Andreas C. Damianou

According to our database, Andreas C. Damianou authored at least 32 papers between 2011 and 2021.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2021

Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis.

Andreas C. Damianou, Neil D. Lawrence, Carl Henrik Ek

J. Mach. Learn. Res., 2021

Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data.

Francesco Tonolini, Pablo Garcia Moreno, Andreas C. Damianou, Roderick Murray-Smith

Proceedings of the 9th International Conference on Learning Representations, 2021

Fast Adaptation with Linearized Neural Networks.

Wesley J. Maddox, Shuai Tang, Pablo Garcia Moreno, Andrew Gordon Wilson, Andreas C. Damianou

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Similarity of Neural Networks with Gradients.

Shuai Tang, Wesley J. Maddox, Charlie Dickens, Tom Diethe, Andreas C. Damianou

CoRR, 2020

Empirical Bayes Transductive Meta-Learning with Synthetic Gradients.

Shell Xu Hu, Pablo Garcia Moreno, Yang Xiao, Xi Shen, Guillaume Obozinski, Neil D. Lawrence, Andreas C. Damianou

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems.

Bharathan Balaji, Jordan Bell-Masterson, Enes Bilgin, Andreas C. Damianou, Pablo Moreno Garcia, Arpit Jain, Runfei Luo, Alvaro Maggiar, Balakrishnan Narayanaswamy, Chun Ye

CoRR, 2019

Deep Gaussian Processes for Multi-fidelity Modeling.

Kurt Cutajar, Mark Pullin, Andreas C. Damianou, Neil D. Lawrence, Javier González

CoRR, 2019

Transferring Knowledge across Learning Processes.

Sebastian Flennerhag, Pablo Garcia Moreno, Neil D. Lawrence, Andreas C. Damianou

Proceedings of the 7th International Conference on Learning Representations, 2019

Variational Information Distillation for Knowledge Transfer.

Sungsoo Ahn, Shell Xu Hu, Andreas C. Damianou, Neil D. Lawrence, Zhenwen Dai

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.

Clément Moulin-Frier, Tobias Fischer, Maxime Petit, Grégoire Pointeau, Jordi-Ysard Puigbo, Ugo Pattacini, Sock Ching Low, Daniel Camilleri, Phuong D. H. Nguyen, Matej Hoffmann, Hyung Jin Chang, Martina Zambelli, Anne-Laure Mealier, Andreas C. Damianou, Giorgio Metta, Tony J. Prescott, Yiannis Demiris, Peter Ford Dominey, Paul F. M. J. Verschure

IEEE Trans. Cogn. Dev. Syst., 2018

Deep Gaussian Processes with Convolutional Kernels.

Vinayak Kumar, Vaibhav Singh, P. K. Srijith, Andreas C. Damianou

CoRR, 2018

Leveraging Crowdsourcing Data for Deep Active Learning An Application: Learning Intents in Alexa.

Jie Yang, Thomas Drake, Andreas C. Damianou, Yoelle Maarek

Proceedings of the 2018 World Wide Web Conference on World Wide Web, 2018

2017

Manifold Alignment Determination: finding correspondences across different data views.

Andreas C. Damianou, Neil D. Lawrence, Carl Henrik Ek

CoRR, 2017

Online Constrained Model-based Reinforcement Learning.

Benjamin van Niekerk, Andreas C. Damianou, Benjamin Rosman

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Inverse Reinforcement Learning via Deep Gaussian Process.

Ming Jin, Andreas C. Damianou, Pieter Abbeel, Costas J. Spanos

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Preferential Bayesian Optimization.

Javier González, Zhenwen Dai, Andreas C. Damianou, Neil D. Lawrence

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes.

Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence

J. Mach. Learn. Res., 2016

Recurrent Gaussian Processes.

César Lincoln C. Mattos, Zhenwen Dai, Andreas C. Damianou, Jeremy Forth, Guilherme A. Barreto, Neil D. Lawrence

Proceedings of the 4th International Conference on Learning Representations, 2016

Variational Auto-encoded Deep Gaussian Processes.

Zhenwen Dai, Andreas C. Damianou, Javier González, Neil D. Lawrence

Proceedings of the 4th International Conference on Learning Representations, 2016

A Bioinspired Approach to Vision.

Daniel Camilleri, Luke W. Boorman, Uriel Martinez-Hernandez, Andreas C. Damianou, Tony J. Prescott

Proceedings of the Towards Autonomous Robotic Systems - 17th Annual Conference, 2016

An integrated probabilistic framework for robot perception, learning and memory.

Uriel Martinez-Hernandez, Andreas C. Damianou, Daniel Camilleri, Luke W. Boorman, Neil D. Lawrence, Tony J. Prescott

Proceedings of the 2016 IEEE International Conference on Robotics and Biomimetics, 2016

iCub Visual Memory Inspector: Visualising the iCub's Thoughts.

Daniel Camilleri, Andreas C. Damianou, Harry Jackson, Neil D. Lawrence, Tony J. Prescott

Proceedings of the Biomimetic and Biohybrid Systems - 5th International Conference, 2016

Probabilistic consolidation of grasp experience.

Yasemin Bekiroglu, Andreas C. Damianou, Renaud Detry, Johannes A. Stork, Danica Kragic, Carl Henrik Ek

Proceedings of the 2016 IEEE International Conference on Robotics and Automation, 2016

2015

Semi-described and semi-supervised learning with Gaussian processes.

Andreas C. Damianou, Neil D. Lawrence

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

A Top-Down Approach for a Synthetic Autobiographical Memory System.

Andreas C. Damianou, Carl Henrik Ek, Luke Boorman, Neil D. Lawrence, Tony J. Prescott

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.

Luke W. Boorman, Andreas C. Damianou, Uriel Martinez-Hernandez, Tony J. Prescott

Proceedings of the Biomimetic and Biohybrid Systems - 4th International Conference, 2015

2014

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

Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence

CoRR, 2014

Gaussian Process Models with Parallelization and GPU acceleration.

Zhenwen Dai, Andreas C. Damianou, James Hensman, Neil D. Lawrence

CoRR, 2014

Active learning for sparse bayesian multilabel classification.

Deepak Vasisht, Andreas C. Damianou, Manik Varma, Ashish Kapoor

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014

2013

Deep Gaussian Processes.

Andreas C. Damianou, Neil D. Lawrence

Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2012

Manifold Relevance Determination.

Andreas C. Damianou, Carl Henrik Ek, Michalis K. Titsias, Neil D. Lawrence

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Variational Gaussian Process Dynamical Systems.

Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence

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