arXiv:2512.20762v2 Announce Type: replace-cross Abstract: We study the problem of subgroup discovery for survival analysis, where the goal is to find an interpretable subset of the data on which a Cox model is highly accurate. Our work is the first to study this particular subgroup problem, for which we make several contributions. Subgroup discovery methods generally require a “quality function” in order to sift through and select the most advantageous subgroups. We first examine why existing natural choices […]

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Reinforcement learning with verifiable rewards (RLVR) is a simple but powerful paradigm for training LLMs: sample a completion, verify it, and update. In practice, however, the verifier is almost never clean–unit tests probe only limited corner cases; human and synthetic labels are imperfect; and LLM judges (e.g., RLAIF) are noisy and can be exploited–and this problem worsens on harder domains (especially coding) where tests are sparse and increasingly model-generated. We ask a pragmatic question: does the verification noise […]

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Ever since the AI race picked up the pace, we have been witnessing some landmark investments within the industry. SoftBank, a major Japanese multinational conglomerate headquartered in Tokyo, has been among the most popular names involved in such investments. Earlier this month, there were reports hinting that Soft Bank is coming together with NVIDIA to invest at least $15 billion into Skild AI, which is already a well-funded startup that makes foundation models for robots. SoftBank boosts its […]

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When the independent Tunisian online media collective Nawaat announced that the government had suspended its activities for one month, the news landed like a punch in the gut for anyone who remembers what the Arab uprisings promised: dignity, democracy, and a free press. But Tunisia’s October 31 suspension of Nawaat—delivered quietly, without formal notice, and justified under Decree-Law 2011-88—is not just a bureaucratic decision. It’s a warning shot aimed at the very idea of independent civic life. The […]

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Mobile Fortify, the new app used by Immigration and Customs Enforcement (ICE) to use face recognition technology (FRT) to identify people during street encounters, is an affront to the rights and dignity of migrants and U.S. citizens alike. That’s why a coalition of privacy, civil liberties and civil rights organizations are demanding the Department of Homeland Security (DHS) shut down the use of Mobile Fortify, release the agency’s privacy analyses of the app, and clarify the agency’s policy […]

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Many probabilistic models introduce strong dependencies between variables using a latent multivariate Gaussian distribution or a Gaussian process. We present a new Markov chain Monte Carlo algorithm for performing inference in models with multivariate Gaussian priors. Its key properties are: 1) it has simple, generic code applicable to many models, 2) it has no free parameters, 3) it works well for a variety of Gaussian process based models. These properties make our method ideal for use while model […]

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arXiv:2501.02505v4 Announce Type: replace-cross Abstract: Ranking items based on pairwise comparisons is common, from using match outcomes to rank sports teams to using purchase or survey data to rank consumer products. Statistical inference-based methods such as the Bradley-Terry model, which extract rankings based on an underlying generative model, have emerged as flexible and powerful tools to tackle ranking in empirical data. In situations with limited and/or noisy comparisons, it is often challenging to confidently distinguish the performance of […]

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Chelsea Follett The debut of the robot butler NEO has drawn widespread ridicule. Unable to perform many chores without a remote human operator, the machine has become a target of social media backlash. Videos circulating online show the robot struggling with basic tasks, such as closing a dishwasher. , But don’t underestimate the potential of robotic housekeepers just yet. The technology is dawning at an opportune time. Consider the growing concerns about plummeting birth rates. Last year saw […]

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Machine unlearning aims to remove the influence of specific training data from a learned model without full retraining. While recent work has begun to explore unlearning in quantum machine learning, existing approaches largely rely on fixed, uniform target distributions and do not explicitly control the trade-off between forgetting and retained model behaviour. In this work, we propose a distribution-guided framework for class-level quantum machine unlearning that treats unlearning as a constrained optimization problem. Our method introduces a tunable […]

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