NCAA responds after Riley Gaines' lawsuit is dismissed, affirms compliance with Trump's execuitve order Jose Canseco says Bigfoot is real and humans'have some type of agreements with aliens' Madison Beer gushes over'incredible' Justin Herbert, WAG culture is headed to college & the NASCAR swear jar Mike Tomlin's coach criticism makes him better TV analyst but raises question whether he's eyeing Cowboys job Bucks go toe-to-toe in man's yard, manage to avoid his truck & boat during antler-locking combat Ross Chastain calls Ryan Blaney's fight with Carson Hocevar at Bristol Motor Speedway'NASCAR 101' Which college football coaches improved or tanked their hot seat status four games into the season? Iowa wide receiver'blacked out' after hauling in last-second game winner against Michigan Riley Gaines' lawsuit vs NCAA dismissed by Biden-appointed judge, appeal planned WWE star Penta subtly pays tribute to Pac after wrestler's shocking death at 40 WWE star Jaida Parker crashes'Monday Night Raw,' runs over Jacob Fatu with her car as top factions go to war Rutgers female rugby club erases'women' from its title name in favor of trans inclusion Affirm CEO touts partnership with Crate & Barrel: 'First grown-up brand' BREAKING: Ex-'American Idol' contestant found guilty in murder of wife Residents living 200 feet from a Virginia data center reveal what it's really like OutKick Aaron Judge's calf strain could cascade into hamstring or Achilles injuries if rushed back, surgeon warns Dr. Drew Burdi warns that a premature return from the soleus injury risked turning a moderate tear into something worse Ex-college football player-turned surgeon Dr. Drew Burdi tells OutKick in a recent interview Aaron Judge could injure his Achilles or hamstring by returning too soon from his calf injury. Aaron Judge desperately tried to return for the postseason after straining his calf, but the New York Yankees captain was wisely left off the Wild Card series roster. Judge sustained a moderate soleus muscle strain in his calf and received a platelet-poor plasma (PPP) injection to try to speed up his rehab, but it wasn't enough for him to make the roster for the team's best-of-three series against the Boston Red Sox. This page may contain affiliate links to legal sports betting partners.

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Midterm elections could hinge on turnout, redistricting and key House races in Ohio, Iowa, Virginia and Maine as Democrats and Republicans battle for control.

Madison Beer's bid to become the NFL's next Taylor Swift hits road bump, Browns are a mess & a Daejon Love fan Denny Hamlin says forward-facing sonar in professional bass fishing has gone'a little overboard' NASCAR driver says quiet part out loud about Sophie Cunningham, the WNBA looks even worse & car goes airborne! Rams' Sean McVay reveals wife's reality check after mulling media jump following losing season in 2022 Caitlin Clark says she and Angel Reese have'always been friends' after winning FIBA Women's World Cup Stefon Diggs guilty of NFL's dumbest move of Week 1 and it may have decided Commanders loss to Eagles John Harbaugh's Giants on full display in bruising win over Cowboys to kick off NFL season WWE star Stephanie Vaquer captures Women's World Championship at Chile live event in surprising moment Ben Shelton loses in US Open final to Zverev, but America has found its next big men's tennis star Jeremiyah Love makes immediate statement with touchdown on first career drive in Cardinals' upset win Josh Allen torches one of NFL's best defenses to make case it's time to move on from 2025 playoff loss Afghan woman deported in landmark'alien terrorist' court case AI policy expert dismisses Big Tech regulation calls: 'We should see through this' AI agents are'hacking out of the container' despite best efforts to prevent it, expert warns Prosecution in Lindsay Clancy trial may have'alienated' jurors, criminal defense attorney says You treat a nuclear power'significantly different' than a non-nuclear power: Gen. Keith Kellogg Accepting political violence as a form of expression is'dangerous,' Jonathan Turley warns'The Squad' faces backlash over claims'modern-day lynchings' are now common in America OutKick Sports WWE broadcaster believes'the pieces are all in place' for an upset as Penta challenges Roman Reigns for title Roman Reigns defends the World Heavyweight Championship against Penta on'Monday Night Raw' There's a massive collision set for Monday Night Raw in Mexico City. Roman Reigns will put the World Heavyweight Championship on the line against Penta. Reigns has been the champion since defeating CM Punk at WrestleMania 42 and has put down everyone who has stepped up to the plate to try and take a swing at him. Penta poses an element as a challenger that Reigns really hasn't faced before.

