Search Results for author: Xiaoyu Lu

Found 11 papers, 3 papers with code

Trajectory VAE for multi-modal imitation

no code implementations ICLR 2019 Xiaoyu Lu, Jan Stuehmer, Katja Hofmann

In this paper, we use a generative model to capture different emergent playstyles in an unsupervised manner, enabling the imitation of a diverse range of distinct behaviours.

Continuous Control Imitation Learning

Additive Gaussian Processes Revisited

1 code implementation20 Jun 2022 Xiaoyu Lu, Alexis Boukouvalas, James Hensman

Gaussian Process (GP) models are a class of flexible non-parametric models that have rich representational power.

Gaussian Processes

Spatially and Robustly Hybrid Mixture Regression Model for Inference of Spatial Dependence

1 code implementation1 Sep 2021 Wennan Chang, Pengtao Dang, Changlin Wan, Xiaoyu Lu, Yue Fang, Tong Zhao, Yong Zang, Bo Li, Chi Zhang, Sha Cao

Compared with existing spatial regression models, our proposed model assumes the existence a few distinct regression models that are estimated based on observations that exhibit similar response-predictor relationships.


FactoredRL: Leveraging Factored Graphs for Deep Reinforcement Learning

no code implementations1 Jan 2021 Bharathan Balaji, Petros Christodoulou, Xiaoyu Lu, Byungsoo Jeon, Jordan Bell-Masterson

We propose a simple class of deep reinforcement learning (RL) methods, called FactoredRL, that can leverage factored environment structures to improve the sample efficiency of existing model-based and model-free RL algorithms.

reinforcement-learning Reinforcement Learning (RL)

Structure Mapping for Transferability of Causal Models

1 code implementation18 Jul 2020 Purva Pruthi, Javier González, Xiaoyu Lu, Madalina Fiterau

Human beings learn causal models and constantly use them to transfer knowledge between similar environments.

reinforcement-learning Reinforcement Learning (RL) +1

Causal Bayesian Optimization

no code implementations24 May 2020 Virginia Aglietti, Xiaoyu Lu, Andrei Paleyes, Javier González

This paper studies the problem of globally optimizing a variable of interest that is part of a causal model in which a sequence of interventions can be performed.

Bayesian Optimization Causal Inference +1

On Exploration, Exploitation and Learning in Adaptive Importance Sampling

no code implementations31 Oct 2018 Xiaoyu Lu, Tom Rainforth, Yuan Zhou, Jan-Willem van de Meent, Yee Whye Teh

We study adaptive importance sampling (AIS) as an online learning problem and argue for the importance of the trade-off between exploration and exploitation in this adaptation.

Inference Trees: Adaptive Inference with Exploration

no code implementations25 Jun 2018 Tom Rainforth, Yuan Zhou, Xiaoyu Lu, Yee Whye Teh, Frank Wood, Hongseok Yang, Jan-Willem van de Meent

We introduce inference trees (ITs), a new class of inference methods that build on ideas from Monte Carlo tree search to perform adaptive sampling in a manner that balances exploration with exploitation, ensures consistency, and alleviates pathologies in existing adaptive methods.

Relativistic Monte Carlo

no code implementations14 Sep 2016 Xiaoyu Lu, Valerio Perrone, Leonard Hasenclever, Yee Whye Teh, Sebastian J. Vollmer

Based on this, we develop relativistic stochastic gradient descent by taking the zero-temperature limit of relativistic stochastic gradient Hamiltonian Monte Carlo.

Collaborative Filtering with Side Information: a Gaussian Process Perspective

no code implementations23 May 2016 Hyunjik Kim, Xiaoyu Lu, Seth Flaxman, Yee Whye Teh

We tackle the problem of collaborative filtering (CF) with side information, through the lens of Gaussian Process (GP) regression.

Collaborative Filtering regression

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