The government has shut down - what's next? Will a government shutdown hurt the economy? From Wall Street trading floors to the United States Federal Reserve to economists sipping coffee in their home offices, the first Friday morning of the month typically brings a quiet hush around 8:30am Eastern time in the US [12:30 GMT] as everyone awaits the Labor Department's crucial monthly jobs report. But with the government shut down, no information was released on Friday about hiring in September. Hiring has ground nearly to a halt, threatening to drag down the broader economy. Yet at the same time, consumers -- particularly higher-income earners -- are still spending, and some businesses are ramping up investments in data centres developing artificial intelligence models.

Differential privacy is a cryptographically motivated definition of privacy which has gained considerable attention in the algorithms, machine-learning and data-mining communities. While there has been an explosion of work on differentially private machine learning algorithms, a major barrier to achieving end-to-end differential privacy in practical machine learning applications is the lack of an effective procedure for differentially private parameter tuning, or, determining the parameter value, such as a bin size in a histogram, or a regularization parameter, that is suitable for a particular application. In this paper, we introduce a generic validation procedure for differentially private machine learning algorithms that apply when a certain stability condition holds on the training algorithm and the validation performance metric. The training data size and the privacy budget used for training in our procedure is independent of the number of parameter values searched over. We apply our generic procedure to two fundamental tasks in statistics and machine-learning - training a regularized linear classifier and building a histogram density estimator that result in end-to-end differentially private solutions for these problems.

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Are internet rumours of a comet hurtling towards Earth true? Rumours across social media platforms that a huge comet is on a collision course with Earth have been circulating, with some users describing it as a major threat to humanity. Others are debating how the comet - known as 3I/ATLAS and detected by NASA's ATLAS telescope on July 1 - might be diverted from the Earth. Some have even gone so far as to highlight "news" of military movements and an international coordination to counter the comet before impact, prompting further alarm. So is there any truth to these rumours and what do we know for sure?

We consider the problem of identifying multiway block structure from a large noisy tensor. Such problems arise frequently in applications such as genomics, recommendation system, topic modeling, and sensor network localization.

To solve this, we perform probabilistic reasoning over the depth of neural networks.

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