Constraint programming is a natural paradigm for many combinatorial optimisation problems. The complexity of constraint satisfaction for various forms of constraints has been wide...
David A. Cohen, Martin C. Cooper, Peter G. Jeavons...
Mitigating risk in decision-making has been a longstanding problem. Due to the mathematical challenge of its nonlinear nature, especially in adaptive decisionmaking problems, fin...
Most Relevant Explanation (MRE) is a new inference task in Bayesian networks that finds the most relevant partial instantiation of target variables as an explanation for given ev...
Most word embedding models typically represent each word using a single vector, which makes these models indiscriminative for ubiquitous homonymy and polysemy. In order to enhance...
Adaptive exploration uses active learning principles to improve the efficiency of autonomous robotic surveys. This work considers an important and understudied aspect of autonomo...
David Ray Thompson, David Wettergreen, Greydon T. ...
Graphical models provide a rich framework for summarizing the dependencies among variables. The graphical lasso approach attempts to learn the structure of a Gaussian graphical mo...
Maxim Grechkin, Maryam Fazel, Daniela M. Witten, S...
Hippocampal place cells and entorhinal grid cells have been hypothesized to be able to form map-like spatial representation of the environment, namely cognitive map. In most prior...
Miaolong Yuan, Bo Tian, Vui Ann Shim, Huajin Tang,...
Computing prices in core-selecting combinatorial auctions is a computationally hard problem. Auctions with many bids can only be solved using a recently proposed core constraint g...
Topic models remain a black box both for modelers and for end users in many respects. From the modelers’ perspective, many decisions must be made which lack clear rationales and...