We focus on categorical attributes of language. Examples of such attributes include sentiment, language complexity, tense, voice, honorifics, mood, etc. Our approach draws inspiration from styletransfer methods inthevision andlanguage literature.

We design a stochastic algorithm to find $\varepsilon$-approximate local minima of any smooth nonconvex function in rate $O(\varepsilon^{-3.25})$,

The Iron Age human and animal footprints were preserved before high winds destroyed them. Breakthroughs, discoveries, and DIY tips sent six days a week. Two friends out walking their dogs along the eastern coast of Scotland unexpectedly found an archaeological goldmine . After wind gusts as strong as 55 mph blew away sand on the dunes of a beach near Angus, Ivor Campbell and Jenny Snedden (along with their pooches Ziggy and Juno) spotted the unique indentations in a layer of long-dried clay. The pair contacted a local archaeologist, and researchers from the University of Aberdeen quickly descended on the picturesque seaside locale to preserve the discoveries.

The United States trade deficit has widened sharply in December amid a surge in imports, and the goods shortfall in 2025 was the highest on record despite US President Donald Trump's tariffs on foreign-manufactured merchandise. The second straight monthly deterioration in the trade deficit reported by the US Commerce Department on Thursday suggested that trade made little or no contribution to gross domestic product (GDP) in the fourth quarter. The US deficit in the trade of goods widened 2 percent to a record $1.24 trillion last year as American companies boosted imports of computer chips and other tech goods from Taiwan to support massive investments in artificial intelligence. Amid continuing tensions with Beijing, the deficit in the goods trade with China plunged nearly 32 percent to $202bn in 2025 on a sharp drop in both exports to and imports from the world's second-biggest economy. But trade was diverted away from China.

Donald Trump Jr.'s Private DC Club Has Mysterious Ties to an Ex-Cop With a Controversial Past The Executive Branch has a reported membership list that includes Trumpworld elites like David Sacks. A WIRED review of corporate filings reveals an under-the-radar player: a notorious former DC police officer. When the Executive Branch soft-launched in Washington, DC, last spring, the private club's initial buzz centered on its starry roster of backers and founding members. The president's eldest son, Donald Trump Jr., is one of the club's several co-owners, according to previous reporting. Founding members reportedly include Trump administration AI czar David Sacks and his podcast cohost Chamath Palihapitiya, as well as crypto bigwigs Tyler and Cameron Winklevoss.

We present a novel algorithm to estimate the barycenter of arbitrary probability distributions with respect to the Sinkhorn divergence. Based on a Frank-Wolfe optimization strategy, our approach proceeds by populating the support of the barycenter incrementally, without requiring any pre-allocation.

Neural Information Processing Systems http://nips.cc/

People belong to multiple communities, words belong to multiple topics, and books cover multiple genres; overlapping clusters are commonplace. Many existing overlapping clustering methods model each person (or word, or book) as a non-negative weighted combination of exemplars who belong solely to one community, with some small noise. Geometrically, each person is a point on a cone whose corners are these exemplars. This basic form encompasses the widely used Mixed Membership Stochastic Blockmodel of networks and its degree-corrected variants, as well as topic models such as LDA. We show that a simple one-class SVM yields provably consistent parameter inference for all such models, and scales to large datasets. Experimental results on several simulated and real datasets show our algorithm (called SVM-cone) is both accurate and scalable.

We introduce a framework to transfer knowledge acquired from a repository of (heterogeneous) supervised datasets to new unsupervised datasets. Our perspective avoids the subjectivity inherent in unsupervised learning by reducing it to supervised learning, and provides a principled way to evaluate unsupervised algorithms. We demonstrate the versatility of our framework via rigorous agnostic bounds on a variety of unsupervised problems. In the context of clustering, our approach helps choose the number of clusters and the clustering algorithm, remove the outliers, and provably circumvent Kleinberg's impossibility result. Experiments across hundreds of problems demonstrate improvements in performance on unsupervised data with simple algorithms despite the fact our problems come from heterogeneous domains. Additionally, our framework lets us leverage deep networks to learn common features across many small datasets, and perform zero shot learning.