A new mod lets you smash and shoot Flock's automatic license plate readers around the fictional Los Santos. People are not happy about Flock Safety's automated license plate readers and the cops that allegedly misuse them . If you're one of those ALPR-haters, you can now take out your rage in the video game by installing Grand Theft Automated License Plate Reader, a mod built by artist Morry Kolman. Players can smash and shoot down the cameras and get paid $600 for each one they destroy, which Kolman priced based on a teardown of a Flock Falcon Flex Camera by the Iowa-based civil liberties group Eyes Off of Cedar Rapids. The in-game cameras log every time a player destroys and drives by one, and players can render a photo album of their interactions.

TorchKM is an open-source library for kernel machines, including support vector machines, kernel logistic regression, and kernel quantile regression, with GPU acceleration. The library features a scikit-learn-style API and is designed to exploit GPU-friendly linear algebra, accelerating the full training and model-selection pipeline through intelligent reuse of matrix operations. Benchmarks show competitive predictive performance with substantial speedups over standard baselines. The efficiency and programmable design also make TorchKM a kernel-learning component for AI-driven workflows. Code and documentation are available at https://github.com/YikaiZhang95/torchkm, and the package can be easily installed via PyPI.

We develop a skew-adaptive extension of split conformal prediction for regression. The method starts from an asymmetric interval family centered at a point prediction and uses the gauge approach to deduce the conformity score induced by this family. The inverse hyperbolic sine transform of signed scaled residuals provides the training target for an additional predictive model, whose role is to learn how predictive uncertainty should tilt across the feature space. The resulting procedure preserves the finite-sample marginal validity of split conformal prediction under exchangeability, while producing intervals that adapt to both local scale and local skewness. We also develop a calibration-sample-based estimator for comparing the expected relative future width of the skew-adaptive and classical scaled-score intervals. Experiments on a variety of datasets indicate gains in prediction interval efficiency over the scaled-score construction and conformalized quantile regression, and show that the proposed estimator closely matches the corresponding average width ratio observed on the test sample.

Density estimation and reliable prediction regions for outputs are crucial in supervised and unsupervised learning. While conformal prediction effectively generates coverage-guaranteed regions, it struggles with multi-dimensional outputs due to reliance on one-dimensional nonconformity scores. To address this, we introduce CONTRA: CONformal prediction region via normalizing flow TRAnsformation. CONTRA utilizes the latent spaces of normalizing flows to define nonconformity scores based on distances from the center. This allows for the mapping of high-density regions in latent space to sharp prediction regions in the output space, surpassing traditional hyperrectangular or elliptical conformal regions. Further, for scenarios where other predictive models are favored over flow-based models, we extend CONTRA to enhance any such model with a reliable prediction region by training a simple normalizing flow on the residuals. We demonstrate that both CONTRA and its extension maintain guaranteed coverage probability and outperform existing methods in generating accurate prediction regions across various datasets. We conclude that CONTRA is an effective tool for (conditional) density estimation, addressing the under-explored challenge of delivering multi-dimensional prediction regions.

We study a class of bilevel optimization problems in which both the upper- and lower-level problems have minimax structures. This setting captures a broad range of emerging applications. Despite the extensive literature on bilevel optimization and minimax optimization separately, existing methods mainly focus on bilevel optimization with lower-level minimization problems, often under strong convexity assumptions, and are not directly applicable to the minimax lower-level setting considered here. To address this gap, we develop penalty-based first-order methods for bilevel minimax optimization without requiring strong convexity of the lower-level problem. In the deterministic setting, we establish that the proposed method finds an $ε$-KKT point with $\tilde{O}(ε^{-4})$ oracle complexity. We further show that bilevel problems with convex constrained lower-level minimization can be reformulated as special cases of our framework via Lagrangian duality, leading to an $\tilde{O}(ε^{-4})$ complexity bound that improves upon the existing $\tilde{O}(ε^{-7})$ result. Finally, we extend our approach to the stochastic setting, where only stochastic gradient oracles are available, and prove that the proposed stochastic method finds a nearly $ε$-KKT point with $\tilde{O}(ε^{-9})$ oracle complexity.

We consider a non-convex constrained optimization problem, where the objective function is weakly convex and the constraint function is either convex or weakly convex. To solve this problem, we consider the classical switching subgradient method, which is an intuitive and easily implementable first-order method whose oracle complexity was only known for convex problems. This paper provides the first analysis on the oracle complexity of the switching subgradient method for finding a nearly stationary point of non-convex problems. Our results are derived separately for convex and weakly convex constraints. Compared to existing approaches, especially the double-loop methods, the switching gradient method can be applied to non-smooth problems and achieves the same complexity using only a single loop, which saves the effort on tuning the number of inner iterations.