The connection is established for all of Savage's DPO framework to generalize its functional parts (Alfano et al., 2025; Azar et al., 2024; Chen et al., The latter involves elements from Doignon-Falmagne's stochastic choice These many design elements lead to a generalization making the most of the connection since we encompass all of properness on Savage's side (regardless of optional properties like symmetry, We also encompass all of the modelling's power on Krantz, Luce, Suppes and Notably, our level of generalization is able to support "for free" important This is an important task because DPO was designed with the objective to simplify RLHF and getting "above" DPO is mandatory to improve results by getting more freedom on reward shapes, trajectories and preference behaviours (Gupta et al., 2025), all of which needs to be done while One perhaps unexpected pitfall comes from the RLHF/DPO inherited "gold To preserve readability, all proofs are given in an appendix. We adopt many definitions from Rafailov et al. (2023).

Structured data-quality issues, such as missing values correlated with demographics, culturally biased labels, or systemic selection biases, routinely degrade the reliability of machine-learning pipelines. Regulators now increasingly demand evidence that high-stakes systems can withstand these realistic, interdependent errors, yet current robustness evaluations typically use random or overly simplistic corruptions, leaving worst-case scenarios unexplored. We introduce SAVAGE, a causally inspired framework that (i) formally models realistic data-quality issues through dependency graphs and flexible corruption templates, and (ii) systematically discovers corruption patterns that maximally degrade a target performance metric. SAVAGE employs a bi-level optimization approach to efficiently identify vulnerable data subpopulations and fine-tune corruption severity, treating the full ML pipeline, including preprocessing and potentially non-differentiable models, as a black box. Extensive experiments across multiple datasets and ML tasks (data cleaning, fairness-aware learning, uncertainty quantification) demonstrate that even a small fraction (around 5 %) of structured corruptions identified by SAVAGE severely impacts model performance, far exceeding random or manually crafted errors, and invalidating core assumptions of existing techniques. Thus, SAVAGE provides a practical tool for rigorous pipeline stress-testing, a benchmark for evaluating robustness methods, and actionable guidance for designing more resilient data workflows.

Tested's Adam Savage paired up with Medtronic to offer his commentary on what can contribute to a longer lifespan, including possible differences between men's and women's health. Former "MythBusters" star Adam Savage is exploring the science of longevity, asking how lifestyle choices, stress and even sleep affect how long we live. Savage, now a YouTube creator and head of the channel Tested, has partnered with health technology company Medtronic to engage in discussions about longevity. While not a researcher himself, he has taken a deep dive into scientific insights from experts and reflected on his own experiences. "Longevity has always been a fascination for me," Savage told Fox News Digital in an exclusive interview.

This paper deals with the dueling bandit problem in situations where the goal is to find other notions of winners than the Condorcet one (which is not guaranteed to exist) or the Copeland one (which does always exist, but has certain flaws). Unlike the MAB problem, the question of what constitutes the best arm(s) is not completely straightforward to answer in the dueling setting, in particular when there is no single arm that is preferred to all other arms (aka the Condorcet winner). Indeed, the field of social choice theory has devised a near infinite supply of such notions of winners: the reason for this overabundance of definitions is that in the absence of a Condorcet winner, it is difficult to find a definition that satisfies all reasonable properties that one might hope for, so various definitions have been proposed that satisfy various sets of desirable properties. Given that, it is natural to seek algorithms that can find these other winners, since depending on the application domain, it might very well be that the Condorcet winner does not exist and that the Copeland winner is undesirable. With this in mind, the authors propose a flexible algorithm that is capable of accommodating such needs, given certain rather weak assumptions on the sought after notion of winner.

Cate Blanchett – beloved thespian, film star and refugee advocate – is standing at a lectern, addressing the European Union parliament. "The future is now," she says, authoritatively. So far, so normal, until: "But where the fuck are the sex robots?" The footage is from a 2023 address that Blanchett actually gave – but the rest has been made up. Her voice was generated by Australian artist Xanthe Dobbie using the text-to-speech platform PlayHT, for Dobbie's 2024 video work Future Sex/Love Sounds – an imagining of a sex robot-induced feminist utopia, voiced by celebrity clones.

