This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of fini...
This Chapter presents the PASCAL1 Evaluating Predictive Uncertainty Challenge, introduces the contributed Chapters by the participants who obtained outstanding results, and provide...
Models for sequential decision making under uncertainty (e.g., Markov decision processes,or MDPs) have beenstudied in operations research for decades. The recent incorporation of ...
This paper is concerned with information theoretic "metrics" for comparing two dynamical systems. Following the recent work of Tryphon Georgiou [1], we outline a predicti...
The classification problem of determining if a surveillance camera sees persons is tackled with two neural models: the Self-Organizing Map (SOM) with supervision as in a classical ...