As datasets grow richer, an important challenge is to leverage the full features in the data to maximize the number of useful discoveries while controlling for false positives. We address this problem in the context of multiple hypotheses testing, where for each hypothesis, we observe a p-value along with a set of features specific to that hypothesis. For example, in genetic association studies, each hypothesis tests the correlation between a variant and the trait.

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

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

There are numerous examples where accounting for the predictors' interactions is of interest, including problems of identifying epistasis (gene-gene) and gene-environment interactions in genetics

Extrapolation methods use the last few iterates of an optimization algorithm to produce a better estimate of the optimum. They were shown to achieve optimal convergence rates in a deterministic setting using simple gradient iterates. Here, we study extrapolation methods in a stochastic setting, where the iterates are produced by either a simple or an accelerated stochastic gradient algorithm.

It is therefore possible to learn the symbols by naively collecting samples from the environment, for example by random exploration.

Furthermore, as the interpolation is learned and state-dependent, our method can deal with heterogeneous observability.

This paper presents a probabilistic-graphical model that can be used to infer characteristics of instantaneous brain activity by jointly analyzing spatial and temporal dependencies observed in electroencephalograms (EEG). Specifically, we describe a factor-graph-based model with customized factor-functions defined based on domain knowledge, to infer pathologic brain activity with the goal of identifying seizure-generating brain regions in epilepsy patients. We utilize an inference technique based on the graph-cut algorithm to exactly solve graph inference in polynomial time. We validate the model by using clinically collected intracranial EEG data from 29 epilepsy patients to show that the model correctly identifies seizure-generating brain regions. Our results indicate that our model outperforms two conventional approaches used for seizure-onset localization (5-7% better AUC: 0.72, 0.67, 0.65) and that the proposed inference technique provides 3-10% gain in AUC ( 0.72, 0.62, 0.69) compared to sampling-based alternatives.

Lyapunov function that directly incorporates both objective progress and delays.

Newfoundland dogs are still practicing the same lifesaving skills they would have used in the 19th century. Breakthroughs, discoveries, and DIY tips sent every weekday. It's a dark and stormy night and you've suddenly found yourself swept off of your wooden vessel into the wild Atlantic Ocean. It's 1893, so your woolen clothes are pulling you down to Davy Jones' locker. What kind of dog would want to rescue you?