What are Russia's gains from the Iran war? 'We are not losers; we are winners' Ukrainian officials say Russian drones have again attacked the southern port city of Odesa, injuring at least 11 people, including two children, and damaging homes and important infrastructure. Odesa Governor Oleh Kiper said the attack affected three districts, hitting residential buildings, vehicles and civilian facilities, including a hotel, warehouses and funicular railway. Windows shattered in many buildings and the port area sustained damage. Law enforcement agencies are documenting the latest war crimes committed by Russia against the peaceful population of [the] Odesa region," Kiper said. Russian attacks killed one person in the southeastern Zaporizhzhia region, according to Governor Ivan Fedorov. "A 59-year-old man died as a result of an enemy attack on the Zaporizhzhia region," Fedorov wrote on Telegram. A Ukrainian drone attack killed an employee at the Zaporizhzhia nuclear power plant, which was captured by Russian forces and is shut down. "A driver was killed today when a Ukrainian Armed Forces drone struck the transport department at the Zaporizhzhia Nuclear Power Plant," said a statement from plant managers who were installed by Russia. Regional governor Fedorov said Russian forces launched 629 strikes across 45 settlements in the region in a single day, with at least 50 reports of damage to homes and infrastructure. Russian officials reported Ukrainian drone attacks in the Belgorod border region, where at least one person was killed and four women injured, alongside damage to buildings and vehicles. The attacks come as diplomatic efforts to end the war remain stalled. Donald Trump said on Sunday that he has had "good conversations" with Presidents Vladimir Putin and Volodymyr Zelenskyy. "We're working on the Russia situation, Russia and Ukraine, and hopefully we're going to get it," Trump said on Fox News. "I do have conversations with him, and I do have conversations with President Zelenskyy, and good conversations," he said. "The hatred between President Putin and President Zelenskyy is ridiculous.

Mixability has been shown to be a powerful tool to obtain algorithms with optimal regret. However, the resulting methods often suffer from high computational complexity which has reduced their practical applicability. For example, in the case of multiclass logistic regression, the aggregating forecaster (Foster et al. (2018)) achieves a regret of O(log(Bn)) whereas Online Newton Step achieves O(eBlog(n)) obtaining a double exponential gain in B (a bound on the norm of comparative functions). However, this high statistical performance is at the price of a prohibitive computational complexity O(n37). In this paper, we use quadratic surrogates to make aggregating forecasters more efficient. We show that the resulting algorithm has still high statistical performance for a large class of losses. In particular, we derive an algorithm for multi-class logistic regression with a regret bounded by O(Blog(n)) and a computational complexity of only O(n4).

Rejection sampling is a technique for sampling from difficult distributions. However, its use is limited due to a high rejection rate. Common adaptive rejection sampling methods either work only for very specific distributions or without performance guarantees. In this paper, we present pliable rejection sampling (PRS), a new approach to rejection sampling, where we learn the sampling proposal using a kernel estimator. Since our method builds on rejection sampling, the samples obtained are with high probability i.i.d. and distributed according to f. Moreover, PRS comes with a guarantee on the number of accepted samples.

As the demand to integrate Artificial Intelligence into high-stakes environments continues to grow, explaining the reasoning behind neural-network predictions has shifted from a theoretical curiosity to a strict operational requirement. Our work is motivated by the explanations of autoregressive neural predictions on dynamic physical fields, as in weather forecasting. Gradient-based feature attribution methods are widely used to explain the predictions on such data, in particular due to their scalability to high-dimensional inputs. It is also interesting to remark that gradient-based techniques such as SmoothGrad are now standard on images to robustify the explanations using pointwise averages of the attribution maps obtained from several noised inputs. Our goal is to efficiently adapt this aggregation strategy to dynamic physical fields. To do so, our first contribution is to identify a fundamental failure mode when averaging perturbed attribution maps on dynamic physical fields: stochastic input perturbations do not induce stationary amplitude noise in attribution maps, but instead cause a geometric displacement of the attributions. Consequently, pointwise averaging blurs these spatially misaligned features. To tackle this issue, we introduce WassersteinGrad, which extracts a geometric consensus of perturbed attribution maps by computing their entropic Wasserstein barycenter. The results, obtained on regional weather data and a meteorologist-validated neural model, demonstrate promising explainability properties of WassersteinGrad over gradient-based baselines across both single-step and autoregressive forecasting settings.