Graph Neural Networks (GNNs) have proven to be successful in several predictive modeling tasks for graph-structured data. Amongst those tasks, link prediction is one of the fundamental problems for many real-world applications, such as recommender systems. However, GNNs are not immune to adversarial attacks, i.e., carefully crafted malicious examples that are designed to fool the predictive model. In this work, we focus on a specific, white-box attack to GNN-based link prediction models, where a malicious node aims to appear in the list of recommended nodes for a given target victim. To achieve this goal, the attacker node may also count on the cooperation of other existing peers that it directly controls, namely on the ability to inject a number of ``vicious'' nodes in the network. Specifically, all these malicious nodes can add new edges or remove existing ones, thereby perturbing the original graph. Thus, we propose SAVAGE, a novel framework and a method to mount this type of link prediction attacks. SAVAGE formulates the adversary's goal as an optimization task, striking the balance between the effectiveness of the attack and the sparsity of malicious resources required. Extensive experiments conducted on real-world and synthetic datasets demonstrate that adversarial attacks implemented through SAVAGE indeed achieve high attack success rate yet using a small amount of vicious nodes. Finally, despite those attacks require full knowledge of the target model, we show that they are successfully transferable to other black-box methods for link prediction.

Did you miss a session at the Data Summit? If data is the new oil of the digital economy, artificial intelligence (AI) is the steam engine. Companies that take advantage of the power of data and AI hold the key to innovation -- just as oil and steam engines fueled transportation and, ultimately, the Industrial Revolution. In 2022, data and AI have set the stage for the next chapter of the digital revolution, increasingly powering companies across the globe. How can companies ensure that responsibility and ethics are at the core of these revolutionary technologies?

They hoped, on their return, to have the answer to a question that would change the music industry: can a computer pick a hit record? Pettersson, who is Swedish, was a specialist in artificial intelligence (AI) with a background in neuroscience; Savage, a British music industry professional with tech pedigree, had worked for Shazam and the Pandora streaming service. Savage says Musiio can now run through thousands of songs – submitted as demos or uploaded to streaming services – and sort them, according to whether they contain a vocal, whether they're trap, indie or classical, and even whether they bear resemblances to an existing hit, say Uptown Funk by Mark Ronson. For decades, talent scouts or record company A&R professionals used to find new singers, musicians and MCs by going to concerts, listening to radio, talking to people in record shops, receiving tips from well-connected pros such as gig promoters, and listening to unsolicited demo tapes. Conrad Withey is the CEO of Instrumental, a British company that uses data analysis to identify, track, profile, rank and sign overlooked recording artists across the globe – doing digitally the sort of number-crunching an A&R professional might once have done manually. We also have our own playlists that reach more than 1.5m listeners and those help recordings reach new audiences – and trigger other playlist editors and algorithms." Their software tracks bookings at major venues, mentions on music blogs and inclusions on playlists and charts, as well as support from tastemakers, influencers and playlisters. He says that today, an obscure singer, rapper, music producer or band showing good data points might get multiple offers from labels, and little in the way of guidance. Withey points out that Simon Cowell effectively shuttered his record label Syco – once home to Little Mix and One Direction – last summer and argues that talent-show viewers prefer watching TikTok or Instagram to broadcast TV, and follow the music from there. "If you go on Sony Music's global website," she says, "it says, 'We do not accept unsolicited demos.'

One lunchtime about three years ago, Hazel Savage and Aron Pettersson set a new piece of software running on a laptop then went to a nearby mall for a sandwich. They hoped, on their return, to have the answer to a question that would change the music industry: can a computer pick a hit record? The pair had just founded their firm, Musiio, in Singapore's Boat Quay district. Pettersson, who is Swedish, was a specialist in artificial intelligence (AI) with a background in neuroscience; Savage, a British music industry professional with tech pedigree, had worked for Shazam and the Pandora streaming service. These are written by little-known artists and commonly used for soundtracks and podcasts.

The Regent Honeyeaters of Australasia are forgetting how to talk. The songbird's habitat has been so severely devastated that its numbers are dwindling. Worse, the ones that remain are so scattered that the adult males are too far apart to teach the young how to sing for a mate -- how to speak their own language. The gradual loss of the Honeyeaters' song, their primary tool for wooing a partner, creates a vicious circle of spiraling decline. Humans, on the other hand, cannot shut up.