Kai Arulkumaran

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 42

Top Topics

Privileged Information

Variational Inference

Reinforcement Learning

Maximum Entropy

Top Venues

GECCO Companion

Publications

Motoshige Sato, Kenichi Tomeoka, Ilya Horiguchi, Kai Arulkumaran, Ryota Kanai, Shuntaro Sasai
Scaling Law in Neural Data: Non-Invasive Speech Decoding with 175 Hours of EEG Data. CoRR (2024)
Kai Arulkumaran, Marina Di Vincenzo, Rousslan Fernand Julien Dossa, Shogo Akiyama, Dan Ogawa Lillrank, Motoshige Sato, Kenichi Tomeoka, Shuntaro Sasai
A comparison of visual and auditory EEG interfaces for robot multi-stage task control. Frontiers Robotics AI 11 (2024)
Roberto Gallotta, Kai Arulkumaran, Lisa B. Soros
Preference-Learning Emitters for Mixed-Initiative Quality-Diversity Algorithms. IEEE Trans. Games 16 (2) (2024)
Kai Arulkumaran, Dan Ogawa Lillrank
A Pragmatic Look at Deep Imitation Learning. ACML (2023)
Roberto Gallotta, Kai Arulkumaran, Lisa B. Soros
Surrogate Infeasible Fitness Acquirement FI-2Pop for Procedural Content Generation. CoG (2022)
Roberto Gallotta, Kai Arulkumaran, Lisa B. Soros
Preference-Learning Emitters for Mixed-Initiative Quality-Diversity Algorithms. CoRR (2022)
Tianhong Dai, Kai Arulkumaran, Tamara Gerbert, Samyakh Tukra, Feryal Behbahani, Anil Anthony Bharath
Analysing deep reinforcement learning agents trained with domain randomisation. Neurocomputing 493 (2022)
Kai Arulkumaran, Dylan R. Ashley, Jürgen Schmidhuber, Rupesh Kumar Srivastava
All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL. CoRR (2022)
Arthur Juliani, Kai Arulkumaran, Shuntaro Sasai, Ryota Kanai
On the link between conscious function and general intelligence in humans and machines. Trans. Mach. Learn. Res. 2022 (2022)
Kai Arulkumaran, Thu Nguyen-Phuoc
Minimal criterion artist collective. GECCO Companion (2022)
Roberto Gallotta, Kai Arulkumaran, Lisa B. Soros
Surrogate Infeasible Fitness Acquirement FI-2Pop for Procedural Content Generation. CoRR (2022)
Dylan R. Ashley, Kai Arulkumaran, Jürgen Schmidhuber, Rupesh Kumar Srivastava
Learning Relative Return Policies With Upside-Down Reinforcement Learning. CoRR (2022)
Arthur Juliani, Kai Arulkumaran, Shuntaro Sasai, Ryota Kanai
On the link between conscious function and general intelligence in humans and machines. CoRR (2022)
Roberto Gallotta, Kai Arulkumaran, Lisa B. Soros
Evolving spaceships with a hybrid L-system constrained optimisation evolutionary algorithm. GECCO Companion (2022)
Tianhong Dai, Hengyan Liu, Kai Arulkumaran, Guangyu Ren, Anil Anthony Bharath
Diversity-based Trajectory and Goal Selection with Hindsight Experience Replay. CoRR (2021)
Kai Arulkumaran, Dan Ogawa Lillrank
A Pragmatic Look at Deep Imitation Learning. CoRR (2021)
Tianhong Dai, Hengyan Liu, Kai Arulkumaran, Guangyu Ren, Anil Anthony Bharath
Diversity-Based Trajectory and Goal Selection with Hindsight Experience Replay. PRICAI (3) (2021)
Jess Whittlestone, Kai Arulkumaran, Matthew Crosby
The Societal Implications of Deep Reinforcement Learning. J. Artif. Intell. Res. 70 (2021)
Pierre-Alexandre Kamienny, Kai Arulkumaran, Feryal Behbahani, Wendelin Boehmer, Shimon Whiteson
Privileged Information Dropout in Reinforcement Learning. CoRR (2020)
Kevin Pan, Guillem Hurault, Kai Arulkumaran, Hywel C. Williams, Reiko J. Tanaka
EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis. MLMI@MICCAI (2020)
