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

We propose a novel adaptive approximation approach for test-time resource-constrained prediction motivated by Mobile, IoT, health, security and other applications, where constraints in the form of computation, communication, latency and feature acquisition costs arise. We learn an adaptive low-cost system by training a gating and prediction model that limits utilization of a high-cost model to hard input instances and gates easy-to-handle input instances to a low-cost model. Our method is based on adaptively approximating the high-cost model in regions where low-cost models suffice for making highly accurate predictions. We pose an empirical loss minimization problem with cost constraints to jointly train gating and prediction models. On a number of benchmark datasets our method outperforms state-of-the-art achieving higher accuracy for the same cost.

In many real-world applications, it is typically the case that the same optimization problem is solved again and again on a regular basis, maintaining the same problem structure but differing in the data.

Hierarchical clustering is a data analysis method that has been used for decades. Despite its widespread use, the method has an underdeveloped analytical foundation. Having a well understood foundation would both support the currently used methods and help guide future improvements. The goal of this paper is to give an analytic framework to better understand observations seen in practice.

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

We design efficient algorithms that provably recover the optimal clustering for instances that are additive perturbation stable.

In most applications of interest (e.g., modeling images), we have

Technological breakthroughs allow us to collect data with increasing spatiotemporal resolution from complex interaction systems.

Many real world tasks such as reasoning and physical interaction require identification and manipulation of conceptual entities.

We instead rely on weak supervision sources having some structure by virtue of being encoded programmatically.