What are Russia's gains from the Iran war? 'We are not losers; we are winners' Russia has continued heavy attacks on Ukraine for the past 24 hours, with several coming overnight on Thursday and in the early hours of Friday. At least one person has been killed and several have been injured. A Russian drone attack overnight damaged port infrastructure in Ukraine's southern Odesa region and wounded two people in the Black Sea port city of Odesa, regional Governor Oleh Kiper said on Friday morning. Two high-rise residential buildings were damaged in the attack, which destroyed apartments and caused fires, Kiper wrote on the Telegram messaging app. "This night, Russia again massively attacked the civilian infrastructure of the Odesa region: two people were injured," he said.

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

What are Russia's gains from the Iran war? 'We are not losers; we are winners' What do Ukraine's robot soldiers mean for the future of warfare? In a scene reminiscent of a computer war game, three battle-fatigued soldiers, dressed in white snow camouflage, emerge from a war-torn alley with their hands raised above their heads. They crouch down, following the orders being blasted at them, fear and shock etched across their faces as they stare down the barrel of a machinegun mounted on a so-called ground robot. In April, Ukrainian President Volodymyr Zelenskyy said that, for the "first time in the history of this war, an enemy position was taken exclusively by unmanned platforms - ground systems and drones". "Ground robotic systems have already carried out more than 22,000 missions on the front in just three months," he wrote in a post on X, alongside images of green machines with tank tracks and weapons mounted on top.

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

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

We study the worst-case adaptive optimization problem with budget constraint that is useful for modeling various practical applications in artificial intelligence and machine learning. We investigate the near-optimality of greedy algorithms for this problem with both modular and non-modular cost functions. In both cases, we prove that two simple greedy algorithms are not near-optimal but the best between them is near-optimal if the utility function satisfies pointwise submodularity and pointwise cost-sensitive submodularity respectively. This implies a combined algorithm that is near-optimal with respect to the optimal algorithm that uses half of the budget. We discuss applications of our theoretical results and also report experiments comparing the greedy algorithms on the active learning problem.

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

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

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