We develop a novel computationally efficient and general framework for robust hypothesis testing. The new framework features a new way to construct uncertainty sets under the null and the alternative distributions, which are sets centered around the empirical distribution defined via Wasserstein metric, thus our approach is data-driven and free of distributional assumptions. We develop a convex safe approximation of the minimax formulation and show that such approximation renders a nearly-optimal detector among the family of all possible tests. By exploiting the structure of the least favorable distribution, we also develop a tractable reformulation of such approximation, with complexity independent of the dimension of observation space and can be nearly sample-size-independent in general. Real-data example using human activity data demonstrated the excellent performance of the new robust detector.

The problem of handling adaptivity in data analysis, intentional or not, permeates a variety of fields, including test-set overfitting in ML challenges and the accumulation of invalid scientific discoveries. We propose a mechanism for answering an arbitrarily long sequence of potentially adaptive statistical queries, by charging a price for each query and using the proceeds to collect additional samples. Crucially, we guarantee statistical validity without any assumptions on how the queries are generated. We also ensure with high probability that the cost for $M$ non-adaptive queries is $O(\log M)$, while the cost to a potentially adaptive user who makes $M$ queries that do not depend on any others is $O(\sqrt{M})$.

Sling TV's line up of available channels is getting bigger. The streaming TV service is adding nine new channels from Discovery Networks that offer live and on-demand content, including the flagship Discovery Channel and MotorTrend. The best news for Sling subscribers: some of the channels will be added to your package for free. Access to the channels will be split across Sling's two separate service packages, both of which cost $25 per month. Sling Blue will get Discovery Channel, Investigation Discovery and TLC.

Hypothesis testing plays a central role in statistical inference, and is used in many settings where privacy concerns are paramount. This work answers a basic question about privately testing simple hypotheses: given two distributions $P$ and $Q$, and a privacy level $\varepsilon$, how many i.i.d. samples are needed to distinguish $P$ from $Q$ subject to $\varepsilon$-differential privacy, and what sort of tests have optimal sample complexity? Specifically, we characterize this sample complexity up to constant factors in terms of the structure of $P$ and $Q$ and the privacy level $\varepsilon$, and show that this sample complexity is achieved by a certain randomized and clamped variant of the log-likelihood ratio test. Our result is an analogue of the classical Neyman-Pearson lemma in the setting of private hypothesis testing. We also give an application of our result to the private change-point detection. Our characterization applies more generally to hypothesis tests satisfying essentially any notion of algorithmic stability, which is known to imply strong generalization bounds in adaptive data analysis, and thus our results have applications even when privacy is not a primary concern.

Amazon has suffered a customer data leak less than two days before Black Friday. Amazon customer service contacted people to warn them that their names and email addresses had been compromised, though it is not yet clear how many customers were affected or how it happened. An Amazon spokesperson told The Independent: "We have fixed the issue and informed customers who may have been impacted." The customer message stated: "We're contacting you to let you know that our website inadvertently disclosed your name and email address due to a technical error. "The issue has been fixed.

When analysing graph structure, it can be difficult to determine whether patterns found are due to chance, or due to structural aspects of the process that generated the data. Hypothesis tests are often used to support such analyses. These allow us to make statistical inferences about which null models are responsible for the data, and they can be used as a heuristic in searching for meaningful patterns. The minimum description length (MDL) principle [6, 4] allows us to build such hypothesis tests, based on efficient descriptions of the data. Broadly: we translate the regularity we are interested in into a code for the data, and if this code describes the data more efficiently than a code corresponding to the null model, by a sufficient margin, we may reject the null model. This is a frequentist approach to MDL, based on hypothesis testing. Bayesian approaches to MDL for model selection rather than model rejection are more common, but for the purposes of pattern analysis, a hypothesis testing approach provides a more natural fit with existing literature. 1 We provide a brief illustration of this principle based on the running example of analysing the size of the largest clique in a graph. We illustrate how a code can be constructed to efficiently represent graphs with large cliques, and how the description length of the data under this code can be compared to the description length under a code corresponding to a null model to show that the null model is highly unlikely to have generated the data.

Wireless communications have undeniably shaped our everyday lives. We expect ubiquitous connectivity to the Internet, with increasing demands for higher data rates and low lag everywhere: at work, at home, on the road, even with massive crowds of Internet users around us. Despite impressive breakthroughs in almost every part of our wireless devices--from antennas and hardware to operating software--this demand is getting increasingly challenging to address. The large scale of research efforts and investment in the fifth generation (5G) of wireless communications reflects the enormity of the challenge.1 A valuable and seemingly unnoticed resource could be exploited to meet this goal.

Statistical hypothesis testing serves as statistical evidence for scientific innovation. However, if the reported results are intentionally biased, hypothesis testing no longer controls the rate of false discovery. In particular, we study such selection bias in machine learning models where the reporter is motivated to promote an algorithmic innovation. When the number of possible configurations (e.g., datasets) is large, we show that the reporter can falsely report an innovation even if there is no improvement at all. We propose a `post-reporting' solution to this issue where the bias of the reported results is verified by another set of results. The theoretical findings are supported by experimental results with synthetic and real-world datasets.

What would happen if we made all of our data public--everything from wearables monitoring our biometrics, all the way to smartphones monitoring our location, our social media activity, and even our internet search history? Would such insights into our lives simply provide companies and politicians with greater power to invade our privacy and manipulate us by using our psychological profiles against us? A burgeoning new philosophy called dataism doesn't think so. In fact, this trending ideology believes that liberating the flow of data is the supreme value of the universe, and that it could be the key to unleashing the greatest scientific revolution in the history of humanity. First mentioned by David Brooks in his 2013 New York Times article "The Philosophy of Data," dataism is an ethical system that has been most heavily explored and popularized by renowned historian, Yuval Noah Harari.

Sure, you've heard of game theory. And sure, trading is like a game: you devise a strategy, learn the rules, and try to beat everyone else to the punch, finding trends before anyone else does. You've even heard that letting your emotions get the best of you is a terrible trading strategy. To start to undermine our own self-destructive habits, it helps to understand how the game is geared toward those who figure out the game behind the game: how to play to win by controlling the emotions that lead us to bad decisions. Game theory can be applied to human trading because the object of a trade is to "win" a profit.