Such asymmetry presents two issues.

Latin megastar Bad Bunny performed a medley of his top hits at the Super Bowl on Sunday in a star-studded show that was criticised as terrible by the US president. The Puerto Rican singer, also known as Benito Antonio Martinez Ocasio, was joined on stage by a host of fellow music stars including Lady Gaga, Ricky Martin and Cardi B. Sitting in the stands, Kim Kardashian and Lewis Hamilton made their first major public appearance together, after weeks of speculation about their romance. The seven-time Formula 1 world champion and the reality TV star were spotted chatting and smiling together during the game, and were caught on video by NBC News. Fellow musical superstars Lady Gaga, Cardi B and Jessica Alba joined the dancers on stage alongside Bad Bunny, who was the world's most-played artist in 2025 on Spotify, according to the streaming service. Chilean-American actor Pedro Pascal and Puerto Rican singer Ricky Martin also joined the performance, which was populated by a largely pan-American crowd of celebrities.

Then, 'great tennis player' is a 3-gram as it appears in the text of

Watch Party: The Best TAG in Years, a '60s Sensation, and Omega Goes All White It's LVMH Watch Week, so here's WIRED's pick of the timepieces that made their debut--plus one notable gatecrasher. The watch world is readying itself for the slew of new releases from the likes of Patek Philippe and Rolex when Watches and Wonders descends on Geneva in April. But this week, the watchmaker Omega and the luxury conglomerate LVMH both spotted a window of opportunity to get pieces out ahead of the annual gathering. Since 2020, LVMH has been kicking off each new year by serving up watches from its stable of brands, including Zenith, TAG Heuer, Hublot, and Louis Vuitton. Meanwhile, Omega--muscling in on LVMH's party somewhat--is leaning into its connection to next month's Winter Olympics in Italy, where it will once again serve as the event's official timekeeper.

Zurich-based firm taps into latest robot tech to'fibre-spray' high-end sports shoes worn by the likes of Roger Federer A robot leg whirs around in a complex ballet as an almost invisible spray of "flying fibre" builds a hi-tech £300 sports shoe at its foot. This nearly entirely automated process - like a sci-fi future brought to life - is part of the gameplan from On, the Swiss sports brand that is taking on the sector's mighty champions Nike and Adidas with a mix of technology and chutzpah. The brand is expanding rapidly after teaming up with the former tennis pro Roger Federer to create shoes suitable for the Swiss star's sport and a mix of fashion-led collaborations including with the luxury brand LOEWE, actor Zendaya and singers FKA twigs and Burna Boy. In China, sales have doubled year-on-year. Growth has been strong in the US and mainland Europe and this month On will open its fourth London store, in Kensington.

While preference modelling is becoming one of the pillars of machine learning, the problem of preference explanation remains challenging and underexplored. In this paper, we propose \textsc{Pref-SHAP}, a Shapley value-based model explanation framework for pairwise comparison data. We derive the appropriate value functions for preference models and further extend the framework to model and explain \emph{context specific} information, such as the surface type in a tennis game. To demonstrate the utility of \textsc{Pref-SHAP}, we apply our method to a variety of synthetic and real-world datasets and show that richer and more insightful explanations can be obtained over the baseline.

We consider the problem of ranking objects from noisy pairwise comparisons, for example, ranking tennis players from the outcomes of matches. We follow a standard approach to this problem and assume that each object has an unobserved strength and that the outcome of each comparison depends probabilistically on the strengths of the comparands. However, we do not assume to know a priori how skills affect outcomes. Instead, we present an efficient algorithm for simultaneously inferring both the unobserved strengths and the function that maps strengths to probabilities. Despite this problem being under-constrained, we present experimental evidence that the conclusions of our Bayesian approach are robust to different model specifications. We include several case studies to exemplify the method on real-world data sets.

The paddle follows a long line of oddball products, from $450 mezcal to questionably legal flamethrowers. We may earn revenue from the products available on this page and participate in affiliate programs. Tesla's next big product reveal isn't a long-anticipated affordable passenger car or an actually usable humanoid robot . On Friday, the company announced it has partnered with prominent paddle manufacturer Selkirk Sport on an arguably over-engineered accessory meant to "bring advanced aerodynamics and precision performance" to pickleball players with deep pockets. The result, Tesla claims, is a premium product designed to improve swing speed and durability.

Accelerated gradient methods like Nesterov's Accelerated Gradient (NAG) achieve faster convergence on well-conditioned problems but often diverge on ill-conditioned or non-convex landscapes due to aggressive momentum accumulation. We propose Heavy-Ball Synthetic Gradient Extrapolation (HB-SGE), a robust first-order method that combines heavy-ball momentum with predictive gradient extrapolation. Unlike classical momentum methods that accumulate historical gradients, HB-SGE estimates future gradient directions using local Taylor approximations, providing adaptive acceleration while maintaining stability. We prove convergence guarantees for strongly convex functions and demonstrate empirically that HB-SGE prevents divergence on problems where NAG and standard momentum fail. On ill-conditioned quadratics (condition number κ = 50), HB-SGE converges in 119 iterations while both SGD and NAG diverge. On the non-convex Rosen-brock function, HB-SGE achieves convergence in 2,718 iterations where classical momentum methods diverge within 10 steps. While NAG remains faster on well-conditioned problems, HB-SGE provides a robust alternative with speedup over SGD across diverse landscapes, requiring only O(d) memory overhead and the same hy-perparameters as standard momentum.