Life expectancy is a fundamental indicator of population health and socio-economic well-being, yet accurately forecasting it remains challenging due to the interplay of demographic, environmental, and healthcare factors. Thi s study evaluates three machine learning models--Linear Regression (LR), Regression Decision Tree (RDT), and Random Forest (RF), using a real -world da-taset drawn from World Health Organization (WHO) and United N ations (UN) sources. After extensive preprocessing to address missing v alues and inconsistencies, each model's performance was assessed with R, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). Results show tha t RF achieves the highest predictive accuracy (R = 0.9423), significantly outperforming LR and RDT. Interpretability was prioritized through p -values for LR and feature - importance metrics for the tree -based models, revealing immunization rates (diphtheria, measles) and demographic attributes (HIV/AIDS, adult mortality) as critical drivers of life-expectancy predictions. These insights underscore the synergy between ensemble methods and transparency in addressing public -health challenges. Future research should explore advanced imputation strategies, alternative algorithms (e.g., neural networks), and updated data to further refine predictive accuracy and support evidence-based policymaking in global health contexts.

Many practical modeling problems involve discrete data that are best represented as draws from multinomial or categorical distributions. For example, nucleotides in a DNA sequence, children's names in a given state and year, and text documents are all commonly modeled with multinomial distributions. In all of these cases, we expect some form of dependency between the draws: the nucleotide at one position in the DNA strand may depend on the preceding nucleotides, children's names are highly correlated from year to year, and topics in text may be correlated and dynamic. These dependencies are not naturally captured by the typical Dirichlet-multinomial formulation. Here, we leverage a logistic stick-breaking representation and recent innovations in P olya-gamma augmentation to reformulate the multinomial distribution in terms of latent variables with jointly Gaussian likelihoods, enabling us to take advantage of a host of Bayesian inference techniques for Gaussian models with minimal overhead.

This work was conducted during Chaobing Song's visit to Professor Yi Ma's group at UC Berkeley.

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

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

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

We study the matrix completion problem with side informatio n. Side information has been considered in several matrix completion applicati ons, and has been empirically shown to be useful in many cases. Recently, resear chers studied the effect of side information for matrix completion from a theoretica lv i e w p o i n t,s h o w i n g that sample complexity can be significantly reduced given co mpletely clean features. However, since in reality most given features are noi sy or only weakly informative, the development of a model to handle a general feature set, and investigation of how much noisy features can help matrix recovery, r emains an important issue. In this paper, we propose a novel model that balances b etween features and observations simultaneously in order to leverage feature i nformation yet be robust to feature noise. Moreover, we study the effect of general fe atures in theory and show that by using our model, the sample complexity can be low er than matrix completion as long as features are sufficiently informative .T h i s r e s u l t p r o v i d e s at h e o r e t i c a li n s i g h ti n t ot h eu s e f u l n e s so fg e n e r a ls i d ei n formation. Finally, we consider synthetic data and two applications -- relationshi pp r e d i c t i o na n ds e m i - supervised clustering -- and show that our model outperforms other methods for matrix completion that use features both in theory and pract ice.