94 citations · 165 across the 11 of their papers we have counts for
14 papers
Learning Aggregation Functions
Giovanni Pellegrini, Alessandro Tibo, Paolo Frasconi +2
Learning on sets is increasingly gaining attention in the machine learning community, due to its widespread applicability. Typically, representations over sets are computed by usin…
Approximating Euclidean by Imprecise Markov Decision Processes
Manfred Jaeger, Giorgio Bacci, Giovanni Bacci +2
Euclidean Markov decision processes are a powerful tool for modeling control problems under uncertainty over continuous domains. Finite state imprecise, Markov decision processes c…
A general framework for defining and optimizing robustness
Alessandro Tibo, Manfred Jaeger, Kim G. Larsen
Robustness of neural networks has recently attracted a great amount of interest. The many investigations in this area lack a precise common foundation of robustness concepts. There…
A Complete Characterization of Projectivity for Statistical Relational Models
Manfred Jaeger, Oliver Schulte
A generative probabilistic model for relational data consists of a family of probability distributions for relational structures over domains of different sizes. In most existing s…
Learning and Interpreting Multi-Multi-Instance Learning Networks
Alessandro Tibo, Manfred Jaeger, Paolo Frasconi
We introduce an extension of the multi-instance learning problem where examples are organized as nested bags of instances (e.g., a document could be represented as a bag of sentenc…
Inference, Learning, and Population Size: Projectivity for SRL Models
Manfred Jaeger, Oliver Schulte
A subtle difference between propositional and relational data is that in many relational models, marginal probabilities depend on the population or domain size. This paper connects…