Search Results for author:
Found 7 papers, 1 papers with code
On Distributed Larger-Than-Memory Subset Selection With Pairwise Submodular Functions
In this paper, we relax the requirement of having a central machine for the target subset by proposing a novel distributed bounding algorithm with provable approximation guarantees.
Modyn: A Platform for Model Training on Dynamic Datasets With Sample-Level Data Selection
Machine learning training data is often dynamic in real-world use cases, i. e., data is added or removed and may experience distribution shifts over time.
What's Wrong with Deep Learning in Tree Search for Combinatorial Optimization
Second, using our benchmark suite, we conduct an in-depth analysis of the popular guided tree search algorithm by Li et al. [NeurIPS 2018], testing various configurations on small and large synthetic and real-world graphs.
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.
What’s Wrong with Deep Learning in Tree Search for Combinatorial Optimization
Second, using our benchmark suite, we conduct an in-depth analysis of the popular guided tree search algorithm by Li et al. [NeurIPS 2018], testing various configurations on small and large synthetic and real-world graphs.
Maps for Learning Indexable Classes
We study learning of indexed families from positive data where a learner can freely choose a hypothesis space (with uniformly decidable membership) comprising at least the languages to be learned.
Learning Languages with Decidable Hypotheses
This so-called $W$-index allows for naming arbitrary computably enumerable languages, with the drawback that even the membership problem is undecidable.
