Both humans and other animals are good at learning by inference, using information we do have to figure out things we cannot observe directly. New research from the Center for Mind and Brain at the ...

Tomorrow's AI services depend on networks built for massive inference growth.

Inverse problems arise when one seeks to recover unknown parameters or functions from indirect, noisy observations via a forward model. The Bayesian framework casts this recovery as the updating of a ...

“The rapid release cycle in the AI industry has accelerated to the point where barely a day goes past without a new LLM being announced. But the same cannot be said for the underlying data,” notes ...

Earlier this year, SDxCentral explored the market push behind AI inference – the process where a trained machine learning ...

Paper: "Robust Nonparametric Bias-Corrected Inference in the Regression Discontinuity Design", (joint work with Sebastian Calonico and Rocio Titiunik).