human free-viewpoint videos is criti...

Liao Wang, et al. ∙

research

∙ 06/16/2021

Scalable Quasi-Bayesian Inference for Instrumental Variable Regression

Recent years have witnessed an upsurge of interest in employing flexible...

Ziyu Wang, et al. ∙

research

∙ 05/05/2021

A Novel Multi-scale Dilated 3D CNN for Epileptic Seizure Prediction

Accurate prediction of epileptic seizures allows patients to take preven...

Ziyu Wang, et al. ∙

research

∙ 04/28/2021

Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization

Standard dynamics models for continuous control make use of feedforward ...

Michael R. Zhang, et al. ∙

research

∙ 04/06/2021

MirrorNeRF: One-shot Neural Portrait RadianceField from Multi-mirror Catadioptric Imaging

Photo-realistic neural reconstruction and rendering of the human portrai...

Ziyu Wang, et al. ∙

research

∙ 03/30/2021

Benchmarks for Deep Off-Policy Evaluation

Off-policy evaluation (OPE) holds the promise of being able to leverage ...

Justin Fu, et al. ∙

research

∙ 03/17/2021

Regularized Behavior Value Estimation

Offline reinforcement learning restricts the learning process to rely on...

Caglar Gulcehre, et al. ∙

research

∙ 12/14/2020

Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings

Cycle-consistent training is widely used for jointly learning a forward ...

Qipeng Guo, et al. ∙

research

∙ 11/27/2020

Offline Learning from Demonstrations and Unlabeled Experience

Behavior cloning (BC) is often practical for robot learning because it a...

Konrad Zolna, et al. ∙

research

∙ 10/25/2020

Further Analysis of Outlier Detection with Deep Generative Models

The recent, counter-intuitive discovery that deep generative models (DGM...

Ziyu Wang, et al. ∙

research

∙ 08/17/2020

POP909: A Pop-song Dataset for Music Arrangement Generation

Music arrangement generation is a subtask of automatic music generation,...

Ziyu Wang, et al. ∙

research

∙ 08/17/2020

Learning Interpretable Representation for Controllable Polyphonic Music Generation

While deep generative models have become the leading methods for algorit...

Ziyu Wang, et al. ∙

research

∙ 08/17/2020

PIANOTREE VAE: Structured Representation Learning for Polyphonic Music

The dominant approach for music representation learning involves the dee...

Ziyu Wang, et al. ∙

research

∙ 07/17/2020

Hyperparameter Selection for Offline Reinforcement Learning

Offline reinforcement learning (RL purely from logged data) is an import...

Tom Le Paine, et al. ∙

research

∙ 06/26/2020

Critic Regularized Regression

Offline reinforcement learning (RL), also known as batch RL, offers the ...

Ziyu Wang, et al. ∙

research

∙ 06/24/2020

RL Unplugged: Benchmarks for Offline Reinforcement Learning

Offline methods for reinforcement learning have the potential to help br...

Caglar Gulcehre, et al. ∙

research

∙ 06/01/2020

Acme: A Research Framework for Distributed Reinforcement Learning

Deep reinforcement learning has led to many recent-and groundbreaking-ad...

Matt Hoffman, et al. ∙

research

∙ 02/18/2020

A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models

Score matching provides an effective approach to learning flexible unnor...

Ziyu Wang, et al. ∙

research

∙ 01/14/2020

Incentive Analysis of Bitcoin-NG, Revisited

Bitcoin-NG is among the first blockchain protocols to approach the near-...

Jianyu Niu, et al. ∙

research

∙ 12/23/2019

The Usual Suspects? Reassessing Blame for VAE Posterior Collapse

In narrow asymptotic settings Gaussian VAE models of continuous data hav...

Bin Dai, et al. ∙

research

∙ 10/02/2019

Task-Relevant Adversarial Imitation Learning

We show that a critical problem in adversarial imitation from high-dimen...

