activity
20222024
most citedPOETREE: Interpretable Policy Learning with Adaptive Decision Trees

3 citations · 6 across the 7 of their papers we have counts for

collaborators

7 papers

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.CL2024

West-of-N: Synthetic Preferences for Self-Improving Reward Models

Alizée Pace, Jonathan Mallinson, Eric Malmi +2

The success of reinforcement learning from human feedback (RLHF) in language model alignment is strongly dependent on the quality of the underlying reward model. In this paper, we…

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…

cs.LG2023★ 2 cited

Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding

Alizée Pace, Hugo Yèche, Bernhard Schölkopf +2

A prominent challenge of offline reinforcement learning (RL) is the issue of hidden confounding: unobserved variables may influence both the actions taken by the agent and the obse…

cs.LG2022★ 1 cited

Temporal Label Smoothing for Early Event Prediction

Hugo Yèche, Alizée Pace, Gunnar Rätsch +1

Models that can predict the occurrence of events ahead of time with low false-alarm rates are critical to the acceptance of decision support systems in the medical community. This…

cs.LG2022★ 3 cited

POETREE: Interpretable Policy Learning with Adaptive Decision Trees

Alizée Pace, Alex J. Chan, Mihaela van der Schaar

Building models of human decision-making from observed behaviour is critical to better understand, diagnose and support real-world policies such as clinical care. As established po…