We propose a semi-parametric and dynamic rank factor model for topic modeling, capable of (i) discovering topic prevalence over time, and (ii) learning contemporary multi-scale dependence structures, providing topic and word correlations as a byproduct. The high-dimensional and time-evolving ordinal/rank observations (such as word counts), after an arbitrary monotone transformation, are well accommodated through an underlying dynamic sparse factor model. The framework naturally admits heavy-tailed innovations, capable of inferring abrupt temporal jumps in the importance of topics. Posterior inference is performed through straightforward Gibbs sampling, based on the forward-filtering backwardsampling algorithm. Moreover, an efficient data subsampling scheme is leveraged to speed up inference on massive datasets. The modeling framework is illustrated on two real datasets: the US State of the Union Address and the JSTOR collection from Science.

We formulate the problem of metric learning for k nearest neighbor classification as a large margin structured prediction problem, with a latent variable representing the choice of neighbors and the task loss directly corresponding to classification error. We describe an efficient algorithm for exact loss augmented inference, and a fast gradient descent algorithm for learning in this model. The objective drives the metric to establish neighborhood boundaries that benefit the true class labels for the training points. Our approach, reminiscent of gerrymandering (redrawing of political boundaries to provide advantage to certain parties), is more direct in its handling of optimizing classification accuracy than those previously proposed. In experiments on a variety of data sets our method is shown to achieve excellent results compared to current state of the art in metric learning.

Sparse high dimensional graphical model selection is a popular topic in contemporary machine learning.

The success of machine learning, particularly in supervised settings, has led to numerous attempts to apply it in adversarial settings such as spam and malware detection. The core challenge in this class of applications is that adversaries are not static data generators, but make a deliberate effort to evade the classifiers deployed to detect them. We investigate both the problem of modeling the objectives of such adversaries, as well as the algorithmic problem of accounting for rational, objective-driven adversaries. In particular, we demonstrate severe shortcomings of feature reduction in adversarial settings using several natural adversarial objective functions, an observation that is particularly pronounced when the adversary is able to substitute across similar features (for example, replace words with synonyms or replace letters in words). We offer a simple heuristic method for making learning more robust to feature cross-substitution attacks. We then present a more general approach based on mixed-integer linear programming with constraint generation, which implicitly trades off overfitting and feature selection in an adversarial setting using a sparse regularizer along with an evasion model. Our approach is the first method for combining an adversarial classification algorithm with a very general class of models of adversarial classifier evasion. We show that our algorithmic approach significantly outperforms state-of-the-art alternatives.

We consider the problem of subspace clustering: given points that lie on or near the union of many low-dimensional linear subspaces, recover the subspaces. To this end, one first identifies sets of points close to the same subspace and uses the sets to estimate the subspaces. As the geometric structure of the clusters (linear subspaces) forbids proper performance of general distance based approaches such as K-means, many model-specific methods have been proposed. In this paper, we provide new simple and efficient algorithms for this problem. Our statistical analysis shows that the algorithms are guaranteed exact (perfect) clustering performance under certain conditions on the number of points and the affinity between subspaces. These conditions are weaker than those considered in the standard statistical literature. Experimental results on synthetic data generated from the standard unions of subspaces model demonstrate our theory. We also show that our algorithm performs competitively against state-of-the-art algorithms on realworld applications such as motion segmentation and face clustering, with much simpler implementation and lower computational cost.

A cult expert lifted the veil on the "Zizian" fringe group that is linked to the Vermont U.S. Border Patrol agent shooting. The "Zizians" are named for a 34-year-old computer engineer, Jack Amadeus LaSota, who goes by the nickname "Ziz," according to the San Francisco Chronicle. LaSota, who is transgender, goes by female pronouns and created the group of vegan activists, the outlet reported. The group, which began on the West Coast, was launched into the national spotlight after the killing of U.S. Border Patrol Agent David "Chris" Maland in Vermont on Jan. 20. David Maland, a Minnesota native and U.S. Air Force veteran, worked as a Border Patrol agent at the U.S. Customs and Border Protection's Newport Station.

Yurts founder and CEO Ben Van Roo breaks down concerns over DeepSeek on'The Will Cain Show.' Artificial intelligence may be the new frontier for childhood schooling, but the idea of teacherless classrooms has received mixed reviews from state education officials. Unbound Academy, a Texas-based institution billing itself as the nation's first virtual, tuition-free charter school for grades 4 through 8, reportedly employs AI to teach students in a way that can be geared toward the individual student without "frustration[s]" sometimes present in traditional schooling. While such schools have seen success in being approved to educate students in Arizona, Unbound was formally rejected by the Pennsylvania Department of Education in a letter obtained by Fox News Digital. In a letter to an Unbound Academy official with a Lancaster office address, Secretary Angela Fitterer said her office has found "deficiencies" in all five criteria needed for approval to teach Keystone State students. Pennsylvania's Charter School law denotes a school must demonstrate sustainable support for the cyber charter school plan from teachers, parents and students.

Much of research in machine learning has centered around the search for inference algorithms that are both general-purpose and efficient. The problem is extremely challenging and general inference remains computationally expensive. We seek to address this problem by observing that in most specific applications of a model, we typically only need to perform a small subset of all possible inference computations. Motivated by this, we introduce just-in-time learning, a framework for fast and flexible inference that learns to speed up inference at run-time. Through a series of experiments, we show how this framework can allow us to combine the flexibility of sampling with the efficiency of deterministic message-passing.

At President Donald Trump's inauguration, Sam Altman, CEO of OpenAI, was relegated to the overflow room while other tech billionaires like Elon Musk and Mark Zuckerberg took prime spots on the dais under the Capitol rotunda. But days earlier, before flying to Washington, Altman was on the phone with Trump, preparing an announcement that would outflank Musk and put Altman's company at the center of the new administration's agenda for artificial intelligence. On the 25-minute call, Altman appealed to Trump's love of a big story and of a big deal. He told the president-elect that the tech industry would achieve artificial general intelligence -- the hypothetical moment when technology matches human intelligence -- during the Trump administration, according to three people familiar with the call. And to get there before Chinese competitors, OpenAI, Oracle and SoftBank had completed a 100 billion deal to build data centers across the country.

Hierarchical clustering methods offer an intuitive and powerful way to model a wide variety of data sets. However, the assumption of a fixed hierarchy is often overly restrictive when working with data generated over a period of time: We expect both the structure of our hierarchy, and the parameters of the clusters, to evolve with time. In this paper, we present a distribution over collections of time-dependent, infinite-dimensional trees that can be used to model evolving hierarchies, and present an efficient and scalable algorithm for performing approximate inference in such a model. We demonstrate the efficacy of our model and inference algorithm on both synthetic data and real-world document corpora.