maximum-likelihood-estimation-5595150b·2 events·first seen Aliases: Maximum Likelihood Estimation, Implicit Maximum Likelihood Estimation
A new arXiv preprint introduces ROMS-IMLE, a minimalist single-step generative model that challenges the prevailing belief that iterative denoising (as in diffusion models) is necessary for high-quality image generation. Using Implicit Maximum Likelihood Estimation (IMLE) as the training objective and a convolutional network rather than a transformer, the model achieves FID 2.56 on ImageNet 256 with competitive precision and recall. The result is parameter-efficient and fast, suggesting that much of the complexity in modern generative pipelines may be unnecessary.
This paper addresses miscalibration in Gaussian process predictive distributions used for Bayesian optimization, focusing specifically on the lower tail relevant to minimization objectives. The authors introduce a framework for 'goal-oriented' spatial calibration below a threshold t, defining occurrence calibration and thresholded μ-calibration on sublevel sets. They propose tcGP, a post-hoc calibration method, and prove the resulting EI-based optimizer remains dense in the design space. Experiments on standard benchmarks show tcGP improves both lower-tail calibration and overall BO performance compared to standard and globally calibrated GP models.