Konrad Zolna, et al. ∙

research

∙ 09/26/2019

A Framework for Data-Driven Robotics

We present a framework for data-driven robotics that makes use of a larg...

Serkan Cabi, et al. ∙

research

∙ 09/03/2019

Making Efficient Use of Demonstrations to Solve Hard Exploration Problems

This paper introduces R2D3, an agent that makes efficient use of demonst...

Tom Le Paine, et al. ∙

research

∙ 07/18/2019

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

Proof-of-Space provides an intriguing alternative for consensus protocol...

Shuyang Tang, et al. ∙

research

∙ 06/09/2019

Deep Music Analogy Via Latent Representation Disentanglement

Analogy is a key solution to automated music generation, featured by its...

Ruihan Yang, et al. ∙

research

∙ 02/26/2019

Function Space Particle Optimization for Bayesian Neural Networks

While Bayesian neural networks (BNNs) have drawn increasing attention, t...

Ziyu Wang, et al. ∙

research

∙ 12/28/2018

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

We contribute a pop-song automation framework for lead melody generation...

Ziyu Wang, et al. ∙

research

∙ 12/17/2018

Bayesian Optimization in AlphaGo

During the development of AlphaGo, its many hyper-parameters were tuned ...

Yutian Chen, et al. ∙

research

∙ 10/11/2018

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

Humans are experts at high-fidelity imitation -- closely mimicking a dem...

Tom Le Paine, et al. ∙

research

∙ 05/29/2018

Playing hard exploration games by watching YouTube

Deep reinforcement learning methods traditionally struggle with tasks wh...

Yusuf Aytar, et al. ∙

research

∙ 02/26/2018

Reinforcement and Imitation Learning for Diverse Visuomotor Skills

We propose a model-free deep reinforcement learning method that leverage...

Yuke Zhu, et al. ∙

research

∙ 07/11/2017

The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously

This paper introduces the Intentional Unintentional (IU) agent. This age...

Serkan Cabi, et al. ∙

research

∙ 07/07/2017

Emergence of Locomotion Behaviours in Rich Environments

The reinforcement learning paradigm allows, in principle, for complex be...

Nicolas Heess, et al. ∙

research

∙ 03/10/2017

Parallel Multiscale Autoregressive Density Estimation

PixelCNN achieves state-of-the-art results in density estimation for nat...

Scott Reed, et al. ∙

research

∙ 12/22/2014

Bayesian Optimisation for Machine Translation

This paper presents novel Bayesian optimisation algorithms for minimum e...

Yishu Miao, et al. ∙

research

∙ 12/22/2014

Deep Fried Convnets

The fully connected layers of a deep convolutional neural network typica...

Zichao Yang, et al. ∙

research

∙ 10/27/2014

Heteroscedastic Treed Bayesian Optimisation

Optimising black-box functions is important in many disciplines, such as...

John-Alexander M. Assael, et al. ∙

research

∙ 06/30/2014

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

Bayesian optimisation has gained great popularity as a tool for optimisi...

Ziyu Wang, et al. ∙

research

∙ 02/27/2014

Bayesian Multi-Scale Optimistic Optimization

Bayesian optimization is a powerful global optimization technique for ex...

Ziyu Wang, et al. ∙

research

∙ 01/09/2013

Bayesian Optimization in a Billion Dimensions via Random Embeddings

Bayesian optimization techniques have been successfully applied to robot...

Ziyu Wang, et al. ∙

research

∙ 11/23/2011

Self-Avoiding Random Dynamics on Integer Complex Systems

This paper introduces a new specialized algorithm for equilibrium Monte ...

Firas Hamze, et al. ∙

research

∙ 10/29/2011

Bayesian Optimization for Adaptive MCMC

This paper proposes a new randomized strategy for adaptive MCMC using Ba...

Nimalan Mahendran, et al. ∙

Ziyu Wang

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