| Can LLMs predict the convergence of Stochastic Gradient Descent? O Zekri, A Benechehab, I Redko ICML 2024 Workshop on In-Context Learning, 2024 | | 2024 |
| A Study of the Weighted Multi-step Loss Impact on the Predictive Error and the Return in MBRL A Benechehab, A Thomas, G Paolo, M Filippone, B Kégl I Can't Believe It's Not Better Workshop: Failure Modes of Sequential …, 2024 | | 2024 |
| Fair Model-Based Reinforcement Learning Comparisons with Explicit and Consistent Update Frequency A Thomas, A Benechehab, G Paolo, B Kégl The Third Blogpost Track at ICLR 2024, 2024 | | 2024 |
| A Multi-step Loss Function for Robust Learning of the Dynamics in Model-based Reinforcement Learning A Benechehab, A Thomas, G Paolo, M Filippone, B Kégl arXiv preprint arXiv:2402.03146, 2024 | | 2024 |
| Multi-timestep models for Model-based Reinforcement Learning A Benechehab, G Paolo, A Thomas, M Filippone, B Kégl arXiv preprint arXiv:2310.05672, 2023 | | 2023 |
| Deep autoregressive density nets vs neural ensembles for model-based offline reinforcement learning A Benechehab, A Thomas, B Kégl | | 2022 |
Balazs KEGLResearch scientist, Paris AI leader, Noah's Ark Lab, Huawei ResearchVerified email at huawei.com
