9 citations · 33 across the 9 of their papers we have counts for
11 papers
Do LLMs estimate uncertainty well in instruction-following?
Juyeon Heo, Miao Xiong, Christina Heinze-Deml +1
Large language models (LLMs) could be valuable personal AI agents across various domains, provided they can precisely follow user instructions. However, recent studies have shown s…
Do LLMs "know" internally when they follow instructions?
Juyeon Heo, Christina Heinze-Deml, Oussama Elachqar +5
Instruction-following is crucial for building AI agents with large language models (LLMs), as these models must adhere strictly to user-provided constraints and guidelines. However…
Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions
Juan L. Gamella, Armeen Taeb, Christina Heinze-Deml +1
We consider the problem of recovering the causal structure underlying observations from different experimental conditions when the targets of the interventions in each experiment a…
Learning and scoring Gaussian latent variable causal models with unknown additive interventions
Armeen Taeb, Juan L. Gamella, Christina Heinze-Deml +1
With observational data alone, causal structure learning is a challenging problem. The task becomes easier when having access to data collected from perturbations of the underlying…
Think before you act: A simple baseline for compositional generalization
Christina Heinze-Deml, Diane Bouchacourt
Contrarily to humans who have the ability to recombine familiar expressions to create novel ones, modern neural networks struggle to do so. This has been emphasized recently with t…
Active Invariant Causal Prediction: Experiment Selection through Stability
Juan L. Gamella, Christina Heinze-Deml
A fundamental difficulty of causal learning is that causal models can generally not be fully identified based on observational data only. Interventional data, that is, data origina…
Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness
Fanny Yang, Zuowen Wang, Christina Heinze-Deml
This work provides theoretical and empirical evidence that invariance-inducing regularizers can increase predictive accuracy for worst-case spatial transformations (spatial robustn…
Conditional Variance Penalties and Domain Shift Robustness
Christina Heinze-Deml, Nicolai Meinshausen
When training a deep neural network for image classification, one can broadly distinguish between two types of latent features of images that will drive the classification. We can…
Causal Structure Learning
Christina Heinze-Deml, Marloes H. Maathuis, Nicolai Meinshausen
Graphical models can represent a multivariate distribution in a convenient and accessible form as a graph. Causal models can be viewed as a special class of graphical models that n…
Invariant Causal Prediction for Nonlinear Models
Christina Heinze-Deml, Jonas Peters, Nicolai Meinshausen
An important problem in many domains is to predict how a system will respond to interventions. This task is inherently linked to estimating the system's underlying causal structure…
Preserving Differential Privacy Between Features in Distributed Estimation
Christina Heinze-Deml, Brian McWilliams, Nicolai Meinshausen
Privacy is crucial in many applications of machine learning. Legal, ethical and societal issues restrict the sharing of sensitive data making it difficult to learn from datasets th…