19 citations · 32 across the 9 of their papers we have counts for
9 papers
1000 Rallies: An Event-Camera Dataset and Real-Time Learned Ball-State Estimation for Robotic Table Tennis
Raphaela Kreiser, Asude Aydin, Yin Bi +3
Robotic table tennis has emerged as a compelling benchmark for real-time robotic perception due to its fast ball dynamics and stringent timing requirements. Accurate, high-frequenc…
Tiny Autoregressive Recursive Models
Paulius Rauba, Claudio Fanconi, Mihaela van der Schaar
Tiny Recursive Models (TRMs) have recently demonstrated remarkable performance on ARC-AGI, showing that very small models can compete against large foundation models through a two-…
Learning Reasoning Rewards from Expert Demonstrations with Inverse Reinforcement Learning
Claudio Fanconi, Nicolás Astorga, Mihaela van der Schaar
Teaching large language models (LLMs) to reason during post-training typically relies on reinforcement learning with explicit outcome- or process-based reward functions. However, i…
Cascaded Language Models for Cost-effective Human-AI Decision-Making
Claudio Fanconi, Mihaela van der Schaar
A challenge in human-AI decision-making is to balance three factors: the correctness of predictions, the cost of knowledge and reasoning complexity, and the confidence about whethe…
Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes
Katarzyna Kobalczyk, Claudio Fanconi, Hao Sun +1
As large language models (LLMs) become increasingly embedded in everyday applications, ensuring their alignment with the diverse preferences of individual users has become a critic…
Discovering Preference Optimization Algorithms with and for Large Language Models
Chris Lu, Samuel Holt, Claudio Fanconi +4
Offline preference optimization is a key method for enhancing and controlling the quality of Large Language Model (LLM) outputs. Typically, preference optimization is approached as…
This Reads Like That: Deep Learning for Interpretable Natural Language Processing
Claudio Fanconi, Moritz Vandenhirtz, Severin Husmann +1
Prototype learning, a popular machine learning method designed for inherently interpretable decisions, leverages similarities to learned prototypes for classifying new data. While…
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
Clinical notes are an essential component of a health record. This paper evaluates how natural language processing (NLP) can be used to identify the risk of acute care use (ACU) in…
This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks
Adrian Hoffmann, Claudio Fanconi, Rahul Rade +1
Deep neural networks that yield human interpretable decisions by architectural design have lately become an increasingly popular alternative to post hoc interpretation of tradition…