activity
20152026
most citedDeep Counterfactual Networks with Propensity-Dropout

48 citations · 278 across the 74 of their papers we have counts for

collaborators
Showing cs.CLShow all

5 papers · 1 filter

cs.CL2025

Visualizing token importance for black-box language models

Paulius Rauba, Qiyao Wei, Mihaela van der Schaar

We consider the problem of auditing black-box large language models (LLMs) to ensure they behave reliably when deployed in production settings, particularly in high-stakes domains…

cs.CL20251 cited

Continuously Updating Digital Twins using Large Language Models

Harry Amad, Nicolás Astorga, Mihaela van der Schaar

Digital twins are models of real-world systems that can simulate their dynamics in response to potential actions. In complex settings, the state and action variables, and available…

cs.CL2024

Retrieval Augmented Thought Process for Private Data Handling in Healthcare

Thomas Pouplin, Hao Sun, Samuel Holt +1

Large Language Models (LLMs) have demonstrated the strong potential to assist both clinicians and the general public with their extensive medical knowledge. However, their applicat…

cs.CL2023

Redefining Digital Health Interfaces with Large Language Models

Fergus Imrie, Paulius Rauba, Mihaela van der Schaar

Digital health tools have the potential to significantly improve the delivery of healthcare services. However, their adoption remains comparatively limited due, in part, to challen…

cs.CL2023

Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL

Hao Sun, Alihan Hüyük, Mihaela van der Schaar

In this study, we aim to enhance the arithmetic reasoning ability of Large Language Models (LLMs) through zero-shot prompt optimization. We identify a previously overlooked objecti…