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Found 6 papers, 1 papers with code
Sketches for Time-Dependent Machine Learning
Time series data can be subject to changes in the underlying process that generates them and, because of these changes, models built on old samples can become obsolete or perform poorly.
Characterizing Transactional Databases for Frequent Itemset Mining
This paper presents a study of the characteristics of transactional databases used in frequent itemset mining.
Synthetic Dataset Generation with Itemset-Based Generative Models
This paper proposes three different data generators, tailored to transactional datasets, based on existing itemset-based generative models.
A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization
In this paper we design and evaluate a Deep-Reinforcement Learning agent that optimizes routing.
Identifiability and Transportability in Dynamic Causal Networks
In this paper we propose a causal analog to the purely observational Dynamic Bayesian Networks, which we call Dynamic Causal Networks.
Learning Definite Horn Formulas from Closure Queries
A definite Horn theory is a set of n-dimensional Boolean vectors whose characteristic function is expressible as a definite Horn formula, that is, as conjunction of definite Horn clauses.
