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Found 6 papers, 4 papers with code
Conformalized Deep Splines for Optimal and Efficient Prediction Sets
SPICE is compatible with two different efficient-to-compute conformal scores, one oracle-optimal for marginal coverage (SPICE-ND) and the other asymptotically optimal for conditional coverage (SPICE-HPD).
Complex Preferences for Different Convergent Priors in Discrete Graph Diffusion
Diffusion models have achieved state-of-the-art performance in generating many different kinds of data, including images, text, and videos.
GraphGUIDE: interpretable and controllable conditional graph generation with discrete Bernoulli diffusion
Our framework for graph diffusion can have a large impact on the interpretable conditional generation of graphs, including the generation of drug-like molecules with desired properties in a way which is informed by experimental evidence.
Improving Graph Generation by Restricting Graph Bandwidth
However, one of the main limitations of existing methods is their large output space, which limits generation scalability and hinders accurate modeling of the underlying distribution.
Conditional Diffusion with Less Explicit Guidance via Model Predictive Control
How much explicit guidance is necessary for conditional diffusion?
Patient Contrastive Learning: a Performant, Expressive, and Practical Approach to ECG Modeling
Supervised machine learning applications in health care are often limited due to a scarcity of labeled training data.
