A key prerequisite to optimal reasoning under uncertainty in intelligent systems is to start with good class probability estimates. This paper improves on the current best probabi...
Existing work shows that classic decision trees have inherent deficiencies in obtaining a good probability-based ranking (e.g. AUC). This paper aims to improve the ranking perfor...
Importance sampling is a popular approach to estimate rare event failures of SRAM cells. We propose to improve importance sampling by probability collectives. First, we use “Kul...
Fang Gong, Sina Basir-Kazeruni, Lara Dolecek, Lei ...
Abstract. In this work we investigate several issues in order to improve the performance of probabilistic estimation trees (PETs). First, we derive a new probability smoothing that...
Uncertain inference is a probabilistic generalisation of the logical view on databases, ranking documents according to their probabilities that they logically imply the query. For ...