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Found 9 papers, 2 papers with code
Mapping the Multiverse of Latent Representations
Echoing recent calls to counter reliability and robustness concerns in machine learning via multiverse analysis, we present PRESTO, a principled framework for mapping the multiverse of machine-learning models that rely on latent representations.
Evaluating the "Learning on Graphs" Conference Experience
With machine learning conferences growing ever larger, and reviewing processes becoming increasingly elaborate, more data-driven insights into their workings are required.
Ollivier-Ricci Curvature for Hypergraphs: A Unified Framework
Bridging geometry and topology, curvature is a powerful and expressive invariant.
All the World's a (Hyper)Graph: A Data Drama
We introduce Hyperbard, a dataset of diverse relational data representations derived from Shakespeare's plays.
Sharing and Caring: Creating a Culture of Constructive Criticism in Computational Legal Studies
We introduce seven foundational principles for creating a culture of constructive criticism in computational legal studies.
Differentially Describing Groups of Graphs
How does neural connectivity in autistic children differ from neural connectivity in healthy children or autistic youths?
Law Smells: Defining and Detecting Problematic Patterns in Legal Drafting
Building on the computer science concept of code smells, we initiate the study of law smells, i. e., patterns in legal texts that pose threats to the comprehensibility and maintainability of the law.
Simplify Your Law: Using Information Theory to Deduplicate Legal Documents
Textual redundancy is one of the main challenges to ensuring that legal texts remain comprehensible and maintainable.
Graph Similarity Description: How Are These Graphs Similar?
We formalize this problem as a model selection task using the Minimum Description Length principle, capturing the similarity of the input graphs in a common model and the differences between them in transformations to individual models.
