Since the conflict in the Middle East began on Feb. 28, Emirates has cancelled more than 2,000 flights -- 54% of scheduled services, according to data from Cirium. As Emirates flight EK10 from London cruised over Saudi Arabia on Monday, news broke of a drone strike at its destination, Dubai. The aircraft turned back to Gatwick, flight data shows, completing a 9,100 km round trip -- one of dozens of flights to nowhere triggered by the Middle East war. Roughly 30 Emirates flights heading to Dubai International Airport were also ordered back or rerouted after Iranian drone attacks temporarily shut what is normally the world's busiest airport for international passengers. Passengers expecting a dawn landing in the glitzy United Arab Emirates port city were stunned. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

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We propose nonparametric identification and semiparametric estimation of joint potential outcome distributions in the presence of confounding. First, in settings with observed confounding, we derive tighter, covariate-informed bounds on the joint distribution by leveraging conditional copulas. To overcome the non-differentiability of bounding min/max operators, we establish the asymptotic properties for both a direct estimator with polynomial margin condition and a smooth approximation with log-sum-exp operator, facilitating valid inference for individual-level effects under the canonical rank-preserving assumption. Second, we tackle the challenge of unmeasured confounding by introducing a causal representation learning framework. By utilizing instrumental variables, we prove the nonparametric identifiability of the latent confounding subspace under injectivity and completeness conditions. We develop a ``triple machine learning" estimator that employs cross-fitting scheme to sequentially handle the learned representation, nuisance parameters, and target functional. We characterize the asymptotic distribution with variance inflation induced by representation learning error, and provide conditions for semiparametric efficiency. We also propose a practical VAE-based algorithm for confounding representation learning. Simulations and real-world analysis validate the effectiveness of proposed methods. By bridging classical semiparametric theory with modern representation learning, this work provides a robust statistical foundation for distributional and counterfactual inference in complex causal systems.

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

Statistical learning theory is the foundation of machine learning, providing theoretical bounds for the risk of models learned from a (single) training set, assumed to issue from an unknown probability distribution.

A.I. re-creations of the "Magnificent Ambersons" stars Joseph Cotten, Agnes Moorehead, Dolores Costello, and Tim Holt. Edward Saatchi first saw "The Magnificent Ambersons," Orson Welles's mangled masterpiece from 1942, when he was twelve years old, in the private screening room of his family's crenellated mansion, in West Sussex. Saatchi's parents had already shown him and his brother "Citizen Kane." But "Ambersons," Welles's follow-up film, about a wealthy Midwestern clan brought low, came with a bewitching backstory: R.K.O. had ripped the movie from the director's hands, slashed forty-three minutes, tacked on a happy ending, and destroyed the excised footage in order to free up vault space, leaving decades' worth of cinephiles to obsess over what might have been. Part of this outcome was the result of studio treachery, but Welles, owing to some combination of hubris and distraction, had let his film slip from his grasp. Saatchi recalled, "Around the family dinner table, that was always such a big topic: How much was Welles responsible for this? Mum was always quite tough on him." Saatchi's father, Maurice, a baron also known as Lord Saatchi, is one of two Iraqi British brothers who founded the advertising firm Saatchi & Saatchi, in 1970, which led their family to become one of the richest in the U.K. Edward's mother, Josephine Hart, who died in 2011, was an Irish writer best known for her erotic thriller "Damage," which was adapted into a film by Louis Malle. Edward, born in 1985, grew up in London and at the sprawling country estate, surrounded by palatial gardens and classical statuary. He described his parents as "movie mad." The actor and Welles biographer Simon Callow, a Saatchi family friend, recalled, "They had a cinema of their own inside the house, and it was a ritual of theirs every week to watch a film together." Aside from old movies, Edward was obsessed with "Star Trek"--especially the Holodeck, a device that conjured simulated 3-D worlds populated by characters who could interact with the members of the Starship Enterprise. That kind of wizardry didn't exist in the real world, at least not yet. But the young prince of the Saatchi castle had faith that someday it would, and that it could bring the original "Ambersons" back from oblivion. "To me, this is the lost holy grail of cinema," Saatchi told me recently, like Charles Foster Kane murmuring about Rosebud. "It just seemed intuitively that there would be some way to undo what had happened."

