Area under ROC (AUC) is a metric which is widely used for measuring the classification performance for imbalanced data. It is of theoretical and practical interest to develop online learning algorithms that maximizes AUC for large-scale data. A specific challenge in developing online AUC maximization algorithm is that the learning objective function is usually defined over a pair of training examples of opposite classes, and existing methods achieves on-line processing with higher space and time complexity. In this work, we propose a new stochastic online algorithm for AUC maximization. In particular, we show that AUC optimization can be equivalently formulated as a convex-concave saddle point problem. From this saddle representation, a stochastic online algorithm (SOLAM) is proposed which has time and space complexity of one datum. We establish theoretical convergence of SOLAM with high probability and demonstrate its effectiveness on standard benchmark datasets.

One-person company SoloNest sounder Karen Dai preparing for a coffee chat at a conference room in Shanghai on April 12. | AFP-JIJI Shanghai - Young Chinese, many who fear age discrimination in their workplace after turning 35, are increasingly starting one-person companies that have artificial intelligence do most of the work. Smaller startups are already in vogue in Silicon Valley and elsewhere, with rapidly advancing AI tools seen as a welcome teammate even as they threaten layoffs at existing firms. More young people in China are subscribing to the model, as cities pledge millions of dollars in funding and rent subsidies for such ventures, in alignment with Beijing's political goal of technological self-reliance. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

A soldier carries a drone during a military parade in Washington on June 14, 2025. The Pentagon's largest-ever budget request earmarks $75 billion for drones and technologies to counter them, mainly for a massive increase for a little-known office working with U.S. commandos to test and evaluate various systems, according to defense officials. The drone-funding proposal includes $54.6 billion for the Defense Autonomous Working Group, or DAWG, from just $225.9 million this year. That would appear to be the largest single year-over-year boost of any defense program or office, meaning it's likely to draw particular congressional and public scrutiny in an already eye-catching $1.5 trillion request that's 42% larger than this year's budget. The big boost for the Pentagon's little-known drone unit comes as the U.S. and Israeli war against Iran illustrates how drones can help level the playing field against even the world's most well-funded armed forces.

Things to Do in L.A. Tap to enable a layout that focuses on the article. A drone delivered her lethal dose of fentanyl in a church parking lot. The Drug Enforcement Administration was among agencies involved in the investigation. This is read by an automated voice. Please report any issues or inconsistencies here .

Meta to capture U.S. employee mouse movements and keystrokes to train AI NEW YORK - Meta is installing new tracking software on U.S.-based employees' computers to capture mouse movements, clicks and keystrokes for use in training its artificial intelligence models, part of a broad initiative to build AI agents that can perform work tasks autonomously, the company told staffers in internal memos. The tool, called Model Capability Initiative (MCI), will run on work-related apps and websites and will also take occasional snapshots of the content on employees' screens, according to one of the memos, posted by a staff AI research scientist on Tuesday in a channel for the company's model-building Meta SuperIntelligence Labs team. The purpose, according to the memo, was to improve the company's AI models in areas where they struggle to replicate how humans interact with computers, like choosing from dropdown menus and using keyboard shortcuts. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

Michael Dell and Susan Dell became the first donors to give more than $1 billion to the University of Texas at Austin, funding a massive AI-native hospital and research campus.

We describe Swapout, a new stochastic training method, that outperforms ResNets of identical network structure yielding impressive results on CIFAR-10 and CIFAR100.

Clustering, in particular k-means clustering, is a central topic in data analysis. Clustering with Bregman divergences is a recently proposed generalization of k-means clustering which has already been widely used in applications. In this paper we analyze theoretical properties of Bregman clustering when the number of the clusters k is large. We establish quantization rates and describe the limiting distribution of the centers as k, extending well-known results for k-means clustering.

In this work, we take a fresh look at some old and new algorithms for off-policy, return-based reinforcement learning. Expressing these in a common form, we derive a novel algorithm, Retrace(λ), with three desired properties: (1) it has low variance; (2) it safely uses samples collected from any behaviour policy, whatever its degree of "off-policyness"; and (3) it is efficient as it makes the best use of samples collected from near on-policy behaviour policies. We analyze the contractive nature of the related operator under both off-policy policy evaluation and control settings and derive online sample-based algorithms. We believe this is the first return-based off-policy control algorithm converging a.s. to Q without the GLIE assumption (Greedy in the Limit with Infinite Exploration). As a corollary, we prove the convergence of Watkins' Q(λ), which was an open problem since 1989. We illustrate the benefits of Retrace(λ) on a standard suite of Atari 2600 games. One fundamental trade-off in reinforcement learning lies in the definition of the update target: should one estimate Monte Carlo returns or bootstrap from an existing Q-function?

Weighted minwise hashing (WMH) is one of the fundamental subroutine, required by many celebrated approximation algorithms, commonly adopted in industrial practice for large -scale search and learning. The resource bottleneck with WMH is the computation of multiple (typically a few hundreds to thousands) independent hashes of the data. We propose a simple rejection type sampling scheme based on a carefully designed red-green map, where we show that the number of rejected sample has exactly the same distribution as weighted minwise sampling. The running time of our method, for many practical datasets, is an order of magnitude smaller than existing methods. Experimental evaluations, on real datasets, show that for computing 500 WMH, our proposal can be 60000x faster than the Ioffe's method without losing any accuracy. Our method is also around 100x faster than approximate heuristics capitalizing on the efficient "densified" one permutation hashing schemes [26, 27]. Given the simplicity of our approach and its significant advantages, we hope that it will replace existing implementations in practice.