Andreas C. Damianou

Affiliations:
  • Amazon Research Cambridge, UK
  • University of Sheffield, Department of Computer Science, UK


According to our database1, Andreas C. Damianou authored at least 33 papers between 2011 and 2021.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Bibliography

2021
Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis.
J. Mach. Learn. Res., 2021

Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data.
Proceedings of the 9th International Conference on Learning Representations, 2021

Fast Adaptation with Linearized Neural Networks.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Similarity of Neural Networks with Gradients.
CoRR, 2020

Empirical Bayes Transductive Meta-Learning with Synthetic Gradients.
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems.
CoRR, 2019

Deep Gaussian Processes for Multi-fidelity Modeling.
CoRR, 2019

Transferring Knowledge across Learning Processes.
Proceedings of the 7th International Conference on Learning Representations, 2019

Variational Information Distillation for Knowledge Transfer.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018
DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self.
IEEE Trans. Cogn. Dev. Syst., 2018

Deep Gaussian Processes with Convolutional Kernels.
CoRR, 2018

Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa.
CoRR, 2018

Leveraging Crowdsourcing Data for Deep Active Learning An Application: Learning Intents in Alexa.
Proceedings of the 2018 World Wide Web Conference on World Wide Web, 2018

2017
Manifold Alignment Determination: finding correspondences across different data views.
CoRR, 2017

Online Constrained Model-based Reinforcement Learning.
Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Inverse Reinforcement Learning via Deep Gaussian Process.
Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Preferential Bayesian Optimization.
Proceedings of the 34th International Conference on Machine Learning, 2017

2016
Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes.
J. Mach. Learn. Res., 2016

Recurrent Gaussian Processes.
Proceedings of the 4th International Conference on Learning Representations, 2016

Variational Auto-encoded Deep Gaussian Processes.
Proceedings of the 4th International Conference on Learning Representations, 2016

A Bioinspired Approach to Vision.
Proceedings of the Towards Autonomous Robotic Systems - 17th Annual Conference, 2016

An integrated probabilistic framework for robot perception, learning and memory.
Proceedings of the 2016 IEEE International Conference on Robotics and Biomimetics, 2016

iCub Visual Memory Inspector: Visualising the iCub's Thoughts.
Proceedings of the Biomimetic and Biohybrid Systems - 5th International Conference, 2016

Probabilistic consolidation of grasp experience.
Proceedings of the 2016 IEEE International Conference on Robotics and Automation, 2016

2015
Semi-described and semi-supervised learning with Gaussian processes.
Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

A Top-Down Approach for a Synthetic Autobiographical Memory System.
Proceedings of the Biomimetic and Biohybrid Systems - 4th International Conference, 2015

Extending a Hippocampal Model for Navigation Around a Maze Generated from Real-World Data.
Proceedings of the Biomimetic and Biohybrid Systems - 4th International Conference, 2015

2014
Variational Inference for Uncertainty on the Inputs of Gaussian Process Models.
CoRR, 2014

Gaussian Process Models with Parallelization and GPU acceleration.
CoRR, 2014

Active learning for sparse bayesian multilabel classification.
Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014

2013
Deep Gaussian Processes.
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2012
Manifold Relevance Determination.
Proceedings of the 29th International Conference on Machine Learning, 2012

2011
Variational Gaussian Process Dynamical Systems.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011


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