We propose a robust gradient estimator based on per-sample gradient clipping and analyze its properties both theoretically and empirically. We show that the resulting method, per-sample clipped SGD (PS-Clip-SGD), achieves optimal in-expectation convergence rates for non-convex optimization problems under heavy-tailed gradient noise. Moreover, we establish high-probability convergence guarantees that match the in-expectation rates up to polylogarithmic factors in the failure probability. We complement our theoretical results with multiple numerical experiments. In particular, we demonstrate that PS-Clip-SGD outperforms both vanilla SGD with momentum and standard gradient clipping when training AlexNet on the CIFAR-100 dataset, even after accounting for the additional computational time caused by per-sample clipping. We also empirically show that, in the presence of gradient accumulation, applying clipping at the mini-batch level can improve training performance while incurring virtually no additional computational cost. This finding is particularly interesting, as it contradicts the common practice of applying clipping only after all accumulation steps have been completed.

We consider the infinite-width limit of a fully connected deep neural network with general weights, and we prove quantitative general bounds on the $2$-Wasserstein distance between the network and its infinite-width Gaussian limit, under appropriate regularity assumptions on the activation function. Our main tool is a Lindeberg principle for Deep Neural Networks, which we use to successively replace the weights on each layer by Gaussian random variables.

Probabilistic values, including Shapley values and semivalues, provide a model-agnostic framework to attribute the behavior of a black-box model to data points or features, with a wide range of applications including explainable artificial intelligence and data valuation. However, their exact computation requires utility evaluations over exponentially many coalitions, making Monte Carlo approximation essential in modern machine learning applications. Existing estimators are often developed through different identification strategies, including weighted averages, self-normalized weighting, regression adjustment, and weighted least squares. Our key observation is that these seemingly distinct constructions share a common first-order error structure, in which the leading term is an augmented inverse-probability weighted influence term determined by the sampling law and a working surrogate function. This first-order representation yields an explicit expression for the leading mean squared error (MSE), which characterizes how the sampling law and the surrogate jointly determine statistical efficiency. Guided by this criterion, we propose an Efficiency-Aware Surrogate-adjusted Estimator (EASE) that directly chooses the sampling law and surrogate to minimize the first-order MSE. We demonstrate that EASE consistently outperforms state-of-the-art estimators for various probabilistic values.

The dust at this busy recycling plant is pervasive and the steady noise of hoppers and conveyor belts makes this a challenging environment to work in. The facility in Rainham, east London is owned by Sharp Group, a family-run skip and waste management firm. Along the conveyor belts runs everything you could imagine, from shoes, to old VHS cassettes and blocks of concrete. The team here processes up to 280,000 tonnes of mixed recycling every year with 24 agency workers on its rapid conveyor belts. This is a hazardous industry.

OpenAI's cofounder and president revealed in federal court on Monday that he's one of the largest individual stakeholders in the AI lab. Two days before the Musk v. Altman trial began, Elon Musk asked OpenAI cofounder and president Greg Brockman about reaching a settlement. When Brockman suggested both sides drop their claims, Musk responded, "By the end of this week, you and Sam [Altman] will be the most hated men in America. If you insist, so be it." The message --which OpenAI's lawyers made public on Sunday, and which Judge Yvonne Gonzalez Rogers subsequently refused to let the jury hear about--underscores what may be Musk's larger goal in this trial.

Things have not been looking good for Spirit Airlines for years now. The budget airline known for its bare-bones approach to the sky filed for bankruptcy in 2024 and then again in 2025. And yet, its demise on Saturday felt sudden and shocking: Spirit said it would go out of business, canceling flights, shuttering its customer service lines, and laying off workers without warning. What does it mean for flyers, and for the busy summer travel season? WIRED spoke to experts to find out.

A federal review of new AI models ahead of their public release is being considered as a possible power for that committee, according to the publication's sources. No clear approach has been decided, but the suggested it could mimic what's currently happening within the UK government, where multiple layers of oversight confirm that AI models meet safety standards. There's also still a chance the entire concept fizzles and comes to nothing. If an oversight group is created, it would mark quite a reversal from the hands-off attitude presented in the White House's previously introduced AI Action Plan. The plan appeared willing to offer the AI companies most of the concessions they wanted, although it did leave a lot of potential to create plenty of new problems .

What to Know About Sony's $7.85 Million PlayStation Settlement Are you eligible for a payout? Probably, but it might take a while and will likely be pretty small. Sony, owner of the PlayStation brand, has been accused of antitrust practices. The lawsuit was originally settled in 2024 but was rejected twice during the approval process. Last week, a judge approved a preliminary reopening of the settlement.

A text scam impersonating state courts demands drivers pay $6.99 for fake traffic violations via QR codes. The scheme has hit residents in at least eight U.S. states.

BirdCast season is here once again. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The species migrates annually from South America to North America to breed, traveling thousands of miles each spring. Breakthroughs, discoveries, and DIY tips sent six days a week. Tonight, there will be more birds in the sky than there are people in the United States.