Distributionally robust optimisation (DRO) minimises the worst-case expected loss over an ambiguity set that can capture distributional shifts in out-of-sample environments. While Huber (linear-vacuous) contamination is a classical minimal-assumption model for an $\varepsilon$-fraction of arbitrary perturbations, including it in an ambiguity set can make the worst-case risk infinite and the DRO objective vacuous unless one imposes strong boundedness or support assumptions. We address these challenges by introducing bulk-calibrated credal ambiguity sets: we learn a high-mass bulk set from data while considering contamination inside the bulk and bounding the remaining tail contribution separately. This leads to a closed-form, finite $\mathrm{mean}+\sup$ robust objective and tractable linear or second-order cone programs for common losses and bulk geometries. Through this framework, we highlight and exploit the equivalence between the imprecise probability (IP) notion of upper expectation and the worst-case risk, demonstrating how IP credal sets translate into DRO objectives with interpretable tolerance levels. Experiments on heavy-tailed inventory control, geographically shifted house-price regression, and demographically shifted text classification show competitive robustness-accuracy trade-offs and efficient optimisation times, using Bayesian, frequentist, or empirical reference distributions.

We introduce Neural Optimal Design of Experiments, a learning-based framework for optimal experimental design in inverse problems that avoids classical bilevel optimization and indirect sparsity regularization. NODE jointly trains a neural reconstruction model and a fixed-budget set of continuous design variables representing sensor locations, sampling times, or measurement angles, within a single optimization loop. By optimizing measurement locations directly rather than weighting a dense grid of candidates, the proposed approach enforces sparsity by design, eliminates the need for l1 tuning, and substantially reduces computational complexity. We validate NODE on an analytically tractable exponential growth benchmark, on MNIST image sampling, and illustrate its effectiveness on a real world sparse view X ray CT example. In all cases, NODE outperforms baseline approaches, demonstrating improved reconstruction accuracy and task-specific performance.

We show that quantum oracles provide an advantage over classical oracles for answering classical counterfactual questions in causal models, or equivalently, for identifying unknown causal parameters such as distributions over functional dependences. In structural causal models with discrete classical variables, observational data and even ideal interventions generally fail to answer all counterfactual questions, since different causal parameters can reproduce the same observational and interventional data while disagreeing on counterfactuals. Using a simple binary example, we demonstrate that if the classical variables of interest are encoded in quantum systems and the causal dependence among them is encoded in a quantum oracle, coherently querying the oracle enables the identification of all causal parameters -- hence all classical counterfactuals. We generalize this to arbitrary finite cardinalities and prove that coherent probing 1) allows the identification of all two-way joint counterfactuals p(Y_x=y, Y_{x'}=y'), which is not possible with any number of queries to a classical oracle, and 2) provides tighter bounds on higher-order multi-way counterfactuals than with a classical oracle. This work can also be viewed as an extension to traditional quantum oracle problems such as Deutsch--Jozsa to identifying more causal parameters beyond just, e.g., whether a function is constant or balanced. Finally, we raise the question of whether this quantum advantage relies on uniquely non-classical features like contextuality. We provide some evidence against this by showing that in the binary case, oracles in some classically-explainable theories like Spekkens' toy theory also give rise to a counterfactual identifiability advantage over strictly classical oracles.

Random Forests and Gradient Boosting are among the most effective algorithms for supervised learning on tabular data. Both belong to the class of tree-based ensemble methods, where predictions are obtained by aggregating many randomized regression trees. In this paper, we develop a theoretical framework for analyzing such methods through Reproducing Kernel Hilbert Spaces (RKHSs) constructed on tree ensembles--more precisely, on the random partitions generated by randomized regression trees. We establish fundamental analytical properties of the resulting Random Forest kernel, including boundedness, continuity, and universality, and show that a Random Forest predictor can be characterized as the unique minimizer of a penalized empirical risk functional in this RKHS, providing a variational interpretation of ensemble learning. We further extend this perspective to the continuous-time formulation of Gradient Boosting introduced by Dombry and Duchamps (2024a,b), and demonstrate that it corresponds to a gradient flow on a Hilbert manifold induced by the Random Forest RKHS. A key feature of this framework is that both the kernel and the RKHS geometry are data-dependent, offering a theoretical explanation for the strong empirical performance of tree-based ensembles. Finally, we illustrate the practical potential of this approach by introducing a kernel principal component analysis built on the Random Forest kernel, which enhances the interpretability of ensemble models, as well as GVI, a new geometric variable importance criterion.