Antoine Grosnit

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 19

Top Topics

Posterior Probability

Bayesian Inference

Metric Learning

Top Venues

J. Mach. Learn. Res.

Publications

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
ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning. CoRR (2024)
Vahan Arsenyan, Antoine Grosnit, Haitham Bou-Ammar
Contextual Causal Bayesian Optimisation. CoRR (2023)
Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit, Haitham Bou-Ammar
End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes. CoRR (2023)
Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit, Haitham Bou-Ammar
End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes. NeurIPS (2023)
Kamil Dreczkowski, Antoine Grosnit, Haitham Bou-Ammar
Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. CoRR (2023)
Kamil Dreczkowski, Antoine Grosnit, Haitham Bou-Ammar
Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. NeurIPS (2023)
Antoine Grosnit, Matthieu Zimmer, Rasul Tutunov, Xing Li, Lei Chen, Fan Yang, Mingxuan Yuan, Haitham Bou-Ammar
Lightweight Structural Choices Operator for Technology Mapping. DAC (2023)
Rasul Tutunov, Antoine Grosnit, Juliusz Ziomek, Jun Wang, Haitham Bou-Ammar
Why Can Large Language Models Generate Correct Chain-of-Thoughts? CoRR (2023)
Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit, Rasul Tutunov, Jun Wang, Haitham Bou-Ammar
Sample-Efficient Optimisation with Probabilistic Transformer Surrogates. CoRR (2022)
Cédric Malherbe, Antoine Grosnit, Rasul Tutunov, Haitham Bou-Ammar, Jun Wang
Optimistic Tree Searches for Combinatorial Black-Box Optimization. NeurIPS (2022)
Asif Khan, Alexander Imani 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
AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation. CoRR (2022)
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
HEBO: An Empirical Study of Assumptions in Bayesian Optimisation. J. Artif. Intell. Res. 74 (2022)
Antoine Grosnit, Cédric Malherbe, Rasul Tutunov, Xingchen Wan, Jun Wang, Haitham Bou-Ammar
BOiLS: Bayesian Optimisation for Logic Synthesis. DATE (2022)
Antoine Grosnit, Desmond Cai, Laura Wynter
Decentralized Deterministic Multi-Agent Reinforcement Learning. CoRR (2021)
Antoine Grosnit, Alexander Imani Cowen-Rivers, Rasul Tutunov, Ryan-Rhys Griffiths, Jun Wang, Haitham Bou-Ammar
Are We Forgetting about Compositional Optimisers in Bayesian Optimisation? J. Mach. Learn. Res. 22 (2021)
Antoine Grosnit, Cédric Malherbe, Rasul Tutunov, Xingchen Wan, Jun Wang, Haitham Bou-Ammar
BOiLS: Bayesian Optimisation for Logic Synthesis. CoRR (2021)
Antoine Grosnit, Rasul Tutunov, Alexandre Max Maraval, Ryan-Rhys Griffiths, Alexander Imani Cowen-Rivers, Lin Yang, Lin Zhu, Wenlong Lyu, Zhitang Chen, Jun Wang, Jan Peters, Haitham Bou-Ammar
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning. CoRR (2021)
Antoine Grosnit, Desmond Cai, Laura Wynter
Decentralized Deterministic Multi-Agent Reinforcement Learning. CDC (2021)
Antoine Grosnit, Alexander Imani Cowen-Rivers, Rasul Tutunov, Ryan-Rhys Griffiths, Jun Wang, Haitham Bou-Ammar
Are we Forgetting about Compositional Optimisers in Bayesian Optimisation? CoRR (2020)