Antoine Grosnit

According to our database, Antoine Grosnit authored at least 15 papers between 2021 and 2024.

Collaborative distances:

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

2024

ShortCircuit: AlphaZero-Driven Circuit Design.

Dimitrios Tsaras, Antoine Grosnit, Lei Chen, Zhiyao Xie, Haitham Bou-Ammar, Mingxuan Yuan

CoRR, 2024

ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning.

Christopher E. Mower, Yuhui Wan, Hongzhan Yu, Antoine Grosnit, Jonas Gonzalez-Billandon, Matthieu Zimmer, Jinlong Wang, Xinyu Zhang, Yao Zhao, Anbang Zhai, Puze Liu, Davide Tateo, Cesar Cadena, Marco Hutter, Jan Peters, Guangjian Tian, Yuzheng Zhuang, Kun Shao, Xingyue Quan, Jianye Hao, Jun Wang, Haitham Bou-Ammar

CoRR, 2024

2023

Why Can Large Language Models Generate Correct Chain-of-Thoughts?

Rasul Tutunov, Antoine Grosnit, Juliusz Ziomek, Jun Wang, Haitham Bou-Ammar

CoRR, 2023

Contextual Causal Bayesian Optimisation.

Vahan Arsenyan, Antoine Grosnit, Haitham Bou-Ammar

CoRR, 2023

End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes.

Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit, Haitham Bou-Ammar

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization.

Kamil Dreczkowski, Antoine Grosnit, Haitham Bou-Ammar

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Lightweight Structural Choices Operator for Technology Mapping.

Antoine Grosnit, Matthieu Zimmer, Rasul Tutunov, Xing Li, Lei Chen, Fan Yang, Mingxuan Yuan, Haitham Bou-Ammar

Proceedings of the 60th ACM/IEEE Design Automation Conference, 2023

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

Sample-Efficient Optimisation with Probabilistic Transformer Surrogates.

Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit, Rasul Tutunov, Jun Wang, 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

Optimistic Tree Searches for Combinatorial Black-Box Optimization.

Cédric Malherbe, Antoine Grosnit, Rasul Tutunov, Haitham Bou-Ammar, Jun Wang

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

BOiLS: Bayesian Optimisation for Logic Synthesis.

Antoine Grosnit, Cédric Malherbe, Rasul Tutunov, Xingchen Wan, Jun Wang, Haitham Bou-Ammar

Proceedings of the 2022 Design, Automation & Test in Europe Conference & Exhibition, 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

Decentralized Deterministic Multi-Agent Reinforcement Learning.

Antoine Grosnit, Desmond Cai, Laura Wynter

Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021