In this paper we investigate the computational complexity of learning the graph structure underlying a discrete undirected graphical model from i.i.d.

We describe a framework for designing efficient active learning algorithms that are tolerant to random classification noise and differentially-private. The framework is based on active learning algorithms that are statistical in the sense that they rely on estimates of expectations of functions of filtered random examples. It builds on the powerful statistical query framework of Kearns [30]. We show that any efficient active statistical learning algorithm can be automatically converted to an efficient active learning algorithm which is tolerant to random classification noise as well as other forms of "uncorrelated" noise. We show that commonly studied concept classes including thresholds, rectangles, and linear separators can be efficiently actively learned in our framework. These results combined with our generic conversion lead to the first computationally-efficient algorithms for actively learning some of these concept classes in the presence of random classification noise that provide exponential improvement in the dependence on the error ɛ over their passive counterparts. In addition, we show that our algorithms can be automatically converted to efficient active differentially-private algorithms. This leads to the first differentially-private active learning algorithms with exponential label savings over the passive case.

Machine learning has reached a point where many probabilistic meth- ods can be understood as variations, extensions and combinations of a much smaller set of abstract themes, e.g., as different instances of the EM algorithm. This enables the systematic derivation of algorithms cus- tomized for different models. Here, we describe the AUTO BAYES sys- tem which takes a high-level statistical model specification, uses power- ful symbolic techniques based on schema-based program synthesis and computer algebra to derive an efficient specialized algorithm for learning that model, and generates executable code implementing that algorithm. This capability is far beyond that of code collections such as Matlab tool- boxes or even tools for model-independent optimization such as BUGS for Gibbs sampling: complex new algorithms can be generated with- out new programming, algorithms can be highly specialized and tightly crafted for the exact structure of the model and data, and efficient and commented code can be generated for different languages or systems. We describe a symbolic program synthesis system which works as a "statistical algorithm compiler:" it compiles a statistical model specification into a custom algorithm design and from that further down into a working program implementing the algorithm design.

A team of researchers has developed a new framework which utilizes advanced machine learning and statistical algorithms to predict rare events without the need for large data sets. Scientists can use a combination of advanced machine learning and sequential sampling techniques to predict extreme events without the need for large data sets, according to researchers from Brown and MIT. When it comes to predicting disasters brought on by extreme events (think earthquakes, pandemics, or "rogue waves" that could destroy coastal structures), computational modeling faces an almost insurmountable challenge: Statistically speaking, these events are so rare that there's just not enough data on them to use predictive models to accurately forecast when they'll happen next. However, a group of scientists from Brown University and Massachusetts Institute of Technology suggests that it doesn't have to be that way. In a study published in Nature Computational Science, the researchers explain how they utilized statistical algorithms which require less data for accurate predictions, in combination with a powerful machine learning technique developed at Brown University.

"Neural networks are faced with three big issues…" Today, neural networks dominate the landscape of AI and AIOps, the question I pose is whether this is justifiable and sustainable, writes Will Cappelli, CTO EMEA and Global VP of Product Strategy at Moogsoft. Let's look at this commercially. Within the context of AIOps, neural networks have peaked in their ability to deliver effective and meaningful results. There are a number of limiting issues that relate directly to neural network algorithms, and it is my belief that these cannot be changed. I would say that neural networks are faced with three big issues, and this ranges from single layer neural networks to multiple layer networks.

Epidemiologists use a variety of statistical algorithms for the early detection of outbreaks. The practical usefulness of such methods highly depends on the trade-off between the detection rate of outbreaks and the chances of raising a false alarm. Recent research has shown that the use of machine learning for the fusion of multiple statistical algorithms improves outbreak detection. Instead of relying only on the binary output (alarm or no alarm) of the statistical algorithms, we propose to make use of their p-values for training a fusion classifier. In addition, we also show that adding additional features and adapting the labeling of an epidemic period may further improve performance. For comparison and evaluation, a new measure is introduced which captures the performance of an outbreak detection method with respect to a low rate of false alarms more precisely than previous works. Our results on synthetic data show that it is challenging to improve the performance with a trainable fusion method based on machine learning. In particular, the use of a fusion classifier that is only based on binary outputs of the statistical surveillance methods can make the overall performance worse than directly using the underlying algorithms. However, the use of p-values and additional information for the learning is promising, enabling to identify more valuable patterns to detect outbreaks.

