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cs.LG2024
Uncertainty-Penalized Direct Preference Optimization
Sam Houliston, Alizée Pace, Alexander Immer +1
Aligning Large Language Models (LLMs) to human preferences in content, style, and presentation is challenging, in part because preferences are varied, context-dependent, and someti…
cs.LG2024
Preference Elicitation for Offline Reinforcement Learning
Alizée Pace, Bernhard Schölkopf, Gunnar Rätsch +1
Applying reinforcement learning (RL) to real-world problems is often made challenging by the inability to interact with the environment and the difficulty of designing reward funct…
cs.LG2023
On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Series
Rita Kuznetsova, Alizée Pace, Manuel Burger +2
Recent advances in deep learning architectures for sequence modeling have not fully transferred to tasks handling time-series from electronic health records. In particular, in prob…