UAI-2018 in Monterey

In early August 2018, Aghiles attended the international conference on Uncertainty in Artificial Intelligence (UAI), which took place in Monterey, California. Hereafter, he shares his UAI experience.

The conference was hosted in the InterContinental Hotel, and according to the organizers, this was the biggest UAI ever. The papers presented in the conference covered various current areas in machine learning and AI. Among the topics most represented were: Representation Learning (where our contribution falls into), Causal Inference, Variational Inference, Gaussian Process, Online and Reinforcement Learning.

Each accepted paper was presented as either an oral and/or poster. For the oral presentations, there was only one session at time, which was quite convenient as one could attend any talk of interest. I particularly enjoyed the daily poster sessions; they were highly attended and allowed for deep and enriching discussions/exchanges.

Our work accepted to UAI is entitled “Probabilistic Collaborative Representation Learning for Personalized Item Recommendation” and describes a new Bayesian model for jointly modeling user preferences and deep item  features learning from auxiliary information (such as items’ textual descriptions, images, contexts, etc.).

The conference would not have been a full experience without its banquet dinner at the beautiful Monterey Marina Bay Aquarium. It was another opportunity to meet and interact with people in a broader sense. I particularly retain two things from this evening. The first one, of course, is the excellent food :). The second one, is a discussion with a group of researchers working on causal inference, which allowed me to realized the importance to look into this huge underexplored field in Machine Learning.

UAI’18 was also an opportunity to discover Monterey. Aside from a long and rich history, the things that I enjoyed most about Monterey were the cool-summer weather, fresh & delicious seafood, and all these defunct sardine-canning factories turned into bars, restaurants or shops.