We propose and analyze algorithms to solve a range of learning tasks under userlevel differential privacy constraints. Rather than guaranteeing only the privacy of individual samples, user-level DP protects a user's entire contribution (m 1 samples), providing more stringent but more realistic protection against information leaks. We show that for high-dimensional mean estimation, empirical risk minimization with smooth losses, stochastic convex optimization, and learning hypothesis classes with finite metric entropy, the privacy cost decreases as O(1/ m) as users provide more samples.

Datacentres could require at least 6GW of capacity by 2030 under government plans to expand AI infrastructure. Datacentres could require at least 6GW of capacity by 2030 under government plans to expand AI infrastructure. Sun 26 Apr 2026 03.00 EDTLast modified on Sun 26 Apr 2026 03.01 EDT One vision of the UKâ s future involves a decarbonised economy powered by clean, renewable energy. Another involves making the UK an AI superpower. The government departments responsible for these two visions do not appear to have agreed on their numbers.

I own 20 axolotls - people need to know they're not easy to look after When Emma Honeyfield's daughter Amber asked for an axolotl for her birthday, Emma never imagined it would lead to a collection of 20. The 37-year-old bought her daughter's first axolotl, Stitch, in September and has since fallen in love with their calming nature. Emma said Amber, eight, had always been difficult to buy for, so when she asked for one for her birthday, she couldn't say no. And the family, from Tredegar, Blaenau Gwent, are far from alone in seeking out the amphibians, which are critically endangered and only found in lakes and wetlands in southern Mexico City . The animal's cute, smiling face and appearance in the hugely popular Minecraft and Roblox games has seen an increase in the number of people keeping them as pets.

A still from animated film La Sélection Mécanique, directed by Jules Blachier. A still from animated film La Sélection Mécanique, directed by Jules Blachier. While emerging technology is banned from the Palme d'Or, an upstart movement is gaining investment and attention I n Cannes' darkened screening rooms, the supposed future of cinema flickered into life this week and it was strange. The first edition of the World AI film festival (WAIFF) showcased visions of men with fish scales erupting from their necks and seaweed from their mouths, a heroine with a heart beating outside her body and so many massed armies of AI-generated tanned men sweeping across battlefields that David Lean would have blushed. Last week the Cannes film festival, entering its 76th year, banned the emerging technology from its Palme d'Or competition, insisting "AI imitates very well but it will never feel deep emotions".

Capturing accurate uncertainty quantification of the predictions from deep neural networks is important in many real-world decision-making applications. A reliable predictor is expected to be accurate when it is confident about its predictions and indicate high uncertainty when it is likely to be inaccurate. However, modern neural networks have been found to be poorly calibrated, primarily in the direction of overconfidence. In recent years, there is a surge of research on model calibration by leveraging implicit or explicit regularization techniques during training, which achieve well calibration performance by avoiding overconfident outputs. In our study, we empirically found that despite the predictions obtained from these regularized models are better calibrated, they suffer from not being as calibratable, namely, it is harder to further calibrate these predictions with post-hoc calibration methods like temperature scaling and histogram binning. We conduct a series of empirical studies showing that overconfidence may not hurt final calibration performance if post-hoc calibration is allowed, rather, the penalty of confident outputs will compress the room of potential improvement in post-hoc calibration phase. Our experimental findings point out a new direction to improve calibration of DNNs by considering main training and post-hoc calibration as a unified framework.

The 31-year-old engineer and self-described indie game developer is suspected of firing shots at the annual event attended by President Donald Trump, high-profile media figures, and US government officials. US President Donald Trump listens as acting attorney general Todd Blanche speaks during a press briefing shortly after a shooting incident at the White House Correspondents' Dinner on April 25, 2026. A 31-year-old engineer and computer scientist was identified by media reports and President Donald Trump as the suspected shooter at the White House Correspondents Dinner on Saturday night. Cole Tomas Allen, of Torrance, California, was apprehended following the firing of shots at the Washington Hilton, where Trump was scheduled to deliver remarks to a ballroom full of journalists, cabinet officials, and Hilton staff. Allen's name surfaced in media reports shortly before Trump posted two photos of a suspect following his apprehension.