Theofanis Karaletsos

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Uber ∙ Geometric Intelligence Inc.

Stochastic Aggregation in Graph Neural Networks

Graph neural networks (GNNs) manifest pathologies including over-smoothi...
02/25/2021 ∙ by Yuanqing Wang, et al. ∙ 8 ∙ share

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Variational Auto-Regressive Gaussian Processes for Continual Learning

This paper proposes Variational Auto-Regressive Gaussian Process (VAR-GP...
06/09/2020 ∙ by Sanyam Kapoor, et al. ∙ 0 ∙ share

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Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights

Probabilistic neural networks are typically modeled with independent wei...
02/10/2020 ∙ by Theofanis Karaletsos, et al. ∙ 10 ∙ share

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Generalized Hidden Parameter MDPs Transferable Model-based RL in a Handful of Trials

There is broad interest in creating RL agents that can solve many (relat...
02/08/2020 ∙ by Christian F. Perez, et al. ∙ 0 ∙ share

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Applying SVGD to Bayesian Neural Networks for Cyclical Time-Series Prediction and Inference

A regression-based BNN model is proposed to predict spatiotemporal quant...
01/17/2019 ∙ by Xinyu Hu, et al. ∙ 0 ∙ share

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Efficient transfer learning and online adaptation with latent variable models for continuous control

Traditional model-based RL relies on hand-specified or learned models of...
12/08/2018 ∙ by Christian F. Perez, et al. ∙ 0 ∙ share

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Pyro: Deep Universal Probabilistic Programming

Pyro is a probabilistic programming language built on Python as a platfo...
10/18/2018 ∙ by Eli Bingham, et al. ∙ 0 ∙ share

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Probabilistic Meta-Representations Of Neural Networks

Existing Bayesian treatments of neural networks are typically characteri...
10/01/2018 ∙ by Theofanis Karaletsos, et al. ∙ 0 ∙ share

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Pathwise Derivatives for Multivariate Distributions

We exploit the link between the transport equation and derivatives of ex...
06/05/2018 ∙ by Martin Jankowiak, et al. ∙ 0 ∙ share

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Likelihood-free inference with emulator networks

Approximate Bayesian Computation (ABC) provides methods for Bayesian inf...
05/23/2018 ∙ by Jan-Matthis Lueckmann, et al. ∙ 0 ∙ share

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Adversarial Message Passing For Graphical Models

Bayesian inference on structured models typically relies on the ability ...
12/15/2016 ∙ by Theofanis Karaletsos, et al. ∙ 0 ∙ share

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Conditional Similarity Networks

What makes images similar? To measure the similarity between images, the...
03/25/2016 ∙ by Andreas Veit, et al. ∙ 0 ∙ share

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Knowledge Transfer with Medical Language Embeddings

Identifying relationships between concepts is a key aspect of scientific...
02/10/2016 ∙ by Stephanie L. Hyland, et al. ∙ 0 ∙ share

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A Generative Model of Words and Relationships from Multiple Sources

Neural language models are a powerful tool to embed words into semantic ...
10/01/2015 ∙ by Stephanie L. Hyland, et al. ∙ 0 ∙ share

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Bayesian representation learning with oracle constraints

Representation learning systems typically rely on massive amounts of lab...
06/16/2015 ∙ by Theofanis Karaletsos, et al. ∙ 0 ∙ share

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Automatic Relevance Determination For Deep Generative Models

A recurring problem when building probabilistic latent variable models i...
05/28/2015 ∙ by Theofanis Karaletsos, et al. ∙ 0 ∙ share

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Theofanis Karaletsos

Featured Co-authors

Zoubin Ghahramani

Serge Belongie

Gunnar Rätsch

Noah D. Goodman

Andreas Veit

Martin Jankowiak

Rohit Singh

Jakob H. Macke

Felipe Petroski Such

Stephanie L. Hyland

Thang D. Bui

Stochastic Aggregation in Graph Neural Networks

Variational Auto-Regressive Gaussian Processes for Continual Learning

Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights

Generalized Hidden Parameter MDPs Transferable Model-based RL in a Handful of Trials

Applying SVGD to Bayesian Neural Networks for Cyclical Time-Series Prediction and Inference

Efficient transfer learning and online adaptation with latent variable models for continuous control

Pyro: Deep Universal Probabilistic Programming

Probabilistic Meta-Representations Of Neural Networks

Pathwise Derivatives for Multivariate Distributions

Likelihood-free inference with emulator networks

Adversarial Message Passing For Graphical Models

Conditional Similarity Networks

Knowledge Transfer with Medical Language Embeddings

A Generative Model of Words and Relationships from Multiple Sources

Bayesian representation learning with oracle constraints

Automatic Relevance Determination For Deep Generative Models