COPENHAGEN PROGRAMMING LANGUAGE SEMINAR (COPLAS) TALK:
Probabilistic Programming -- Practical Experiences and Challenges in Voucher Processing
University of Copenhagen and Skanned.com
Probabilistic programming is a paradigm that augments traditional programming with constructs from Bayesian probabilistic modelling including random variables, distributions, sampling and conditioning. Compared to traditional machine learning techniques, it has three advantages: domain knowledge can be encoded directly, models are more easily interpretable, and it is possible to quantify the uncertainty in predictions.
In my talk, I will provide a short introduction to probabilistic programming and discuss how we use the technique at Skanned.com. I will present two concrete models which we have been working on: one for grouping similar vouchers and another for matching keywords against features. We will discuss the high-level constructs used in these models, the challenges we experienced in describing and running inference for them, and finally the preliminary results of the inference.
Ahmad Al-Sibahi obtained his Ph.D. in computer science from the IT University of Copenhagen. He is presently employed as a researcher at Skanned.com and postdoc at DIKU, the Department of Computer Science, University of Copenhagen, working on probabilistic programming.
All are welcome. No registration required.