Like AI and machine learning, predictive analysis has become a bit of an Internet buzzword in the last few years. The technology already has a wide variety of applications for marketers -- predictive advertising is just one of them. If you're among the many who are still fuzzy about what predictive analysis is, here's a rundown on how it's changing the way marketers advertise today. Predictive advertising is a marketing application of predictive analysis. Predictive analysis, by definition, is the use of consumer data, artificial intelligence and statistical algorithms to identify what could happen in the future.

For centuries people have tried to decipher the meaning of the Voynich manuscript, and now a computer scientist claims to have cracked it using AI. The 600-year-old document is described as'the world's most mysterious medieval text', and is full of illustrations of exotic plants, stars, and mysterious human figures. The 240-page manual's intriguing mix of elegant writing and drawings of strange plants and naked women has some believing it holds magical powers. But even the cryptographers from Bletchley Park, the team that broke the Nazi enigma code, couldn't make sense of the manuscript. Now a computer scientist says the manuscript is written in ancient Hebrew and the code involves shuffling the order of letters in each word and dropping the vowels. While his is still to decipher its full meaning, he believes the first sentence of the text says: 'he made recommendations to the priest, man of the house and me and people.'

Learned sources define predictive analytics as the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. I define it as educated guessing with math and statistics, computer powered tarot, or Elon Musk astrology. Essentially it is the tech hype version of fortune telling or handicapping. Also the learned sources need to get their learning on because statistical algorithms, really? I probably should mention it more often.

We consider the following general hidden hubs model: an $n \times n$ random matrix $A$ with a subset $S$ of $k$ special rows (hubs): entries in rows outside $S$ are generated from the probability distribution $p_0 \sim N(0,\sigma_0^2)$; for each row in $S$, some $k$ of its entries are generated from $p_1 \sim N(0,\sigma_1^2)$, $\sigma_1>\sigma_0$, and the rest of the entries from $p_0$. The problem is to identify the high-degree hubs efficiently. This model includes and significantly generalizes the planted Gaussian Submatrix Model, where the special entries are all in a $k \times k$ submatrix. There are two well-known barriers: if $k\geq c\sqrt{n\ln n}$, just the row sums are sufficient to find $S$ in the general model. For the submatrix problem, this can be improved by a $\sqrt{\ln n}$ factor to $k \ge c\sqrt{n}$ by spectral methods or combinatorial methods. In the variant with $p_0=\pm 1$ (with probability $1/2$ each) and $p_1\equiv 1$, neither barrier has been broken. We give a polynomial-time algorithm to identify all the hidden hubs with high probability for $k \ge n^{0.5-\delta}$ for some $\delta >0$, when $\sigma_1^2>2\sigma_0^2$. The algorithm extends to the setting where planted entries might have different variances each at least as large as $\sigma_1^2$. We also show a nearly matching lower bound: for $\sigma_1^2 \le 2\sigma_0^2$, there is no polynomial-time Statistical Query algorithm for distinguishing between a matrix whose entries are all from $N(0,\sigma_0^2)$ and a matrix with $k=n^{0.5-\delta}$ hidden hubs for any $\delta >0$. The lower bound as well as the algorithm are related to whether the chi-squared distance of the two distributions diverges. At the critical value $\sigma_1^2=2\sigma_0^2$, we show that the general hidden hubs problem can be solved for $k\geq c\sqrt n(\ln n)^{1/4}$, improving on the naive row sum-based method.