Doctor's awful mistake led to five days of agony, amputation and eventual death for promising young high school graduate, 18, $100m lawsuit alleges I was so fat I needed two plane seats. Then I lost 208lbs and kept it off for 10 YEARS using'nature's Ozempic' supplement. It was so effortlessly effective... and I could even still eat chocolate! I've discovered the perfect'type' of man that'll drive any woman crazy. The sex is so good, it's ruined every other guy for me: JANA HOCKING Leaked CIA Iran war dossier shreds Trump's boasts... as chilling intel reveals vast missile arsenal Young family were beaming picture of happiness... then affair scandal erupted and three of them were found dead Apple to pay iPhone owners $250 million settlement over claims of false advertising... see if you qualify Why this photo of Princess Charlotte has left Harry'very sad': Friends tell RICHARD EDEN all about his plan for Archie and Lili... and why Meghan has become a'challenge' Panic over SIX Americans who returned to US from deadly rat virus ship... as health officials scramble to find infected all over the world Trump's bombshell private admission sends grim warning to Netanyahu as Israel braces for reckoning Deeply personal reason Aaron Rodgers may have to suddenly retire from NFL... and forgo $15 million for mystery wife Blake Lively and Justin Baldoni's battle continues as she demands he pay legal fees for his failed defamation lawsuit days after their shock settlement Billionaire, 70, settles bitter yearslong divorce with ex-wife after shacking up with new fiancée who's almost half his age I survived hantavirus that's spreading on the cruise ship.

The goal of this thesis is to investigate the structural properties of certain sequential problems in order to bring the solutions closer to a practical use. In the first part, we put a special emphasis on structures that can be represented as graphs on actions. In the second part, we study the large action spaces that can be of exponential size in the number of base actions or even infinite. For graph bandits, we consider the settings of smoothness of rewards (spectral bandits), side observations, and influence maximization. For large structured domains, we cover kernel bandits, polymatroid bandits, bandits for function optimization (including unknown smoothness), and infinitely many-arms bandits. The thesis aspires to be a survey of the author's contributions on graph and structured bandits.

When you purchase through links in our articles, we may earn a small commission. Microsoft 365 is down to $69.99 for a full year, giving you premium Office apps, 1TB storage, and built-in AI tools across your devices. If you're already using Word, Excel, or PowerPoint --even occasionally--this is one of those upgrades that just makes your setup smoother across the board. Microsoft 365 is currently $69.99 for a 1-year subscription (MSRP $99.99), and it bundles together the apps you actually use with features that go beyond basic document editing. That means your files are accessible across devices, backed up, and easy to share without emailing attachments back and forth. One of the bigger upgrades here is the built-in AI assistant, Copilot .

Neural Information Processing Systems http://nips.cc/

Social recommendation has shown promising improvements over traditional systems since it leverages social correlation data as an additional input. Most existing works assume that all data are available to the recommendation platform. However, in practice, user-item interaction data (e.g., rating) and user-user social data are usually generated by different platforms, both of which contain sensitive information. Therefore, How to perform secure and efficient social recommendation across different platforms, where the data are highly-sparse in nature remains an important challenge. In this work, we bring secure computation techniques into social recommendation, and propose S3Rec, a sparsity-aware secure cross-platform social recommendation framework. As a result, S3Rec can not only improve the recommendation performance of the rating platform by incorporating the sparse social data on the social platform, but also protect data privacy of both platforms. Moreover, to further improve model training efficiency, we propose two secure sparse matrix multiplication protocols based on homomorphic encryption and private information retrieval. Our experiments on two benchmark datasets demonstrate that S3Rec improves the computation time and communication size of the state-of-the-art model by about 40 and 423 in average, respectively.

This paper presents a novel value-aware approach to product recommendation that simultaneously addresses the high dimensionality and sparsity of user-item data while explicitly incorporating the contribution of each product and user to overall sales revenue. The proposed framework encodes revenue contributions in the user-item matrix and computes customer similarity directly on this basis using suitable distance measures. This enables the segmentation of users according to the revenue-based similarity of their purchase baskets and supports recommendations aligned with profitability objectives. We compare conventional similarity metrics with a novel alternative tailored to high-dimensional contexts and propose three recommendation strategies based on revenue share, product popularity, and expected profit generation. The effectiveness of the proposed method is validated through simulation experiments and a real-world application using the UCI Online Retail dataset.

Delayed feedback is a critical problem in dynamic recommender systems. In practice, the feedback result often depends on the frequency of recommendation. Most existing online learning literature fails to consider optimization of the recommendation frequency, and regards the reward from each successfully recommended message as equal. In this paper, we consider a novel cascading bandits setting, where individual messages from a selected list are sent to a user periodically. Whenever a user does not like a message, she may abandon the system with a probability positively correlated with the recommendation frequency.

Rapid model validation via the train-test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity. Yet, without rigorous model validation we cannot ensure the intended outcomes of deployed AI systems, including positive social impact, nor continue to advance AI research in a scientifically sound way. In this paper, I will show that for widely considered inference settings in complex social systems the train-test paradigm does not only lack a justification but is indeed invalid for any risk estimator, including counterfactual and causal estimators, with high probability. These formal impossibility results highlight a fundamental epistemic issue, i.e., that for key tasks in modern AI we cannot know whether models are valid under current data collection practices. Importantly, this includes variants of both recommender systems and reasoning via large language models, and neither naïve scaling nor limited benchmarks are suited to address this issue. I am illustrating these results via the widely used MOVIELENS benchmark and conclude by discussing the implications of these results for AI in social systems, including possible remedies such as participatory data curation and open science.