We study an online decision making problem where on each round a learner chooses a list of items based on some side information, receives a scalar feedback value for each individual item, and a reward that is linearly related to this feedback. These problems, known as contextual semibandits, arise in crowdsourcing, recommendation, and many other domains. This paper reduces contextual semibandits to supervised learning, allowing us to leverage powerful supervised learning methods in this partial-feedback setting. Our first reduction applies when the mapping from feedback to reward is known and leads to a computationally efficient algorithm with near-optimal regret. We show that this algorithm outperforms state-of-the-art approaches on real-world learning-to-rank datasets, demonstrating the advantage of oracle-based algorithms. Our second reduction applies to the previously unstudied setting when the linear mapping from feedback to reward is unknown. Our regret guarantees are superior to prior techniques that ignore the feedback.

In this work, we propose an infinite restricted Boltzmann machine (RBM), whose maximum likelihood estimation (MLE) corresponds to a constrained convex optimization. We consider the Frank-Wolfe algorithm to solve the program, which provides a sparse solution that can be interpreted as inserting a hidden unit at each iteration, so that the optimization process takes the form of a sequence of finite models of increasing complexity. As a side benefit, this can be used to easily and efficiently identify an appropriate number of hidden units during the optimization. The resulting model can also be used as an initialization for typical state-of-the-art RBM training algorithms such as contrastive divergence, leading to models with consistently higher test likelihood than random initialization.

Probabilistic inference serves as a popular model for neural processing. It is still unclear, however, how approximate probabilistic inference can be accurate and scalable to very high-dimensional continuous latent spaces. Especially as typical posteriors for sensory data can be expected to exhibit complex latent dependencies including multiple modes. Here, we study an approach that can efficiently be scaled while maintaining a richly structured posterior approximation under these conditions. As example model we use spike-and-slab sparse coding for V1 processing, and combine latent subspace selection with Gibbs sampling (selectand-sample).

McDonald's boss on abuse claims: 'I don't want to talk about the past' The boss of McDonald's UK and Ireland has said she doesn't want to talk about the past when asked about allegations of abuse at the fast-food chain. Lauren Schultz told the BBC what had happened in recent years was unacceptable but said we have drawn a line under it. A BBC investigation in 2023 heard from more than 100 McDonald's workers in the UK claiming they faced a toxic culture of sexual assault, harassment, racism, and bullying. Last year, staff said they still faced sexual abuse and harassment. The UK equality watchdog agreed tougher measures with the company to protect staff in November, including new sexual harassment training.

Anthropic is investigating a claim that a small group of people gained access to its Claude Mythos model - the cyber-security tool which the AI firm says is too powerful to release to the public. We're investigating a report claiming unauthorized access to Claude Mythos Preview through one of our third-party vendor environments, the company said in a statement. It was in response to a Bloomberg report that users in a private forum managed to access the model without the normal permissions. There is deep unease about Mythos' capabilities - though the UK's top cyber official has said advanced AI tools could be a net positive if the technology was secured from misuse. There is currently no suggestion that malicious actors have managed to get hold of the model, and Anthropic says it does not have evidence its systems are affected.

What He's Doing Now Boggles the Mind. My partner, a 36-year-old man, is being let go from his job. He was informed that his company would be cutting him and his entire department. The only person staying is his boss, who will be overseeing the new AI customer service client they are replacing the real life people with. But that's not what this is about, even if AI is going to be the death of humanity.

Pose your questions ahead of our May 8 livestream about the trial that could determine the fate of OpenAI. Two of Big Tech's most influential billionaires, Sam Altman and Elon Musk, will go head-to-head in a highly anticipated trial beginning April 27. In Musk v. Altman a judge, advised by a jury, will ultimately determine whether OpenAI has strayed from its founding mission to ensure that artificial general intelligence (AGI) benefits humanity, and the ruling could influence how the world's leading AI developer controls and distributes its technology. For now, you can learn more about the trial here . On May 8, a panel of WIRED experts will go live to answer your questions about this consequential case.

One town's scheme to get rid of its geese Public officials in one California burgh spent nearly $400,000 on tech to flush out waterfowl. Some geese, like the one on the left, wear GPS trackers as part of the Foster City goose management plan. Our target is in sight: a gaggle of Canada geese, pecking at grass near the dog park. As I approach, tiptoeing over their grayish-white poop, I notice that one bird wears a white cuff around its slender black neck. It's a GPS tracker--part of a new tech-centered campaign to drive the geese out of my hometown of Foster City, California. About 300 geese live in this sleepy Bay Area suburb, equal to nearly 1% of our human population--and some say this town isn't big enough for the both of us.

No part of the globe is free of human fingerprints. Should we deploy technology to change it back? When people talk about "nature," they're generally talking about things that aren't made by human beings. But while there is plenty of God's creation to go around, it is hard to think of anything on Earth that human hands haven't affected. In the Brazilian rainforest, scientists have found microplastics in the bellies of animals ranging from red howler monkeys to manatees. In remotest Yakutia, where much of the earth remains untrodden by human feet, the carbon in the sky above melts the permafrost below.