Uncertainty
Unscrambling disease progression at scale: fast inference of event permutations with optimal transport
Disease progression models infer group-level temporal trajectories of change in patients' features as a chronic degenerative condition plays out.
ExPT: Synthetic Pretraining for Few-Shot Experimental Design
In this work, we address the more challenging yet realistic setting of few-shot experimental design, where only a few labeled data points of input designs and their corresponding values are available.
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing Simon Buchholz
Therefore, it's important to study whether we can and also how to
Energy Discrepancies: A Score-Independent Loss for Energy-Based Models
We propose a novel loss function called Energy Discrepancy (ED) which does not rely on the computation of scores or expensive Markov chain Monte Carlo.
Robust Gaussian Processes via Relevance Pursuit
We compare the model's performance relative to other approaches on diverse regression and Bayesian optimization tasks, including the challenging but common
Detecting and Measuring Confounding Using Causal Mechanism Shifts
Existing methods frequently assume causal sufficiency, disregarding the presence of unobserved confounding variables.