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Kai Arulkumaran
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Books and Theses
- 2020
- [b1]Kai Arulkumaran:
Sample efficiency, transfer learning and interpretability for deep reinforcement learning. Imperial College London, UK, 2020
Journal Articles
- 2024
- [j8]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) - [j7]Roberto Gallotta
, Kai Arulkumaran
, Lisa B. Soros
:
Preference-Learning Emitters for Mixed-Initiative Quality-Diversity Algorithms. IEEE Trans. Games 16(2): 303-316 (2024) - 2022
- [j6]Tianhong Dai
, Kai Arulkumaran, Tamara Gerbert, Samyakh Tukra, Feryal M. P. Behbahani, Anil Anthony Bharath:
Analysing deep reinforcement learning agents trained with domain randomisation. Neurocomputing 493: 143-165 (2022) - [j5]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) - 2021
- [j4]Jess Whittlestone, Kai Arulkumaran, Matthew Crosby:
The Societal Implications of Deep Reinforcement Learning. J. Artif. Intell. Res. 70: 1003-1030 (2021) - 2018
- [j3]Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran
, Biswa Sengupta, Anil A. Bharath
:
Generative Adversarial Networks: An Overview. IEEE Signal Process. Mag. 35(1): 53-65 (2018) - 2017
- [j2]Kai Arulkumaran
, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath:
Deep Reinforcement Learning: A Brief Survey. IEEE Signal Process. Mag. 34(6): 26-38 (2017) - 2016
- [j1]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: 126-145 (2016)
Conference and Workshop Papers
- 2023
- [c12]Kai Arulkumaran, Dan Ogawa Lillrank:
A Pragmatic Look at Deep Imitation Learning. ACML 2023: 58-73 - 2022
- [c11]Roberto Gallotta
, Kai Arulkumaran, Lisa B. Soros:
Surrogate Infeasible Fitness Acquirement FI-2Pop for Procedural Content Generation. CoG 2022: 500-503 - [c10]Kai Arulkumaran, Thu Nguyen-Phuoc:
Minimal criterion artist collective. GECCO Companion 2022: 687-690 - [c9]Roberto Gallotta
, Kai Arulkumaran, Lisa B. Soros:
Evolving spaceships with a hybrid L-system constrained optimisation evolutionary algorithm. GECCO Companion 2022: 711-714 - 2021
- [c8]Tianhong Dai
, Hengyan Liu, Kai Arulkumaran, Guangyu Ren, Anil Anthony Bharath:
Diversity-Based Trajectory and Goal Selection with Hindsight Experience Replay. PRICAI (3) 2021: 32-45 - 2020
- [c7]Manon Flageat, Kai Arulkumaran, Anil A. Bharath:
Incorporating Human Priors into Deep Reinforcement Learning for Robotic Control. ESANN 2020: 229-234 - [c6]Kevin Pan
, Guillem Hurault
, Kai Arulkumaran
, Hywel C. Williams
, Reiko J. Tanaka
:
EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis. MLMI@MICCAI 2020: 220-230 - 2019
- [c5]Kai Arulkumaran, Antoine Cully
, Julian Togelius
:
AlphaStar: an evolutionary computation perspective. GECCO (Companion) 2019: 314-315 - [c4]Ryutaro Tanno, Kai Arulkumaran, Daniel C. Alexander, Antonio Criminisi, Aditya V. Nori:
Adaptive Neural Trees. ICML 2019: 6166-6175 - [c3]Shafa Balaram, Kai Arulkumaran, Tianhong Dai
, Anil Anthony Bharath:
A Maximum Entropy Deep Reinforcement Learning Neural Tracker. MLMI@MICCAI 2019: 400-408 - [c2]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: 39-62 - [c1]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: 130-150
Informal and Other Publications
- 2024
- [i22]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 abs/2407.07595 (2024) - 2022
