Could Ukraine hold a presidential election right now? Will Europe use frozen Russian assets to fund war? How can Ukraine rebuild China ties? 'Ukraine is running out of men, money and time' Russian drone attack kills 4 in Ukraine's Kharkiv as peace remains elusive A Russian drone attack on Ukraine's northeastern city of Kharkiv has killed at least four people and wounded six, officials have said, just hours after Washington accused Moscow of "dangerous and inexplicable escalation" of the war and as a peace deal remains distant. Kharkiv Regional Governor Oleh Syniehubov said Tuesday that the death toll from the attack on the outskirts of the frequently targeted city, just 30km (19 miles) from the border, had risen to four.

The sociology professor is suitably comfortable with AI helpers that he creates his own - it's their inventors' motives and unregulated environment he argues we should be concerned about I f much of the discussion of AI risk conjures doomsday scenarios of hyper-intelligent bots brandishing nuclear codes, perhaps we should be thinking closer to home. In his urgent, humane book, sociologist James Muldoon urges us to pay more attention to our deepening emotional entanglements with AI, and how profit-hungry tech companies might exploit them. A research associate at the Oxford Internet Institute who has previously written about the exploited workers whose labour makes AI possible, Muldoon now takes us into the uncanny terrain of human-AI relationships, meeting the people for whom chatbots aren't merely assistants, but friends, romantic partners, therapists, even avatars of the dead. To some, the idea of falling in love with an AI chatbot, or confiding your deepest secrets to one, might seem mystifying and more than a little creepy. But Muldoon refuses to belittle those seeking intimacy in "synthetic personas".

'Genius' chimpanzee Ai dies in Japan at 49 Studies involving Ai, a genius chimpanzee who has died at the age of 49, are said to have revealed various aspects of the chimpanzee mind. Ai, a genius chimpanzee that could recognize more than 100 Chinese characters and the English alphabet, has died at the age of 49, Japanese researchers have said. Ai, whose name meant love in Japanese, took part in studies on perception, learning and memory that advanced our understanding of primate intelligence, the Center for the Evolutionary Origins of Human Behavior at Kyoto University said in a statement. She died Friday from multiple organ failure and ailments related to old age, the school said. Aside from mastering Chinese characters and the alphabet, Ai could also identify the Arabic numerals from zero to nine and 11 colors, primatologist Tetsuro Matsuzawa said in 2014.

New York governor Kathy Hochul says she will propose a new law allowing limited autonomous vehicle pilots in smaller cities. Full-blown services could be next. As self-driving car services from Alphabet's Waymo, Amazon's Zoox, and Tesla have slowly, quietly expanded across the US, one big, important state has mostly stayed mum: New York . The union's fourth most populous state has some of the tightest laws governing autonomous vehicles, requiring companies approved to test in the state to only do so with a driver behind the wheel. There's no current path for companies to operate the sort of commercial robotaxi services like the sort seen in San Francisco or Las Vegas.

Outstanding claim liabilities are revised repeatedly as claims develop, yet most modern reserving models are trained as one-shot predictors and typically learn only from settled claims. We formulate individual claims reserving as a claim-level Markov decision process in which an agent sequentially updates outstanding claim liability (OCL) estimates over development, using continuous actions and a reward design that balances accuracy with stable reserve revisions. A key advantage of this reinforcement learning (RL) approach is that it can learn from all observed claim trajectories, including claims that remain open at valuation, thereby avoiding the reduced sample size and selection effects inherent in supervised methods trained on ultimate outcomes only. We also introduce practical components needed for actuarial use -- initialisation of new claims, temporally consistent tuning via a rolling-settlement scheme, and an importance-weighting mechanism to mitigate portfolio-level underestimation driven by the rarity of large claims. On CAS and SPLICE synthetic general insurance datasets, the proposed Soft Actor-Critic implementation delivers competitive claim-level accuracy and strong aggregate OCL performance, particularly for the immature claim segments that drive most of the liability.

