Dr. Jason Fung shares on Dr. Mark Hyman's podcast how fasting may help reverse diseases like cancer by putting cells into maintenance mode instead of growth mode.

In 1982, Carnegie Mellon University professor Scott Fahlman suggested using:-) for humorous comments after his colleagues took a joke about mercury seriously. On September 19, 1982, Carnegie Mellon University computer science research assistant professor Scott Fahlman posted a message to the university's bulletin board software that would later come to shape how people communicate online. His proposal: use:-) and:-( as markers to distinguish jokes from serious comments. While Fahlman describes himself as "the inventor or at least one of the inventors" of what would later be called the smiley face emoticon, the full story reveals something more interesting than a lone genius moment. The whole episode started three days earlier when computer scientist Neil Swartz posed a physics problem to colleagues on Carnegie Mellon's "bboard," which was an early online message board.

We formulate a supervised learning problem, referred to as continuous ranking, where a continuous real-valued label Y is assigned to an observable r.v. X taking its values in a feature space X and the goal is to order all possible observations x in X by means of a scoring function s: X R so that s(X) and Y tend to increase or decrease together with highest probability. This problem generalizes bi/multi-partite ranking to a certain extent and the task of finding optimal scoring functions s( x) can be naturally cast as optimization of a dedicated functional criterion, called the IROC curve here, or as maximization of the Kendall τ related to the pair (s(X),Y). From the theoretical side, we describe the optimal elements of this problem and provide statistical guarantees for empirical Kendall τ maximization under appropriate conditions for the class of scoring function candidates. We also propose a recursive statistical learning algorithm tailored to empirical IROC curve optimization and producing a piecewise constant scoring function that is fully described by an oriented binary tree. Preliminary numerical experiments highlight the difference in nature between regression and continuous ranking and provide strong empirical evidence of the performance of empirical optimizers of the criteria proposed.

We develop a nonparametric and online learning algorithm that estimates the triggering functions of a multivariate Hawkes process (MHP).

However, nearly all existing subset selection techniques ignore underlying dynamics of data and treat items independently, leading to incompatible sets of representatives.

In the context of advertising auctions, finding good reserve prices is a notoriously challenging learning problem. This is due to the heterogeneity of ad opportunity types, and the non-convexity of the objective function. In this work, we show how to reduce reserve price optimization to the standard setting of prediction under squared loss, a well understood problem in the learning community. We further bound the gap between the expected bid and revenue in terms of the average loss of the predictor. This is the first result that formally relates the revenue gained to the quality of a standard machine learned model.

Online learning is a general framework for sequential prediction.

Moreover, SCSG is never worse than the state-of-the-art methods based on variance reduction and it significantly outperforms them when the target accuracy is low.

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

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