We focus on generative AI for a type of data that still represent one of the most prevalent form of data: tabular data. Our paper introduces two key contributions: a new powerful class of forest-based models fit for such tasks and a simple training algorithm with strong convergence guarantees in a boosting model that parallels that of the original weak / strong supervised learning setting. This algorithm can be implemented by a few tweaks to the most popular induction scheme for decision tree induction (i.e.

The Burer-Monteiro method is one of the most widely used techniques for solving large-scale semidefinite programs (SDP).

Learning from non-stationary data streams, also called Task-Free Continual Learning (TFCL) remains challenging due to the absence of explicit task information in most applications. Even though recently some algorithms have been proposed for TFCL, these methods lack theoretical guarantees. Moreover, there are no theoretical studies about forgetting during TFCL. This paper develops a new theoretical analysis framework that derives generalization bounds based on the discrepancy distance between the visited samples and the entire information made available for training the model. This analysis provides new insights into the forgetting behaviour in classification tasks. Inspired by this theoretical model, we propose a new approach enabled with the dynamic component expansion mechanism for a mixture model, namely Online Discrepancy Distance Learning (ODDL). ODDL estimates the discrepancy between the current memory and the already accumulated knowledge as an expansion signal aiming to ensure a compact network architecture with optimal performance. We then propose a new sample selection approach that selectively stores the samples into the memory buffer through the discrepancybased measure, further improving the performance. We perform several TFCL experiments with the proposed methodology, which demonstrate that the proposed approach achieves the state of the art performance.

Google issued 100 announcements during its Google I/O developers conference this week, none of which involved the smart home. That apparent lack of enthusiasm for a topic close to our heart didn't dissuade TV and smart-appliance manufacturer Hisense from announcing plans to integrate new Google Home APIs into its own ConnectLife app, so that third-party smart home devices can be folded into that ecosystem. Hisense first announced that it would open its ConnectLife app to third-party products in December, 2024. Today, it announced it will incorporate the latest Google Home APIs into the app by the fall of 2025, Hisense says this will enable users to onboard a wide range of third-party smart home devices--including Matter and Works With Google Home-certified products--to create a more integrated smart home experience. Hisense cited two examples of how this would benefit ConnectLife users: "One-touch modes and customized automations can blend Hisense products with third-party devices to create intelligent home responses, such as air conditioners automatically adjusting based on third-party air quality sensors, or smart lights providing visual notifications when the Hisense refrigerator's VersaTemp drawer reaches the ideal temperature for chilling drinks."

Despite the significant interests and many progresses in decentralized multi-player multi-armed bandits (MP-MAB) problems in recent years, the regret gap to the natural centralized lower bound in the heterogeneous MP-MAB setting remains open. In this paper, we propose BEACON - Batched Exploration with Adaptive COmmunicatioN - that closes this gap. BEACON accomplishes this goal with novel contributions in implicit communication and efficient exploration. For the former, we propose a novel adaptive differential communication (ADC) design that significantly improves the implicit communication efficiency. For the latter, a carefully crafted batched exploration scheme is developed to enable incorporation of the combinatorial upper confidence bound (CUCB) principle. We then generalize the existing linear-reward MP-MAB problems, where the system reward is always the sum of individually collected rewards, to a new MP-MAB problem where the system reward is a general (nonlinear) function of individual rewards. We extend BEACON to solve this problem and prove a logarithmic regret. BEACON bridges the algorithm design and regret analysis of combinatorial MAB (CMAB) and MP-MAB, two largely disjointed areas in MAB, and the results in this paper suggest that this previously ignored connection is worth further investigation.

For centuries, the Rub' al-Khali desert near Saudi Arabia and Dubai -- known as the Empty Quarter -- was dismissed as a lifeless sea of sand. In 2002, Sheikh Mohammed bin Rashid Al Maktoum, ruler of Dubai, spotted unusual dune formations and a large black deposit while flying over the desert. That led to the discovery of Saruq Al-Hadid, an archaeological site rich in remnants of copper and iron smelting, which is now believed to be part of a 5,000-year-old civilization buried beneath the sands. Researchers have now found traces of this ancient society approximately 10 feet beneath the desert surface, hidden in plain sight and long overlooked due to the harsh environment and shifting dunes of the Empty Quarter. This discovery brings fresh life to the legend of a mythical city known as'Atlantis of the Sands.'

Chatbots will be able to teach children more than twice as fast as teachers can within the next decade, the so-called godfather of AI has predicted. Geoffrey Hinton, who won a Nobel Prize for his work on the technology, also claimed AI personal tutors would'be much more efficient and less boring'. Speaking at Gitex Europe, the British computer scientist said: 'It's not there yet, but it's coming, and so we'll get much better education at many levels.' AI personal tutors are already being trialled in UK schools, with the technology now able to talk directly to the student and adapt lesson plans to their knowledge level. The government has already funnelled millions of pounds into AI education initiatives – though it has claimed the technology will'absolutely not' replace teachers.

Amazon is aiming to make shopping just a bit easier. This week, Amazon launched a new generative AI feature that produces short audio summaries, detailing everything you need to know about a product. The audio descriptions, which Amazon is calling "hear the highlights", are created from on-page product summaries, reviews, and information from other websites, crafting short snippets that deliver everything you need to know about a product. The product summaries are now available on a limited number of items on Amazon and for US customers only. To access "Hear the highlights", you can do so in the Amazon app.

Dating apps are getting a lot of flak lately. Daters are opting for in-person events -- even dungeon sound baths -- and moving away from increasing AI features and apps that seem to be copying each other. Vice President JD Vance also has no love for dating apps, apparently. In an interview on the New York Times's "Interesting Times" podcast, Vance spoke about his "noneconomic" concerns with AI and tech. He told host and Times opinion columnist Ross Douthat, "If you look at basic dating behavior among young people -- and I think a lot of this is that the dating apps are probably more destructive than we fully appreciate."

Public pretraining is a promising approach to improve differentially private model training. However, recent work has noted that many positive research results studying this paradigm only consider in-distribution tasks, and may not apply to settings where there is distribution shift between the pretraining and finetuning data--a scenario that is likely when finetuning private tasks due to the sensitive nature of the data. In this work, we show empirically across three tasks that even in settings with large distribution shift, where both zero-shot performance from public data and training from scratch with private data give unusably weak results, public features can in fact improve private training accuracy by up to 67% over private training from scratch. We provide a theoretical explanation for this phenomenon, showing that if the public and private data share a low-dimensional representation, public representations can improve the sample complexity of private training even if it is impossible to learn the private task from the public data alone. Altogether, our results provide evidence that public data can indeed make private training practical in realistic settings of extreme distribution shift.