Pirates vs. Diamondbacks betting preview targets the under as both offenses go cold in series Former LSU coach Brian Kelly uses AI to prepare for job interviews, proving he's just like the rest of us Newsom office source responds to planned protest against trans athlete at state playoff girls' track meet Framber Valdez gets what he deserves for punk move, suspended six games after drilling Boston's Trevor Story MLB's new automated strike zone has a hidden feature helping umpires become more accurate than ever FIFA's World Cup ticket defense falls apart when compared to college football and NFL playoff prices Sophie Cunningham tells Angel Reese to'move on' after she reposted boyfriend dunking on rumored ex US blockade keeps stranglehold on Iran's economy Pratt issues SHOCKING WARNING to socialist opponent: 'Stabbed in the NECK!' 'Fox & Friends' explores wearable technology's role in health and wellness DraftKings has Detroit as a -3.5 favorite with Cade Cunningham leading the postseason in scoring at 31.3 PPG Dan weighs in on ESPN allegedly going after Steve Kerr to join their team. The Detroit Pistons punched Cleveland in the mouth in Game 1, beating the Cavaliers 111-101 at Little Caesars Arena. Now, the Cavs try to answer in Game 2 of the Eastern Conference semifinals in the 2026 NBA Playoffs NBA Playoffs Tuesday at 7 p.m. ET on Amazon Prime Video. DraftKings has Detroit as a -155 moneyline favorite, -3.5 against the spread, with a 215.5 total. The Pistons were better in the opener; their style travels better in this matchup, and Cleveland still hasn't proven it can win on the road this postseason.

Statistical validity is a widely recognized crucial feature of modern science.

Tables 3 and 4 summarize hyperparameters for P A TE-FM and ALIBI respectively. Table 3: P A TE-FM (Algorithms 1 and 2) hyperparameters for select accuracy levels. By repeating this game multiple times, we can estimate the adversary's success rate and convert this The probability is taken over the bit b, the randomness of the mechanism M and the algorithm A. Theorem B.1. It now remains to be seen how we can bound the adversary's correct guessing rate "canaries", we can compute a lower bound on the adversary's We can improve the tightness of this bound further. The adversary simply looks at the model's confidence on (Game 3).

The Dallas Cowboys had just won the Vince Lombardi Trophy, handing the Buffalo Bills their third straight loss in the Super Bowl. Bill Clinton was sworn into office as the 42nd president of the United States. And American music icon Prince became The Artist Formerly Known as Prince. It was also the last time a Canadian hockey team won the Stanley Cup. On Saturday night, the Edmonton Oilers hope to take the first step toward breaking that drought when they take on the Florida Panthers in Game 1 of the Stanley Cup Final.

Zero-sum games arise in a wide variety of problems, including robust optimization and adversarial learning. However, algorithms deployed for finding a local Nash equilibrium in these games often converge to non-Nash stationary points. This highlights a key challenge: for any algorithm, the stability properties of its underlying dynamical system can cause non-Nash points to be potential attractors. To overcome this challenge, algorithms must account for subtleties involving the curvatures of players' costs. To this end, we leverage dynamical system theory and develop a second-order algorithm for finding a local Nash equilibrium in the smooth, possibly nonconvex-nonconcave, zero-sum game setting. First, we prove that this novel method guarantees convergence to only local Nash equilibria with a local linear convergence rate. We then interpret a version of this method as a modified Gauss-Newton algorithm with local superlinear convergence to the neighborhood of a point that satisfies first-order local Nash equilibrium conditions. In comparison, current related state-of-the-art methods do not offer convergence rate guarantees. Furthermore, we show that this approach naturally generalizes to settings with convex and potentially coupled constraints while retaining earlier guarantees of convergence to only local (generalized) Nash equilibria.

Many real-world multi-agent interactions consider multiple distinct criteria, i.e. the payoffs are multi-objective in nature. However, the same multi-objective payoff vector may lead to different utilities for each participant. Therefore, it is essential for an agent to learn about the behaviour of other agents in the system. In this work, we present the first study of the effects of such opponent modelling on multi-objective multi-agent interactions with non-linear utilities. Specifically, we consider two-player multi-objective normal form games with non-linear utility functions under the scalarised expected returns optimisation criterion. We contribute novel actor-critic and policy gradient formulations to allow reinforcement learning of mixed strategies in this setting, along with extensions that incorporate opponent policy reconstruction and learning with opponent learning awareness (i.e., learning while considering the impact of one's policy when anticipating the opponent's learning step). Empirical results in five different MONFGs demonstrate that opponent learning awareness and modelling can drastically alter the learning dynamics in this setting. When equilibria are present, opponent modelling can confer significant benefits on agents that implement it. When there are no Nash equilibria, opponent learning awareness and modelling allows agents to still converge to meaningful solutions that approximate equilibria.

It wasn't easy and it wasn't especially pretty, but world chess champion Magnus Carlsen has successfully defended his crown in what was scheduled to be a 12-game match against world No. 2 Fabiano Caruana. After all 12 of those games were drawn, the victor was decided via a best-of-four series of "rapid chess" contests, in which each player has about 30 minutes to complete all his moves. The Norwegian Carlsen, by far the world's No. 1 player at rapid chess, predictably dominated Caruana, who entered the match ranked only No. 8 in the format, winning the playoff games 3-0 and retaining his title for another two years. What kind of match was it? A bit dull, to be honest, at least until Wednesday's rapid games. Top-level chess isn't the romantic game it once was, and it's becoming less and less romantic every year.

Amazon's Alexa voice assistant is a handy, seamless way to listen to music and find out about the weather. As the NBA finals head into tonight's Game 2 between the Golden State Warriors and the Cleveland Cavaliers, though, the voice assistant is also dabbling in sports analysis. If you ask Alexa "Who will win the NBA Finals this year," it gives you the following dissertation: "Even with both conference finals going to game 7, these playoffs were over before they even started. I think the Warriors will win the playoffs pretty handily, and the rest of the league will spend the off-season trying to figure out what they will do to damper the dynasty." You'd be forgiven for thinking that Alexa is showing some bias – the Warriors' home base in Oakland is much closer than the Cav's HQ to both Amazon's Seattle headquarters and to Silicon Valley, which you might call Alexa's spiritual home.