Manon Flageat, Kai Arulkumaran, Anil A. Bharath
Incorporating Human Priors into Deep Reinforcement Learning for Robotic Control. ESANN (2020)
Tianhong Dai, Magda Dubois, Kai Arulkumaran, Jonathan Campbell, Cher Bass, Benjamin Billot, Fatmatülzehra Uslu, Vincenzo De Paola, Claudia Clopath, Anil Anthony Bharath
Deep Reinforcement Learning for Subpixel Neural Tracking. MIDL (2019)
Marta Sarrico, Kai Arulkumaran, Andrea Agostinelli, Pierre Richemond, Anil Anthony Bharath
Sample-Efficient Reinforcement Learning with Maximum Entropy Mellowmax Episodic Control. CoRR (2019)
Tianhong Dai, Kai Arulkumaran, Samyakh Tukra, Feryal Behbahani, Anil Anthony Bharath
Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation. CoRR (2019)
Kai Arulkumaran, Antoine Cully, Julian Togelius
AlphaStar: An Evolutionary Computation Perspective. CoRR (2019)
Kai Arulkumaran, Antoine Cully, Julian Togelius
AlphaStar: an evolutionary computation perspective. GECCO (Companion) (2019)
Ryutaro Tanno, Kai Arulkumaran, Daniel C. Alexander, Antonio Criminisi, Aditya V. Nori
Adaptive Neural Trees. ICML (2019)
Cher Bass, Tianhong Dai, Benjamin Billot, Kai Arulkumaran, Antonia Creswell, Claudia Clopath, Vincenzo De Paola, Anil Anthony Bharath
Image Synthesis with a Convolutional Capsule Generative Adversarial Network. MIDL (2019)
Andrea Agostinelli, Kai Arulkumaran, Marta Sarrico, Pierre Richemond, Anil Anthony Bharath
Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-means. CoRR (2019)
Shafa Balaram, Kai Arulkumaran, Tianhong Dai, Anil Anthony Bharath
A Maximum Entropy Deep Reinforcement Learning Neural Tracker. MLMI@MICCAI (2019)
Sebastian Tschiatschek, Kai Arulkumaran, Jan Stühmer, Katja Hofmann
Variational Inference for Data-Efficient Model Learning in POMDPs. CoRR (2018)
Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, Anil A. Bharath
Generative Adversarial Networks: An Overview. IEEE Signal Process. Mag. 35 (1) (2018)
Ryutaro Tanno, Kai Arulkumaran, Daniel C. Alexander, Antonio Criminisi, Aditya V. Nori
Adaptive Neural Trees. CoRR (2018)
Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath
A Brief Survey of Deep Reinforcement Learning. CoRR (2017)
Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath
Deep Reinforcement Learning: A Brief Survey. IEEE Signal Process. Mag. 34 (6) (2017)
Antonia Creswell, Kai Arulkumaran, Anil A. Bharath
On denoising autoencoders trained to minimise binary cross-entropy. CoRR (2017)
Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, Anil A. Bharath
Generative Adversarial Networks: An Overview. CoRR (2017)
Marta Garnelo, Kai Arulkumaran, Murray Shanahan
Towards Deep Symbolic Reinforcement Learning. CoRR (2016)
Nat Dilokthanakul, Pedro A. M. Mediano, Marta Garnelo, Matthew C. H. Lee, Hugh Salimbeni, Kai Arulkumaran, Murray Shanahan
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders. CoRR (2016)
Kai Arulkumaran, Nat Dilokthanakul, Murray Shanahan, Anil Anthony Bharath
Classifying Options for Deep Reinforcement Learning. CoRR (2016)
Kai Arulkumaran, Antonia Creswell, Anil Anthony Bharath
Improving Sampling from Generative Autoencoders with Markov Chains. CoRR (2016)
Jose Rivera-Rubio, Kai Arulkumaran, Hemang Rishi, Ioannis Alexiou, Anil A. Bharath
An assistive haptic interface for appearance-based indoor navigation. Comput. Vis. Image Underst. 149 (2016)