Agustinus Kristiadi -- Probabilistic Inference and Decision-Making With and For Foundation Models

Bio

Probabilistic inference is a compelling framework for capturing our belief about an unknown given observations. Central in this paradigm are probabilistic models and approximate inference methods. The former models one’s prior belief and encodes the data, while the latter produces posterior distributions based on the former. In the era of large-scale neural networks and foundation models, leveraging them in probabilistic modeling or improving them using probabilistic inference is challenging due to their sheer size. In this talk, I will discuss recent works in (i) developing efficient probabilistic models with and for large foundation models, (ii) leveraging the resulting powerful, calibrated beliefs to improve decision-making and planning, and (iii) applying the resulting probabilistic decision-making/planning systems for improving scientific discovery, and improving the neural networks themselves.

Abstract

apturing our belief about an unknown given observations. Central in this paradigm are probabilistic models and approximate inference methods. The former models one’s prior belief and encodes the data, while the latter produces posterior distributions based on the former. In the era of large-scale neural networks and foundation models, leveraging them in probabilistic modeling or improving them using probabilistic inference is challenging due to their sheer size. In this talk, I will discuss recent works in (i) developing efficient probabilistic models with and for large foundation models, (ii) leveraging the resulting powerful, calibrated beliefs to improve decision-making and planning, and (iii) applying the resulting probabilistic decision-making/planning systems for improving scientific discovery, and improving the neural networks themselves.