Adaptive testing and sequential estimation problems have recently gained substantial attention due to their foundational role in modern artificial intelligence and interactive systems. Prominent applications include online preference learning, where systems dynamically adapt to user feedback to refine personalized recommendations, and reinforcement learning from human feedback (RLHF), which aims to align AI agents with human values by adaptively querying users. In these contexts, the main focus is to efficiently extract maximal information from human responses, which are inherently stochastic and limited in quantity. Among various types of such problems, this work particularly considers a fundamental yet illustrative case involving stochastic binary responses. Here, a decision-maker sequentially selects questions of varying difficulty from a continuous pool to pose to a candidate and aims to efficiently estimate the candidate's ability (represented by an unknown continuous parameter) by utilizing the binary feedback (e.g., correct/incorrect) collected, which depends probabilistically on the candidate's ability and the question's difficulty. This setup is arguably the simplest scenario that captures the essence of continuous parameter estimation under uncertainty, making it an ideal benchmark for developing fundamental theoretical insights and practical algorithms. Variants of this fundamental adaptive estimation problem have been studied in several communities.

We consider the model-free feature screening in large-scale ultrahigh-dimensional data analysis. Existing feature screening methods often face substantial computational challenges when dealing with large sample sizes. To alleviate the computational burden, we propose a rank-based model-free sure independence screening method (CR-SIS) and its efficient variant, BanditCR-SIS. The CR-SIS method, based on Chatterjee's rank correlation, is as straightforward to implement as the sure independence screening (SIS) method based on Pearson correlation introduced by Fan and Lv(2008), but it is significantly more powerful in detecting nonlinear relationships between variables. Motivated by the multi-armed bandit (MAB) problem, we reformulate the feature screening procedure to significantly reduce the computational complexity of CR-SIS. For a predictor matrix of size n \times p, the computational cost of CR-SIS is O(nlog(n)p), while BanditCR-SIS reduces this to O(\sqrt(n)log(n)p + nlog(n)). Theoretically, we establish the sure screening property for both CR-SIS and BanditCR-SIS under mild regularity conditions. Furthermore, we demonstrate the effectiveness of our methods through extensive experimental studies on both synthetic and real-world datasets. The results highlight their superior performance compared to classical screening methods, requiring significantly less computational time.

Predictions from statistical physics postulate that recovery of the communities in Stochastic Block Model (SBM) is possible in polynomial time above, and only above, the Kesten-Stigum (KS) threshold. This conjecture has given rise to a rich literature, proving that non-trivial community recovery is indeed possible in SBM above the KS threshold, as long as the number $K$ of communities remains smaller than $\sqrt{n}$, where $n$ is the number of nodes in the observed graph. Failure of low-degree polynomials below the KS threshold was also proven when $K=o(\sqrt{n})$. When $K\geq \sqrt{n}$, Chin et al.(2025) recently prove that, in a sparse regime, community recovery in polynomial time is possible below the KS threshold by counting non-backtracking paths. This breakthrough result lead them to postulate a new threshold for the many communities regime $K\geq \sqrt{n}$. In this work, we provide evidences that confirm their conjecture for $K\geq \sqrt{n}$: 1- We prove that, for any density of the graph, low-degree polynomials fail to recover communities below the threshold postulated by Chin et al.(2025); 2- We prove that community recovery is possible in polynomial time above the postulated threshold, not only in the sparse regime of~Chin et al., but also in some (but not all) moderately sparse regimes by essentially counting clique occurence in the observed graph.

The authors' response was in many respects quite comprehensive so I am inclined to slightly revise my score. As I said, I think the results presented in the paper seem interesting and novel, however I still feel that the motivation for signed DPP's is not sufficiently studied. The example of coffee, tea and mugs is nice, but there is just not enough concrete evidence in the current discussion suggesting that the signed DPP would even do the right thing in this simple case (I'm not saying that it wouldn't, just that it was not scientifically established in any way). The authors first define the generalized DPP and then discuss the challenges that the non-symmetric DPP poses for the task of learning of a kernel matrix from i.i.d samples when using the method of moments from prior work [23]. Then, under various assumptions on the nonsymmetric kernel matrix, a learning algorithm is proposed which runs in polynomial time (the analysis follows the ideas of [23], but addresses the challenges posed by the non-symmetric nature of the kernel).

Example-based explanations are widely used in the effort to improve the interpretability of highly complex distributions. However, prototypes alone are rarely sufficient to represent the gist of the complexity. In order for users to construct better mental models and understand complex data distributions, we also need criticism to explain what are not captured by prototypes. Motivated by the Bayesian model criticism framework, we develop MMD-critic which efficiently learns prototypes and criticism, designed to aid human interpretability. A human subject pilot study shows that the MMD-critic selects prototypes and criticism that are useful to facilitate human understanding and reasoning. We also evaluate the prototypes selected by MMD-critic via a nearest prototype classifier, showing competitive performance compared to baselines.

Commercial 3-D printing--or additive manufacturing (AM)--is a booming industry. But if printers were liberated from the typical setup involving an immobile box and a gantry, and set free to work in roving, collaborative teams, the AM business might be much bigger with many more applications, including as robotic masons at construction sites and repairing crumbling urban and rural civil infrastructure. A multidisciplinary robotics team at the NYU Tandon School of Engineering, hosted by NYU's Center for Urban Science and Progress (CUSP) and supported by a $1.2 million grant from the National Science Foundation (NSF), is working to make the concept a reality by designing autonomous systems for 3-D printers on robotic arms attached to mobile, roving platforms. Functioning in teams--a concept called collective additive manufacturing (CAM)--these printers, with machine learning and other artificial intelligence (AI) capabilities, could repair bridges, tunnels and other civic structures; work in ocean depths and disaster zones; or even head to space to work on the Moon, Mars, and beyond. Feng explained that the goal is for accuracy, efficiency, and adaptability to the environment and to real-time conditions--rather the way a navigation app reroutes a vehicle that it senses has veered from a mapped course.

Commentators on AI converge on two goals they believe define the field: (1) to better understand the mind by specifying computational models and (2) to construct computer systems that perform actions traditionally regarded as mental. We should recognize that AI has a third, hidden, more basic aim; that the first two goals are special cases of the third; and that the actual technical substance of AI concerns only this more basic aim. This third aim is to establish new computation-based representational media, media in which human intellect can come to express itself with different clarity and force. This article articulates this proposal by showing how the intellectual activity we label AI can be likened in revealing ways to each of five familiar technologies. AI is not about building artificial intelligences, nor is it about understanding the human mind or any other kind of mind.

The need for online diagnostics in the electric powergeneration industry is driven by a number of significant factors . Due to the low number of new power plants being built by electric utilities, the average age of existing power plant equipment in the United States and its susceptibility to failure is increasing rapidly. Figure 1 shows the percentage of power-generation equipment over 20 years old as a function of year. Note the rapid increase of average age after 1980 and the fact that by the year 2000 fully 50 percent of all generation equipment in the United States will be over 20, the oldest average age of power plant equipment ever experienced by U.S. utilities. Thus, there is a need to know what the actual operating condition of the equipment is at all times, so that outages can be avoided by taking corrective actions at the earliest possible time and by preplanning for outages if they become necessary in order to to minimize their length.

Two topics are treated here First, we present a user model pattcrncd after the stereotype approach (Rich, 1979) This model surpasses Rich's model with respect to its greater flexibility in the construction of user profiles, and its trcat,ment of positive and negative arguments. Second, we present an inference machine This machine treats uncertain knowledge in t,he form of evidence for and against the accuracy of a proposition. Assuming a homogeneous user group, systems developers were able to design a system to perform in accordance with the requirements and capabilities assumed for a partirulal type of user (implicit user modeling). With a heterogeneous user group, this is no longer possible. Since self-assessment,s usually render a distorted picture of the user and are not expected in a real consultative dialogue, they should not be specially required in man-machine communication.