Publications

Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes

Katarzyna Kobalczyk*, Claudio Fanconi*, Hao Sun, Mihaela van der Schaar

Preprint

Discovering Preference Optimization Algorithms with and for Large Language Models

Chris Lu*, Samuel Holt*, Claudio Fanconi*, Alex J. Chan, Jakob Foerster‡, Mihaela van der Schaar‡, Robert Tjarko Lange‡

NeurIPS 2024 & ICML 2024 Workshop on AutoRL

Applying Natural Language Processing to Patient Messages to Identify Depression Concerns in Cancer Patients

Marieke van Buchem, Anne de Hond, Claudio Fanconi, Vaibhavi Shah, Max Schuessler, Ilse Kant, Ewout W Steyerberg, Tina Hernandez-Boussard

Journal of the American Medical Informatics Association

Predicting Depression Risk in Patients With Cancer Using Multimodal Data - Algorithm Development Study

Anne de Hond, Marieke van Buchem, Claudio Fanconi, Mohana Roy, Douglas Blayney, Ilse Kant, Ewout Steyerberg, Tina Hernandez-Boussard

JMIR Medical Informatics, 2024 Jan 18

This Reads Like That - Deep Learning for Interpretable Natural Language Processing

Claudio Fanconi*, Moritz Vandenhirtz*, Severin Husmann, Julia E. Vogt

EMNLP 2023

A Bayesian Approach to Predictive Uncertainty in Chemotherapy Patients at Risk of Acute Care Use

Claudio Fanconi, Anne de Hond, Dylan Peterson, Angelo Capodici, Tina Hernandez-Boussard

The Lancet eBioMedicine, Volume 92, 2023

Natural Language Processing Methods to Identify Oncology Patients at High Risk for Acute Care with Clinical Notes

Claudio Fanconi, Marieke van Buchem, Tina Hernandez-Boussard

AMIA 2023 Informatics Summit

Predict, Diagnose, and Treat Chronic Kidney Disease with Machine Learning: a Systematic Literature Review

Francesco Sanmarchi, Claudio Fanconi, Davide Golinelli, Davide Gori, Tina Hernandez-Boussard, Angelo Capodici

Journal of Nephrology

This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks

Adrian Hoffmann*, Claudio Fanconi*, Rahul Rade*, Jonas Kohler

ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI