This study uses controlled simulations with known ground-truth parameters to evaluate how Distributional Latent Variable Models (DLVM) and Bayesian Distributional Active LEarning (DALE) perform in comparison to conventional Independent Maximum Likelihood Estimation (IMLE). DLVM integrates observations across multiple executive function tasks and individuals, allowing parameter estimation even under sparse or incomplete data conditions. DLVM consistently outperformed IMLE, especially under with smaller amounts of data, and converges faster to highly accurate estimates of the true distributions. In a second set of analyses, DALE adaptively guided sampling to maximize information gain, outperforming random sampling and fixed test batteries, particularly within the first 80 trials. These findings establish the advantages of combining DLVM's cross-task inference with DALE's optimal adaptive sampling, providing a principled basis for more efficient cognitive assessments.

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

Triangle counting is a fundamental and widely studied problem on static graphs, and recently on temporal graphs, where edges carry information on the timings of the associated events. Streaming processing and resource efficiency are crucial requirements for counting triangles in modern massive temporal graphs, with millions of nodes and up to billions of temporal edges. However, current exact and approximate algorithms are unable to handle large-scale temporal graphs. To fill such a gap, we introduce STEP, a scalable and efficient algorithm to approximate temporal triangle counts from a stream of temporal edges. STEP combines predictions to the number of triangles a temporal edge is involved in, with a simple sampling strategy, leading to scalability, efficiency, and accurate approximation of all eight temporal triangle types simultaneously. We analytically prove that, by using a sublinear amount of memory, STEP obtains unbiased and very accurate estimates. In fact, even noisy predictions can significantly reduce the variance of STEP's estimates. Our extensive experiments on massive temporal graphs with up to billions of edges demonstrate that STEP outputs high-quality estimates and is more efficient than state-of-the-art methods.

This paper concerns the problem of estimating a vector \beta, from /-1 measurements y_i which depend statistically on linear functions x_i, \beta, where the x_i are Gaussian random vectors. This model is general enough to capture compressed sensing and phase retrieval problems with binary measurements. The paper assumes that f is completely unknown ahead of time, but that it satisfies certain moment conditions. The paper shows how, under these moment conditions, to reduce the problem of estimating \beta to a sparse PCA problem, with covariance matrix generated by pairs of observations. The idea is to look at differences of x_i - x_{i'} and y_i - y_{i'}; the paper proves that the population covariance matrix of \delta_y \delta_x is a spiked identity matrix, where the spike is of the form \beta* \beta* T. This clever reduction appears to be the main contribution of the work.

Agent-based simulation with a synthetic population can help us compare different treatment conditions while keeping everything else constant within the same population (i.e., as digital twins). Such population-scale simulations require large computational power (i.e., CPU resources) to get accurate estimates for treatment effects. We can use meta models of the simulation results to circumvent the need to simulate every treatment condition. Selecting the best estimating model at a given sample size (number of simulation runs) is a crucial problem. Depending on the sample size, the ability of the method to estimate accurately can change significantly. In this paper, we discuss different methods to explore what model works best at a specific sample size. In addition to the empirical results, we provide a mathematical analysis of the MSE equation and how its components decide which model to select and why a specific method behaves that way in a range of sample sizes. The analysis showed why the direction estimation method is better than model-based methods in larger sample sizes and how the between-group variation and the within-group variation affect the MSE equation.

Many powerful Monte Carlo techniques for estimating partition functions, such as annealed importance sampling (AIS), are based on sampling from a sequence of intermediate distributions which interpolate between a tractable initial distribution and the intractable target distribution. The near-universal practice is to use geometric averages of the initial and target distributions, but alternative paths can perform substantially better. We present a novel sequence of intermediate distributions for exponential families defined by averaging the moments of the initial and target distributions. We analyze the asymptotic performance of both the geometric and moment averages paths and derive an asymptotically optimal piecewise linear schedule. AIS with moment averaging performs well empirically at estimating partition functions of restricted Boltzmann machines (RBMs), which form the building blocks of many deep learning models.

We propose a robust and efficient approach to the problem of compressive phase retrieval in which the goal is to reconstruct a sparse vector from the magnitude of a number of its linear measurements. The proposed framework relies on constrained sensing vectors and a two-stage reconstruction method that consists of two standard convex programs that are solved sequentially. In recent years, various methods are proposed for compressive phase retrieval, but they have suboptimal sample complexity or lack robustness guarantees. The main obstacle has been that there is no straightforward convex relaxations for the type of structure in the target. Given a set of underdetermined measurements, there is a standard framework for recovering a sparse matrix, and a standard framework for recovering a low-rank matrix. However, a general, efficient method for recovering a jointly sparse and low-rank matrix has remained elusive. Deviating from the models with generic measurements, in this paper we show that if the sensing vectors are chosen at random from an incoherent subspace, then the low-rank and sparse structures of the target signal can be effectively decoupled.

Miniaturization has revolutionized computing ever since the advent of the first digital computer. The machines that once filled entire rooms and required teams of engineers to operate are now vastly dwarfed in capability by the smartphones that we carry in our pockets. Now that trend towards miniaturization is transforming the field of machine learning as well. Image and voice recognition, object detection, predictive maintenance, and many other applications that once required complex algorithms that run on huge computing resources in the cloud can now run on a tiny microcontroller in a low-power IoT device. In part, these feats have been achieved with the help of advances in computing power that were previously mentioned, but that alone is not enough.

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AI for health care is all the rage. Who wouldn't be excited about applications that could help detect cancer, diagnose COVID-19 or even dementia well before they are otherwise noticeable, or predict diabetes before its onset? Machine and deep learning have already been shown to make these outcomes possible. Possible, that is, in the research lab. In health care, there is often a long lag between research findings and implementation at the bedside.