We present novel bounds for estimating discrete probability distributions under the $\ell_\infty$ norm. These are nearly optimal in various precise senses, including a kind of instance-optimality. Our data-dependent convergence guarantees for the maximum likelihood estimator significantly improve upon the currently known results. A variety of techniques are utilized and innovated upon, including Chernoff-type inequalities and empirical Bernstein bounds. We illustrate our results in synthetic and real-world experiments. Finally, we apply our proposed framework to a basic selective inference problem, where we estimate the most frequent probabilities in a sample.

Digital twin (DT) platforms are increasingly regarded as a promising technology for controlling, optimizing, and monitoring complex engineering systems such as next-generation wireless networks. An important challenge in adopting DT solutions is their reliance on data collected offline, lacking direct access to the physical environment. This limitation is particularly severe in multi-agent systems, for which conventional multi-agent reinforcement (MARL) requires online interactions with the environment. A direct application of online MARL schemes to an offline setting would generally fail due to the epistemic uncertainty entailed by the limited availability of data. In this work, we propose an offline MARL scheme for DT-based wireless networks that integrates distributional RL and conservative Q-learning to address the environment's inherent aleatoric uncertainty and the epistemic uncertainty arising from limited data. To further exploit the offline data, we adapt the proposed scheme to the centralized training decentralized execution framework, allowing joint training of the agents' policies. The proposed MARL scheme, referred to as multi-agent conservative quantile regression (MA-CQR) addresses general risk-sensitive design criteria and is applied to the trajectory planning problem in drone networks, showcasing its advantages.

An algorithm effects a causal representation of relations between features and labels in the human's perception. Such a representation might conflict with the human's prior belief. Explanations can direct the human's attention to the conflicting feature and away from other relevant features. This leads to causal overattribution and may adversely affect the human's information processing. In a field experiment we implemented an XGBoost-trained model as a decision-making aid for counselors at a public employment service to predict candidates' risk of long-term unemployment. The treatment group of counselors was also provided with SHAP. The results show that the quality of the human's decision-making is worse when a feature on which the human holds a conflicting prior belief is displayed as part of the explanation.

Machine Learning (ML) and 'Artificial Intelligence' ('AI') methods tend to replicate and amplify existing biases and prejudices, as do Robots with AI. For example, robots with facial recognition have failed to identify Black Women as human, while others have categorized people, such as Black Men, as criminals based on appearance alone. A 'culture of modularity' means harms are perceived as 'out of scope', or someone else's responsibility, throughout employment positions in the 'AI supply chain'. Incidents are routine enough (incidentdatabase.ai lists over 2000 examples) to indicate that few organizations are capable of completely respecting peoples' rights; meeting claimed equity, diversity, and inclusion (EDI or DEI) goals; or recognizing and then addressing such failures in their organizations and artifacts. We propose a framework for adapting widely practiced Research and Development (R&D) project management methodologies to build organizational equity capabilities and better integrate known evidence-based best practices. We describe how project teams can organize and operationalize the most promising practices, skill sets, organizational cultures, and methods to detect and address rights-based fairness, equity, accountability, and ethical problems as early as possible when they are often less harmful and easier to mitigate; then monitor for unforeseen incidents to adaptively and constructively address them. Our primary example adapts an Agile development process based on Scrum, one of the most widely adopted approaches to organizing R&D teams. We also discuss limitations of our proposed framework and future research directions.

Large-scale black-box models have become ubiquitous across numerous applications. Understanding the influence of individual training data sources on predictions made by these models is crucial for improving their trustworthiness. Current influence estimation techniques involve computing gradients for every training point or repeated training on different subsets. These approaches face obvious computational challenges when scaled up to large datasets and models. In this paper, we introduce and explore the Mirrored Influence Hypothesis, highlighting a reciprocal nature of influence between training and test data. Specifically, it suggests that evaluating the influence of training data on test predictions can be reformulated as an equivalent, yet inverse problem: assessing how the predictions for training samples would be altered if the model were trained on specific test samples. Through both empirical and theoretical validations, we demonstrate the wide applicability of our hypothesis. Inspired by this, we introduce a new method for estimating the influence of training data, which requires calculating gradients for specific test samples, paired with a forward pass for each training point. This approach can capitalize on the common asymmetry in scenarios where the number of test samples under concurrent examination is much smaller than the scale of the training dataset, thus gaining a significant improvement in efficiency compared to existing approaches. We demonstrate the applicability of our method across a range of scenarios, including data attribution in diffusion models, data leakage detection, analysis of memorization, mislabeled data detection, and tracing behavior in language models. Our code will be made available at https://github.com/ruoxi-jia-group/Forward-INF.

White House press secretary Karine Jean-Pierre on Monday said that President Biden will not be taking a cognitive test during his regular physical exam. President Biden will not take a cognitive test as part of his upcoming physical exam, the White House confirmed Monday. White House press secretary Karine Jean-Pierre stated that Biden's physician, Dr. Kevin O'Connor, does not believe a cognitive test is necessary. She said O'Connor believes Biden proves his cognitive ability "every day [in] how he operates and how he thinks." Reporters pressed Jean-Pierre on the issue due to last week's report from Special Counsel Robert Hur that found Biden has significant memory issues.

In 1976, Frank Collin, an ambitious leader in the small but resilient Nazi party of the United States, planned a march in Skokie, Illinois--an attempt to raise the profile of his organization and build support for his cause. The town, many of whose residents were Jewish and had lived through the war, vehemently opposed the demonstration, and the case went to the courts. The American Civil Liberties Union came to the legal defense of Collin and his fellow Nazis on First Amendment grounds--a move that would be almost unthinkable today. Aryeh Neier, the national executive director of the ACLU at the time, received thousands of letters condemning his organization's decision to defend the free speech rights of Nazis. Neier was born into a Jewish family in Berlin in 1937 and fled from Germany to England along with his parents as a child.

Esmeralda Bernal is the valedictorian of Downtown Magnets High School this year, the daughter of Mexican immigrants who never went to college. She's taken 18 college-level classes and aced them with a cumulative 4.5 GPA while taking on leadership roles in her school's robotics and math clubs. She dreams of becoming a civil engineer. Yet the brainy senior couldn't get past glitches to submit her federal financial aid form for more than a month. A new form designed to be simpler was just the opposite for her: impenetrable.

As Ukraine approaches the second anniversary of Russia's full-scale invasion, it plans to produce more if its own ammunition and key weapons systems. The goal of greater self-sufficiency comes as Ukraine's Western allies meet increasing political resistance to military aid and Russia ramps up weapons production. Last month, Ukraine's prime minister, Denys Shmyhal, said the country plans to increase its domestic weapons production sixfold this year. Ukraine's defence industry has already begun to expand. Strategic industries minister Oleksandr Kamyshin said Ukraine last year doubled its ammunition production for NATO-calibre artillery systems.

When Pakistan's government censored the media, former Prime Minister Imran Khan's party posted campaign videos on TikTok. When the police barred his supporters from holding rallies, they hosted virtual gatherings online. And when Khan ended up behind bars, his supporters produced speeches using artificial intelligence to simulate his voice. Khan's message resonated with millions across the country who were frustrated by the country's economic crisis and old political dynasties: Pakistan has been on a steep decline for decades, he explained, and only he could restore its former greatness.