US judge rejects bid to break up Google's ad business Share US judge rejects bid to break up Google's ad business on social media Alphabet's Google has escaped a breakup of its advertising technology business, marking the third time in recent years that United States antitrust enforcers have tried to force a Big Tech breakup and lost. US Judge Leonie Brinkema in Alexandria, Virginia, on Wednesday declined to make Google sell AdX, where publishers pay Google a 20 percent fee to sell ads in auctions that happen instantly when users load websites. The judge accepted behavioural remedies. The reasoning behind today's decision was not immediately made public. Brinkema filed her opinion under seal for 14 days, leaving the details of how Google must change its ad business unknown for now.

Judge declines to make Google sell AdX in win for firm against US antitrust enforcers' attempts to break up big tech Alphabet's Google escaped a breakup of its advertising technology business on Wednesday, when a judge in Virginia rejected US antitrust enforcers' attempt to force a sale of Google's online advertising exchange. While the ad exchange is a small part of Google's business, the ruling is the second powerful symbolic victory against the US Department of Justice in its efforts to force Google to sell assets to address illegal monopolies. US judge Leonie Brinkema in Alexandria, Virginia, declined to make Google sell AdX, where publishers pay Google a 20% fee to sell ads in auctions that happen instantly when users load websites. She accepted most of the parties' proposed behavioral remedies. The justice department and a broad coalition of states sued Google in 2023 over its dominance in markets for advertising technology used by online publishers and websites.

The John ellipsoid problem has far-reaching applications in numerous fields of computer science.

The field of learning-augmented online algorithms has gained rapid prominence in recent years.

Policy inference plays an essential role in the contextual bandit problem. In this paper, we use empirical likelihood to develop a Bayesian inference method for the joint analysis of multiple contextual bandit policies in finite sample regimes. The proposed inference method is robust to small sample sizes and is able to provide accurate uncertainty measurements for policy value evaluation. In addition, it allows for flexible inferences on policy comparison with full uncertainty quantification. We demonstrate the effectiveness of the proposed inference method using Monte Carlo simulations and its application to an adolescent body mass index data set.

Inparticular,standardmetricformulations as hierarchical k-center,k-means, andk-median received a lot of attention and the problems have been studied extensively in different models of computation.