We study differentially private ordinary least squares (DP-OLS) with bounded data. The dominant approach, adaptive sufficient-statistics perturbation (AdaSSP), adds an adaptively chosen perturbation to the sufficient statistics, namely, the matrix $X^{\top}X$ and the vector $X^{\top}Y$, and is known to achieve near-optimal accuracy and to have strong empirical performance. In contrast, methods that rely on Gaussian-sketching, which ensure differential privacy by pre-multiplying the data with a random Gaussian matrix, are widely used in federated and distributed regression, yet remain relatively uncommon for DP-OLS. In this work, we introduce the iterative Hessian mixing, a novel DP-OLS algorithm that relies on Gaussian sketches and is inspired by the iterative Hessian sketch algorithm. We provide utility analysis for the iterative Hessian mixing as well as a new analysis for the previous methods that rely on Gaussian sketches. Then, we show that our new approach circumvents the intrinsic limitations of the prior methods and provides non-trivial improvements over AdaSSP. We conclude by running an extensive set of experiments across standard benchmarks to demonstrate further that our approach consistently outperforms these prior baselines.

Clustering with bandit feedback refers to the problem of partitioning a set of items, where the clustering algorithm can sequentially query the items to receive noisy observations. The problem is formally posed as the task of partitioning the arms of an N-armed stochastic bandit according to their underlying distributions, grouping two arms together if and only if they share the same distribution, using samples collected sequentially and adaptively. This setting has gained attention in recent years due to its applicability in recommendation systems and crowdsourcing. Existing works on clustering with bandit feedback rely on a strong assumption that the underlying distributions are sub-Gaussian. As a consequence, the existing methods mainly cover settings with linearly-separable clusters, which has little practical relevance. We introduce a framework of ``nonparametric clustering with bandit feedback'', where the underlying arm distributions are not constrained to any parametric, and hence, it is applicable for active clustering of real-world datasets. We adopt a kernel-based approach, which allows us to reformulate the nonparametric problem as the task of clustering the arms according to their kernel mean embeddings in a reproducing kernel Hilbert space (RKHS). Building on this formulation, we introduce the KABC algorithm with theoretical correctness guarantees and analyze its sampling budget. We introduce a notion of signal-to-noise ratio for this problem that depends on the maximum mean discrepancy (MMD) between the arm distributions and on their variance in the RKHS. Our algorithm is adaptive to this unknown quantity: it does not require it as an input yet achieves instance-dependent guarantees.

Traditional reinforcement learning usually assumes either episodic interactions with resets or continuous operation to minimize average or cumulative loss. While episodic settings have many theoretical results, resets are often unrealistic in practice. The infinite-horizon setting avoids this issue but lacks non-asymptotic guarantees in online scenarios with unknown dynamics. In this work, we move towards closing this gap by introducing a reset-free framework called the periodic framework, where the goal is to find periodic policies: policies that not only minimize cumulative loss but also return the agents to their initial state distribution after a fixed number of steps. We formalize the problem of finding optimal periodic policies and identify sufficient conditions under which it is well-defined for tabular Markov decision processes. To evaluate algorithms in this framework, we introduce the periodic regret, a measure that balances cumulative loss with the terminal law constraint. We then propose the first algorithms for computing periodic policies in two multi-agent settings and show they achieve sublinear periodic regret of order $\tilde O(T^{3/4})$. This provides the first non-asymptotic guarantees for reset-free learning in the setting of $M$ homogeneous agents, for $M > 1$.

The $ρ$-posterior framework provides universal Bayesian estimation with explicit contamination rates and optimal convergence guarantees, but has remained computationally difficult due to an optimization over reference distributions that precludes intractable posterior computation. We develop a PAC-Bayesian framework that recovers these theoretical guarantees through temperature-dependent Gibbs posteriors, deriving finite-sample oracle inequalities with explicit rates and introducing tractable variational approximations that inherit the robustness properties of exact $ρ$-posteriors. Numerical experiments demonstrate that this approach achieves theoretical contamination rates while remaining computationally feasible, providing the first practical implementation of $ρ$-posterior inference with rigorous finite-sample guarantees.

Covariate shift arises when covariate distributions differ between source and target populations while the conditional distribution of the response remains invariant, and it underlies problems in missing data and causal inference. We propose a minimum Wasserstein distance estimation framework for inference under covariate shift that avoids explicit modeling of outcome regressions or importance weights. The resulting W-estimator admits a closed-form expression and is numerically equivalent to the classical 1-nearest neighbor estimator, yielding a new optimal transport interpretation of nearest neighbor methods. We establish root-$n$ asymptotic normality and show that the estimator is not asymptotically linear, leading to super-efficiency relative to the semiparametric efficient estimator under covariate shift in certain regimes, and uniformly in missing data problems. Numerical simulations, along with an analysis of a rainfall dataset, underscore the exceptional performance of our W-estimator.