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2020 – today
- 2024
[i5]Antoine Grosnit, Alexandre Maraval, James Doran, Giuseppe Paolo, Albert Thomas, Refinath Shahul Hameed Nabeezath Beevi, Jonas Gonzalez, Khyati Khandelwal, Ignacio Iacobacci, Abdelhakim Benechehab, Hamza Cherkaoui, Youssef Attia El Hili, Kun Shao, Jianye Hao, Jun Yao, Balázs Kégl, Haitham Bou-Ammar, Jun Wang:
Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level. CoRR abs/2411.03562 (2024)- 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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