Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models
Two new arXiv papers explore advanced inference techniques in machine learning. One paper benchmarks likelihood-free inference methods, evaluating their performance with heavy-tailed and discrete data. The other paper bridges maximum likelihood and optimal transport for efficient inference and model selection in stochastic block models, proposing a regularized formulation for simultaneous parameter recovery and cluster number selection. AI
IMPACT These papers introduce novel statistical methods that could enhance the accuracy and efficiency of machine learning models in complex data scenarios.