Alexander I. Cowen-Rivers

According to our database, Alexander I. Cowen-Rivers authored at least 21 papers between 2018 and 2023.

Collaborative distances:

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

2023

Pushing The Limits of Sample-Efficient Optimisation.

Alexander Imani Cowen-Rivers

PhD thesis, 2023

2022

SAMBA: safe model-based & active reinforcement learning.

Alexander I. Cowen-Rivers, Daniel Palenicek, Vincent Moens, Mohammed Amin Abdullah, Aivar Sootla, Jun Wang, Haitham Bou-Ammar

Mach. Learn., 2022

HEBO: An Empirical Study of Assumptions in Bayesian Optimisation.

Alexander I. Cowen-Rivers, Wenlong Lyu, Rasul Tutunov, Zhi Wang, Antoine Grosnit, Ryan-Rhys Griffiths, Alexandre Max Maraval, Jianye Hao, Jun Wang, Jan Peters, Haitham Bou-Ammar

J. Artif. Intell. Res., 2022

Structured Q-learning For Antibody Design.

Alexander I. Cowen-Rivers, Philip John Gorinski, Aivar Sootla, Asif Khan, Furui Liu, Jun Wang, Jan Peters, Haitham Bou-Ammar

CoRR, 2022

AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation.

Mohammad Asif Khan, Alexander I. Cowen-Rivers, Derrick-Goh-Xin Deik, Antoine Grosnit, Kamil Dreczkowski, Philippe A. Robert, Victor Greiff, Rasul Tutunov, Dany Bou-Ammar, Jun Wang, Haitham Bou-Ammar

CoRR, 2022

Learning Geometric Constraints in Task and Motion Planning.

Tianyu Ren, Alexander Imani Cowen-Rivers, Haitham Bou-Ammar, Jan Peters

CoRR, 2022

Enhancing Safe Exploration Using Safety State Augmentation.

Aivar Sootla, Alexander I. Cowen-Rivers, Jun Wang, Haitham Bou-Ammar

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation.

Aivar Sootla, Alexander I. Cowen-Rivers, Taher Jafferjee, Ziyan Wang, David Henry Mguni, Jun Wang, Haitham Ammar

Proceedings of the International Conference on Machine Learning, 2022

2021

Are We Forgetting about Compositional Optimisers in Bayesian Optimisation?

Antoine Grosnit, Alexander I. Cowen-Rivers, Rasul Tutunov, Ryan-Rhys Griffiths, Jun Wang, Haitham Bou-Ammar

J. Mach. Learn. Res., 2021

High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning.

Antoine Grosnit, Rasul Tutunov, Alexandre Max Maraval, Ryan-Rhys Griffiths, Alexander I. Cowen-Rivers, Lin Yang, Lin Zhu, Wenlong Lyu, Zhitang Chen, Jun Wang, Jan Peters, Haitham Bou-Ammar

CoRR, 2021

2020

HEBO: Heteroscedastic Evolutionary Bayesian Optimisation.

Alexander I. Cowen-Rivers, Wenlong Lyu, Zhi Wang, Rasul Tutunov, Jianye Hao, Jun Wang, Haitham Bou-Ammar

CoRR, 2020

SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving.

Ming Zhou, Jun Luo, Julian Villela, Yaodong Yang, David Rusu, Jiayu Miao, Weinan Zhang, Montgomery Alban, Iman Fadakar, Zheng Chen, Aurora Chongxi Huang, Ying Wen, Kimia Hassanzadeh, Daniel Graves, Dong Chen, Zhengbang Zhu, Nhat M. Nguyen, Mohamed Elsayed, Kun Shao, Sanjeevan Ahilan, Baokuan Zhang, Jiannan Wu, Zhengang Fu, Kasra Rezaee, Peyman Yadmellat, Mohsen Rohani, Nicolas Perez Nieves, Yihan Ni, Seyedershad Banijamali, Alexander I. Cowen-Rivers, Zheng Tian, Daniel Palenicek, Haitham Bou-Ammar, Hongbo Zhang, Wulong Liu, Jianye Hao, Jun Wang

CoRR, 2020

Emergent Communication with World Models.

Alexander I. Cowen-Rivers, Jason Naradowsky

CoRR, 2020

SMARTS: An Open-Source Scalable Multi-Agent RL Training School for Autonomous Driving.

Ming Zhou, Jun Luo, Julian Villela, Yaodong Yang, David Rusu, Jiayu Miao, Weinan Zhang, Montgomery Alban, Iman Fadakar, Zheng Chen, Chongxi Huang, Ying Wen, Kimia Hassanzadeh, Daniel Graves, Zhengbang Zhu, Yihan Ni, Nhat M. Nguyen, Mohamed Elsayed, Haitham Ammar, Alexander I. Cowen-Rivers, Sanjeevan Ahilan, Zheng Tian, Daniel Palenicek, Kasra Rezaee, Peyman Yadmellat, Kun Shao, Dong Chen, Baokuan Zhang, Hongbo Zhang, Jianye Hao, Wulong Liu, Jun Wang

Proceedings of the 4th Conference on Robot Learning, 2020

2019

RL: Generic reinforcement learning codebase in TensorFlow.

Bryan M. Li, Alexander I. Cowen-Rivers, Piotr Kozakowski, David Tao, Siddhartha Kamalakara, Nitarshan Rajkumar, Hariharan Sezhiyan, Sicong Huang, Aidan N. Gomez

J. Open Source Softw., 2019

Neural Variational Inference For Estimating Uncertainty in Knowledge Graph Embeddings.

Alexander I. Cowen-Rivers, Pasquale Minervini, Tim Rocktäschel, Matko Bosnjak, Sebastian Riedel, Jun Wang

CoRR, 2019

Infer Your Enemies and Know Yourself, Learning in Real-Time Bidding with Partially Observable Opponents.

Manxing Du, Alexander I. Cowen-Rivers, Ying Wen, Phu Sakulwongtana, Jun Wang, Mats Brorsson, Radu State

CoRR, 2019

Neural Variational Inference For Estimating Knowledge Graph Embedding Uncertainty.

Alexander I. Cowen-Rivers, Pasquale Minervini, Sebastian Riedel, Tim Rocktäschel, Jun Wang, Matko Bosnjak

Proceedings of the 2019 International Workshop on Neural-Symbolic Learning and Reasoning (NeSy 2019), 2019

Know Your Enemies and Know Yourself in the Real-Time Bidding Function Optimisation.

Manxing Du, Alexander I. Cowen-Rivers, Ying Wen, Phu Sakulwongtana, Jun Wang, Mats Brorsson, Radu State

Proceedings of the 2019 International Conference on Data Mining Workshops, 2019

2018

Summer Research Report: Towards Incremental Lazard Cylindrical Algebraic Decomposition.

Alexander Imani Cowen-Rivers, Matthew England

CoRR, 2018

Towards Incremental Cylindrical Algebraic Decomposition in Maple.

Alexander I. Cowen-Rivers, Matthew England

Proceedings of the 3rd Workshop on Satisfiability Checking and Symbolic Computation co-located with Federated Logic Conference, 2018