- [i21]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 abs/2202.11960 (2022) - [i20]Dylan R. Ashley, Kai Arulkumaran, Jürgen Schmidhuber, Rupesh Kumar Srivastava:
Learning Relative Return Policies With Upside-Down Reinforcement Learning. CoRR abs/2202.12742 (2022) - [i19]Arthur Juliani, Kai Arulkumaran, Shuntaro Sasai, Ryota Kanai:
On the link between conscious function and general intelligence in humans and machines. CoRR abs/2204.05133 (2022) - [i18]Roberto Gallotta
, Kai Arulkumaran, Lisa B. Soros:
Surrogate Infeasible Fitness Acquirement FI-2Pop for Procedural Content Generation. CoRR abs/2205.05834 (2022) - [i17]Roberto Gallotta, Kai Arulkumaran, Lisa B. Soros:
Preference-Learning Emitters for Mixed-Initiative Quality-Diversity Algorithms. CoRR abs/2210.13839 (2022) - 2021
- [i16]Kai Arulkumaran, Dan Ogawa Lillrank:
A Pragmatic Look at Deep Imitation Learning. CoRR abs/2108.01867 (2021) - [i15]Tianhong Dai, Hengyan Liu, Kai Arulkumaran, Guangyu Ren, Anil Anthony Bharath:
Diversity-based Trajectory and Goal Selection with Hindsight Experience Replay. CoRR abs/2108.07887 (2021) - 2020
- [i14]Pierre-Alexandre Kamienny, Kai Arulkumaran, Feryal M. P. Behbahani, Wendelin Boehmer, Shimon Whiteson:
Privileged Information Dropout in Reinforcement Learning. CoRR abs/2005.09220 (2020) - 2019
- [i13]Kai Arulkumaran, Antoine Cully
, Julian Togelius:
AlphaStar: An Evolutionary Computation Perspective. CoRR abs/1902.01724 (2019) - [i12]Andrea Agostinelli, Kai Arulkumaran, Marta Sarrico, Pierre Richemond, Anil Anthony Bharath:
Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-means. CoRR abs/1911.09560 (2019) - [i11]Marta Sarrico, Kai Arulkumaran, Andrea Agostinelli, Pierre Richemond, Anil Anthony Bharath:
Sample-Efficient Reinforcement Learning with Maximum Entropy Mellowmax Episodic Control. CoRR abs/1911.09615 (2019) - [i10]Tianhong Dai, Kai Arulkumaran, Samyakh Tukra, Feryal M. P. Behbahani, Anil Anthony Bharath:
Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation. CoRR abs/1912.08324 (2019) - 2018
- [i9]Sebastian Tschiatschek, Kai Arulkumaran, Jan Stühmer, Katja Hofmann:
Variational Inference for Data-Efficient Model Learning in POMDPs. CoRR abs/1805.09281 (2018) - [i8]Ryutaro Tanno, Kai Arulkumaran, Daniel C. Alexander, Antonio Criminisi, Aditya V. Nori:
Adaptive Neural Trees. CoRR abs/1807.06699 (2018) - 2017
- [i7]Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath:
A Brief Survey of Deep Reinforcement Learning. CoRR abs/1708.05866 (2017) - [i6]Antonia Creswell, Kai Arulkumaran, Anil A. Bharath:
On denoising autoencoders trained to minimise binary cross-entropy. CoRR abs/1708.08487 (2017) - [i5]Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, Anil A. Bharath:
Generative Adversarial Networks: An Overview. CoRR abs/1710.07035 (2017) - 2016
- [i4]Kai Arulkumaran, Nat Dilokthanakul, Murray Shanahan, Anil Anthony Bharath:
Classifying Options for Deep Reinforcement Learning. CoRR abs/1604.08153 (2016) - [i3]Marta Garnelo, Kai Arulkumaran, Murray Shanahan:
Towards Deep Symbolic Reinforcement Learning. CoRR abs/1609.05518 (2016) - [i2]Kai Arulkumaran, Antonia Creswell, Anil Anthony Bharath:
Improving Sampling from Generative Autoencoders with Markov Chains. CoRR abs/1610.09296 (2016) - [i1]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 abs/1611.02648 (2016)
Coauthor Index
aka: Anil Anthony Bharath
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