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ORCIDAlexandre Maraval
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2020 – today
- 2023
[c1]Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit, Haitham Bou-Ammar:
End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes. NeurIPS 2023- [i4]Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit, Haitham Bou-Ammar:
End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes. CoRR abs/2305.15930 (2023) - 2022
- [j1]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: 1269-1349 (2022) - [i3]Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit, Rasul Tutunov, Jun Wang, Haitham Bou-Ammar:
Sample-Efficient Optimisation with Probabilistic Transformer Surrogates. CoRR abs/2205.13902 (2022) - 2021
- [i2]Vincent Moens, Hang Ren, Alexandre Maraval, Rasul Tutunov, Jun Wang, Haitham Ammar:
Efficient Semi-Implicit Variational Inference. CoRR abs/2101.06070 (2021) - [i1]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:
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning. CoRR abs/2106.03609 (2021)
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