Kai Arulkumaran

According to our database, Kai Arulkumaran authored at least 36 papers between 2016 and 2024.

Collaborative distances:

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

2024

Preference-Learning Emitters for Mixed-Initiative Quality-Diversity Algorithms.

Roberto Gallotta, Kai Arulkumaran, Lisa B. Soros

IEEE Trans. Games, June, 2024

A comparison of visual and auditory EEG interfaces for robot multi-stage task control.

Kai Arulkumaran, Marina Di Vincenzo, Rousslan Fernand Julien Dossa, Shogo Akiyama, Dan Ogawa Lillrank, Motoshige Sato, Kenichi Tomeoka, Shuntaro Sasai

Frontiers Robotics AI, 2024

Design and evaluation of a global workspace agent embodied in a realistic multimodal environment.

Rousslan Fernand Julien Dossa, Kai Arulkumaran, Arthur Juliani, Shuntaro Sasai, Ryota Kanai

Frontiers Comput. Neurosci., 2024

Scaling Law in Neural Data: Non-Invasive Speech Decoding with 175 Hours of EEG Data.

Motoshige Sato, Kenichi Tomeoka, Ilya Horiguchi, Kai Arulkumaran, Ryota Kanai, Shuntaro Sasai

CoRR, 2024

2023

A Pragmatic Look at Deep Imitation Learning.

Kai Arulkumaran, Dan Ogawa Lillrank

Proceedings of the Asian Conference on Machine Learning, 2023

2022

On the link between conscious function and general intelligence in humans and machines.

Arthur Juliani, Kai Arulkumaran, Shuntaro Sasai, Ryota Kanai

Trans. Mach. Learn. Res., 2022

Analysing deep reinforcement learning agents trained with domain randomisation.

Tianhong Dai, Kai Arulkumaran, Tamara Gerbert, Samyakh Tukra, Feryal M. P. Behbahani, Anil Anthony Bharath

Neurocomputing, 2022

Learning Relative Return Policies With Upside-Down Reinforcement Learning.

Dylan R. Ashley, Kai Arulkumaran, Jürgen Schmidhuber, Rupesh Kumar Srivastava

CoRR, 2022

All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL.

Kai Arulkumaran, Dylan R. Ashley, Jürgen Schmidhuber, Rupesh Kumar Srivastava

CoRR, 2022

Evolving spaceships with a hybrid L-system constrained optimisation evolutionary algorithm.

Roberto Gallotta, Kai Arulkumaran, Lisa B. Soros

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9, 2022

Minimal criterion artist collective.

Kai Arulkumaran, Thu Nguyen-Phuoc

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9, 2022

Surrogate Infeasible Fitness Acquirement FI-2Pop for Procedural Content Generation.

Roberto Gallotta, Kai Arulkumaran, Lisa B. Soros

Proceedings of the IEEE Conference on Games, CoG 2022, Beijing, 2022

2021

The Societal Implications of Deep Reinforcement Learning.

Jess Whittlestone, Kai Arulkumaran, Matthew Crosby

J. Artif. Intell. Res., 2021

Diversity-Based Trajectory and Goal Selection with Hindsight Experience Replay.

Tianhong Dai, Hengyan Liu, Kai Arulkumaran, Guangyu Ren, Anil Anthony Bharath

Proceedings of the PRICAI 2021: Trends in Artificial Intelligence, 2021

2020

Sample efficiency, transfer learning and interpretability for deep reinforcement learning.

Kai Arulkumaran

PhD thesis, 2020

Privileged Information Dropout in Reinforcement Learning.

Pierre-Alexandre Kamienny, Kai Arulkumaran, Feryal M. P. Behbahani, Wendelin Boehmer, Shimon Whiteson

CoRR, 2020

EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis.

