Discovering statistical structure from links is a fundamental problem in the analysis of social networks. Choosing a misspecified model, or equivalently, an incorrect inference algorithm will result in an invalid analysis or even falsely uncover patterns that are in fact artifacts of the model. This work focuses on unifying two of the most widely used link-formation models: the stochastic blockmodel (SBM) and the small world (or latent space) model (SWM). Integrating techniques from kernel learning, spectral graph theory, and nonlinear dimensionality reduction, we develop the first statistically sound polynomial-time algorithm to discover latent patterns in sparse graphs for both models. When the network comes from an SBM, the algorithm outputs a block structure. When it is from an SWM, the algorithm outputs estimates of each node's latent position.

Approximate probabilistic inference algorithms are central to many fields. Examples include sequential Monte Carlo inference in robotics, variational inference in machine learning, and Markov chain Monte Carlo inference in statistics.

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.

Well-calibrated uncertainty is crucial for many tasks in deep learning.

Leading the Future said it will spend millions to keep Alex Bores out of Congress. It might be helping him instead. It turns out that when an AI-friendly super PAC with $100 million in backing from Silicon Valley bigwigs identifies you as its first target, it ends up generating a lot of attention. "I want to thank [the PAC] for their partnership in raising up the issue of how we regulate an incredibly powerful technology so that the future is one that benefits all of us," says Alex Bores, a New York Assembly member and Democratic congressional candidate, in an interview with WIRED. "I couldn't imagine a better partner this week."

As a result, the use of group-conditional decision making systems is often prohibited.

We prove uniqueness of the barycenter of a population of GPs, as well as convergence of the metric and the barycenter of their finite-dimensional counterparts.

Evaluating a policy by deploying it in the real world can be risky and costly.

New analysis reveals why America's $5 trillion AI race against China proves as consequential as the Cold War, citing economic dominance and national security at stake.