Asurion Japan Holdings • Previous: Data Scientist at Abeja Inc. • MSc degree from Jagiellonian University, Poland • Contributor to several ML libraries
is the worst thing we can do With DL models we generally only have point estimates of parameters and predictions Hard to make decisions when we‘re not able to tell whether a DL model is certain about its output or not Trust and adoption of DL is still low
true ) P(θ true ) P(D) • P(θ true | D): The posterior • the probability of the model parameters given the data: this is the result we want to compute. • P(D | θ true ): The likelihood • proportional to the likelihood estimation in the frequentist approach. • P(θ true ): The model prior • encodes what we knew about the model prior to the application of the data D. • P(D): The data probability • which in practice amounts to simply a normalization term.
purpose framework • Generative models • Clarity of FS + Power of ML – White-box modelling – Black-box fitting (NUTS, ADVI) – Uncertainity → Intuitive insights • Learning from very small datasets • Probabilistic Programming
is very easy and intuitive • Natural hierarchical structure of observational data • Variation among individual groups • Knowledge transfer between groups
• NN models with small datasets • Complex hierarchical neural networks (Bayesian CNN) • Minibatches • Knowledge transfer Business perspective • Clear and intuitive models • Uncertainity in Finance & Insurance is extremely important • Better trust and adoption of Neural Network-based models