Identifying latent variables and the causal structure involving them is essential across various scientific fields. While many existing works fall under the category of constraint-based methods (with e.g. conditional independence or rank deficiency tests), they may face empirical challenges such as testing-order dependency, error propagation, and choosing an appropriate significance level. These issues can potentially be mitigated by properly designed score-based methods, such as Greedy Equivalence Search (GES) (Chickering, 2002) in the specific setting without latent variables. Yet, formulating score-based methods with latent variables is highly challenging. In this work, we develop score-based methods that are capable of identifying causal structures containing causally-related latent variables with identifiability guarantees. Specifically, we show that a properly formulated scoring function can achieve score equivalence and consistency for structure learning of latent variable causal models. We further provide a characterization of the degrees of freedom for the marginal over the observed variables under multiple structural assumptions considered in the literature, and accordingly develop both exact and continuous score-based methods. This offers a unified view of several existing constraint-based methods with different structural assumptions. Experimental results validate the effectiveness of the proposed methods.

Enes Kanter Freedom says Taiwan is the center of the global technology race and remains a key unresolved issue after the Trump-Xi summit produced no new discussions on chips or AI.

Government departments and other public bodies in the UK must consider requests to release information about AI-produced content, regulators have confirmed. The move follows a successful request by New Scientist for the release of a minister's ChatGPT logs The use of AI chatbots is subject to the UK's Freedom of Information laws Text, images and other content produced by UK government departments and other public bodies using artificial intelligence are subject to freedom of information (FOI) laws, regulators have confirmed - potentially opening the door for the public to gain access to ministers' ChatGPT or other chatbot records. The Information Commissioner's Office (ICO), the UK's data-protection agency, has released new guidance confirming that "If staff at a public authority use AI for work purposes, the information generated will be subject to FOIA [the Freedom of Information Act] along with the prompts used". Last year, successfully requested the then-UK tech secretary Peter Kyle's ChatGPT logs under FOI legislation, in what is believed to be a world first. That triggered subsequent requests from other news outlets to obtain other information, but many have either been rejected on cost grounds or labelled as "vexatious", an umbrella term that allows authorities to reject a request.

Probabilistic circuits (PCs) are a family of generative models which allows for the computation of exact likelihoods and marginals of its probability distributions. PCs are both expressive and tractable, and serve as popular choices for discrete density estimation tasks. However, large PCs are susceptible to overfitting, and only a few regularization strategies (e.g., dropout, weight-decay) have been explored. We propose HyperSPNs: a new paradigm of generating the mixture weights of large PCs using a small-scale neural network. Our framework can be viewed as a soft weight-sharing strategy, which combines the greater expressiveness of large models with the better generalization and memory-footprint properties of small models. We show the merits of our regularization strategy on two state-of-theart PC families introduced in recent literature - RAT-SPNs and EiNETs - and demonstrate generalization improvements in both models on a suite of density estimation benchmarks in both discrete and continuous domains.

Non-Gaussian statistics are a challenge for data assimilation. Linear methods oversimplify the problem, yet fully nonlinear methods are often too expensive to use in practice. The best solution usually lies between these extremes. Triangular measure transport offers a flexible framework for nonlinear data assimilation. Its success, however, depends on how the map is parametrized. Too much flexibility leads to overfitting; too little misses important structure. To address this balance, we develop an adaptation algorithm that selects a parsimonious parametrization automatically. Our method uses P-spline basis functions and an information criterion as a continuous measure of model complexity. This formulation enables gradient descent and allows efficient, fine-scale adaptation in high-dimensional settings. The resulting algorithm requires no hyperparameter tuning. It adjusts the transport map to the appropriate level of complexity based on the system statistics and ensemble size. We demonstrate its performance in nonlinear, non-Gaussian problems, including a high-dimensional distributed groundwater model.

You can now buy a humanoid robot housekeeper for less than the price of a second-hand car. But before splashing out, there's something you need to know Science fiction is strewn with humanoid robots, from bad-tempered Bender in to cunning Ava in . And it has long seemed like that's the natural home for such robots - on the screen and in books. The idea of a walking, talking, functioning robot with two arms and two legs has appeared to be a distant dream. Last year, machines ran, boxed and even played football at China's World Humanoid Robot Games, albeit sometimes falling over in the process . Meanwhile, companies have been readying their own range of humanoids that promise to do something a bit more useful: help around the house .

Conditional sampling is an essential primitive in the machine learning toolkit.

Conditional sampling is an essential primitive in the machine learning toolkit.

Subsampling involves creating smaller datasets by randomly selecting a subset of the original data points.