Much of the previous work towards digital agents for graphical user interfaces (GUIs) has relied on text-based representations (derived from HTML or other structured data sources), which are not always readily available.

They iteratively modify and resolve linear programs to conduct a depth-first search from any node in the search tree. Existing divers rely on generic decision rules that fail to exploit structural commonality between similar problem instances that often arise in practice.

Becauseofarichvarietyof applications, themaximization ofanonnegativesubmodular function with respect toacardinality constraint (MCC) has a long history of study (Nemhauser et al., 1978). Furthermore, this ratio is optimal under thevalue oracle model (Nemhauser and Wolsey,1978).

Unsupervised learning (UL)-based solvers for combinatorial optimization (CO) train a neural network that generates a soft solution by directly optimizing the CO objective using a continuous relaxation strategy. These solvers offer several advantages over traditional methods and other learning-based methods, particularly for large-scale CO problems.

Learning the minimum/maximum mean among a finite set of distributions is a fundamental sub-task in planning, game tree search and reinforcement learning.

Graph search is one of the most successful algorithmic trends in near neighbor search. Severalofthemostpopular andempirically successful algorithms are,at their core, a greedy walk along a pruned near neighbor graph.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/