Bayesian Inference

Why this matters now Reinforcement Learning from Human Feedback (RLHF) has become the de facto standard for aligning large language models with human values. Yet, the process remains painfully inefficient—annotators evaluate thousands of pairs, most of which offer little new information. As AI models scale, so does the human cost. The question is no longer can we align models, but can we afford to keep doing it this way? A recent paper from Politecnico di Milano proposes a pragmatic answer: inject Bayesian intelligence into the feedback loop. Their hybrid framework—Bayesian RLHF—blends the scalability of neural reinforcement learning with the data thriftiness of Bayesian optimization. The result: smarter questions, faster convergence, and fewer wasted clicks. ...

Opening — Why this matters now Hospitals collect oceans of data, but critical care remains an island of uncertainty. In intensive care units (ICUs), patients’ vital signs change minute by minute, sensors fail, nurses skip readings, and yet clinical AI models are expected to predict life-or-death outcomes with eerie precision. The problem isn’t data scarcity — it’s missingness. When 30% of oxygen or pressure readings vanish, most machine learning systems either pretend nothing happened or fill in the blanks with statistical guesswork. That’s not science; that’s wishful thinking. ...

Lévy processes — stochastic processes with jumps — are the bedrock of modern financial modeling. From the Variance Gamma model to the CGMY framework, these models have replaced Brownian motion in capturing the reality of financial returns: asymmetry, fat tails, and sudden discontinuities. But what if we told you these processes don’t just live on probability distributions — they live on manifolds? ...