Kevin Pan, Guillem Hurault, Kai Arulkumaran, Hywel C. Williams, Reiko J. Tanaka

Proceedings of the Machine Learning in Medical Imaging - 11th International Workshop, 2020

Incorporating Human Priors into Deep Reinforcement Learning for Robotic Control.

Manon Flageat, Kai Arulkumaran, Anil A. Bharath

Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

2019

Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation.

Tianhong Dai, Kai Arulkumaran, Samyakh Tukra, Feryal M. P. Behbahani, Anil Anthony Bharath

CoRR, 2019

Sample-Efficient Reinforcement Learning with Maximum Entropy Mellowmax Episodic Control.

Marta Sarrico, Kai Arulkumaran, Andrea Agostinelli, Pierre Richemond, Anil Anthony Bharath

CoRR, 2019

Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-means.

Andrea Agostinelli, Kai Arulkumaran, Marta Sarrico, Pierre Richemond, Anil Anthony Bharath

CoRR, 2019

Deep Reinforcement Learning for Subpixel Neural Tracking.

Tianhong Dai, Magda Dubois, Kai Arulkumaran, Jonathan Campbell, Cher Bass, Benjamin Billot, Fatmatülzehra Uslu, Vincenzo De Paola, Claudia Clopath, Anil Anthony Bharath

Proceedings of the International Conference on Medical Imaging with Deep Learning, 2019

Image Synthesis with a Convolutional Capsule Generative Adversarial Network.

Cher Bass, Tianhong Dai, Benjamin Billot, Kai Arulkumaran, Antonia Creswell, Claudia Clopath, Vincenzo De Paola, Anil Anthony Bharath

Proceedings of the International Conference on Medical Imaging with Deep Learning, 2019

A Maximum Entropy Deep Reinforcement Learning Neural Tracker.

Shafa Balaram, Kai Arulkumaran, Tianhong Dai, Anil Anthony Bharath

Proceedings of the Machine Learning in Medical Imaging - 10th International Workshop, 2019

Adaptive Neural Trees.

Ryutaro Tanno, Kai Arulkumaran, Daniel C. Alexander, Antonio Criminisi, Aditya V. Nori

Proceedings of the 36th International Conference on Machine Learning, 2019

AlphaStar: an evolutionary computation perspective.

Kai Arulkumaran, Antoine Cully, Julian Togelius

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019

2018

Generative Adversarial Networks: An Overview.

Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, Anil A. Bharath

IEEE Signal Process. Mag., 2018

Variational Inference for Data-Efficient Model Learning in POMDPs.

Sebastian Tschiatschek, Kai Arulkumaran, Jan Stühmer, Katja Hofmann

CoRR, 2018

2017

Deep Reinforcement Learning: A Brief Survey.

Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath

IEEE Signal Process. Mag., 2017

On denoising autoencoders trained to minimise binary cross-entropy.

Antonia Creswell, Kai Arulkumaran, Anil A. Bharath

CoRR, 2017

A Brief Survey of Deep Reinforcement Learning.

Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath

CoRR, 2017

2016

An assistive haptic interface for appearance-based indoor navigation.

Jose Rivera-Rubio, Kai Arulkumaran, Hemang Rishi, Ioannis Alexiou, Anil A. Bharath

Comput. Vis. Image Underst., 2016

Towards Deep Symbolic Reinforcement Learning.

Marta Garnelo, Kai Arulkumaran, Murray Shanahan

CoRR, 2016

Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders.

Nat Dilokthanakul, Pedro A. M. Mediano, Marta Garnelo, Matthew C. H. Lee, Hugh Salimbeni, Kai Arulkumaran, Murray Shanahan

CoRR, 2016

Classifying Options for Deep Reinforcement Learning.

Kai Arulkumaran, Nat Dilokthanakul, Murray Shanahan, Anil Anthony Bharath

CoRR, 2016

Improving Sampling from Generative Autoencoders with Markov Chains.

Kai Arulkumaran, Antonia Creswell, Anil Anthony Bharath

CoRR, 2016