Since it is generally NPhard [7], much effort has been devoted to the design of polynomial-time approximation algorithms. The greedy algorithm is most favored for its simplicity, which iteratively chooses one item with the largest immediate benefit. Despite the greedy nature, it can perform well in many cases.

The key insight of the approach is that quantization methods, in particular product quantization, perform poorly when there is large variance in the norms of the data points.

Sequential identification questions in game trees with stochastic payoffs arise naturally as robust versions of bandit problems.

We study minimax strategies for the online prediction problem with expert advice.

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

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

Recently, there has been substantial research on how to perform MIPS in sub-linear time, but most of the existing work does not have the flexibility to control the trade-off between search efficiency and search quality.

In particular, it allows us to approximately maximize discrete, monotone submodular optimization problems via projected gradient ascent on a continuous relaxation, directly connecting the discrete and continuous domains.

Computing the partition function is a key inference task in many graphical models. In this paper, we propose a dynamic importance sampling scheme that provides anytime finite-sample bounds for the partition function.

Sparse edge recovery is generally not easy for an arbitrary distribution.