Author

Vadim Sokolov

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Review Open access Aug 2026

Everything Is Prediction: Modern Machine Learning as Bayesian Inference

We argue that the core methods of modern machine learning—conformal prediction, large language models and in-context learning, and generative/diffusion models—are not rivals to Bayesian inference but implementations of it, almost always of its predictive (de Finetti) form rather than its parameter-centric (prior-to-pos...

Nicholas G. Polson, Vadim Sokolov, R. Soyer · Entropy · 0 citations

#artificial intelligence Preprint Mar 2025

Generative Learner for Distributional Causal Effects

The proposed generative learner reduces out-of-sample mean squared error relative to the generalized random forest, double machine learning, and generative adversarial networks, with gains ranging from 5.4% to 93.5% on average across experimental designs.

Maria Nareklishvili, Nicholas G. Polson, Vadim Sokolov · 1 citation