56 citations · 57 across the 13 of their papers we have counts for
6 papers · 1 filter
The Alignment Auditor: A Bayesian Framework for Verifying and Refining LLM Objectives
Matthieu Bou, Nyal Patel, Arjun Jagota +2
The objectives that Large Language Models (LLMs) implicitly optimize remain dangerously opaque, making trustworthy alignment and auditing a grand challenge. While Inverse Reinforce…
Learning from Failures: Understanding LLM Alignment through Failure-Aware Inverse RL
Nyal Patel, Matthieu Bou, Arjun Jagota +2
Reinforcement Learning from Human Feedback (RLHF) aligns Large Language Models (LLMs) with human preferences, yet the underlying reward signals they internalize remain hidden, posi…
Improving ARDS Diagnosis Through Context-Aware Concept Bottleneck Models
Anish Narain, Ritam Majumdar, Nikita Narayanan +2
Large, publicly available clinical datasets have emerged as a novel resource for understanding disease heterogeneity and to explore personalization of therapy. These datasets are d…
Causal Bayesian Optimization with Unknown Graphs
Jean Durand, Yashas Annadani, Stefan Bauer +1
Causal Bayesian Optimization (CBO) is a methodology designed to optimize an outcome variable by leveraging known causal relationships through targeted interventions. Traditional CB…
Do regularization methods for shortcut mitigation work as intended?
Haoyang Hong, Ioanna Papanikolaou, Sonali Parbhoo
Mitigating shortcuts, where models exploit spurious correlations in training data, remains a significant challenge for improving generalization. Regularization methods have been pr…
Feature Importance Depends on Properties of the Data: Towards Choosing the Correct Explanations for Your Data and Decision Trees based Models
Célia Wafa Ayad, Thomas Bonnier, Benjamin Bosch +2
In order to ensure the reliability of the explanations of machine learning models, it is crucial to establish their advantages and limits and in which case each of these methods ou…