Bayesian Inference
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.
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
Training Energy-Based Normalizing Flow with Score-Matching Objectives Chen-Hao Chao 1, Wei-Fang Sun 1,2, Yen-Chang Hsu
Our experimental results demonstrate that this approach significantly improves the training efficiency as compared to maximum likelihood training.
A Proof of Theorem
In this section, we provide proof for the disentanglement identifiability of the inferred exogenous variable. Our proof consists of three main components. Then we have ( f, T, ฮป) ( f, T, ฮป) . The conditional V AE, in this case, inherits all the properties of maximum likelihood estimation. The following proof is based on the reduction to absurdity.
