Category: Navigate

I will slowly restart blogging a bit about my past year experience that included visiting Prof. Arto Klami group at the University of Helsinki and a research internship at Curious AI, working under the guidance of Mathias Berglund and Harri Valpola. The first part of my stay resulted in an interesting research direction exploring how to use the prior-predictive distribution to obtain direct relationships between moments of the data (if generated by the model being specified) and hyperparameters of the model. I will discuss this further, but for now, I leave the abstract and link to the preprint.

Abstract: Hyperparameter optimization for machine learning models is typically carried out by some sort of cross-validation procedure or global optimization, both of which require running the learning algorithm numerous times. We show that for Bayesian hierarchical models there is an appealing alternative that allows selecting good hyperparameters without learning the model parameters during the process at all, facilitated by the prior predictive distribution that marginalizes out the model parameters. We propose an approach that matches suitable statistics of the prior predictive distribution with ones provided by an expert and apply the general concept for matrix factorization models. For some Poisson matrix factorization models we can analytically obtain exact hyperparameters, including the number of factors, and for more complex models we propose a model-independent optimization procedure.

• Link: https://arxiv.org/abs/1910.12263
• Code: https://github.com/zehsilva/prior-predictive-specification/

After the successful first edition of ProbAI, we are again in full motion to organize this summer school. The main difference introduced in the program is making it more tight-knit in a way that we expect higher convergence between students, topics and speakers. Also, we are working towards having an extra Q&A session in the of the day with a group of teaching assistant that will be prepared to go over the content of the day and help clarify any doubts.

Applications: https://probabilistic.ai/application/

Here the official announcement we release in different open channels online.

Continue reading “Nordic Probabilistic AI School 2020!” →

Open for applications! You are welcome to apply for the Nordic Probabilistic AI School (ProbAI) 2019 finding place on June 3-7 in Trondheim (Norway). About the ProbAI 2019 The Nordic Probabilistic AI School (ProbAI) is a new annual event serving a state-of-the-art expertise in machine learning and artificial intelligence to the public, students, academia and industry. Our objective is to bring an intermediate to advanced level summer school with a particular focus on probabilistic models and deep generative models, covering the topics of latent variable models, inference with sampling and variational approximations, and probabilistic programming and tools. The intentionally small team of invited lecturers will cover a carefully designed curriculum. Through a tight cooperation between our lecturers, and through theoretical lectures and hands-on tutorials, we hope to provide a high quality continuous and consistent knowledge transfer. Go to topics → Registration Fee The registration fee includes all courses, coffee breaks, lunches and banquet. Students (including PhD) → 2500 NOK ~ 256 EUR Academia → 5000 NOK ~ 512 EUR Industry → 10000 NOK ~ 1024 EUR We are also offering a limited number of scholarships. Visit our website to learn more.

Go to website →

In the next couple of months, I will be visiting Arto Klami‘s Multi-Source Probabilistic Inference group. Also, I am happy to I am working again close to my ex-colleague at NTNU and friend Tomasz. This next couple of months look promising and I am looking forward to finalizing all the open-threads that would lead my PhD thesis!

The ArXiv preprint to our paper introducing a joint Point process and Hierarchical RNN for item and time prediction is now available.

---

Time is of the Essence: a Joint Hierarchical RNN and Point Process Model for Time and Item Predictions

In recent years session-based recommendation has emerged as an increasingly applicable type of recommendation. As sessions consist of sequences of events, this type of recommendation is a natural fit for Recurrent Neural Networks (RNNs). Several additions have been proposed for extending such models in order to handle specific problems or data. Two such extensions are 1.) modeling of inter-session relations for catching long term dependencies over user sessions, and 2.) modeling temporal aspects of user-item interactions. The former allows the session-based recommendation to utilize extended session history and inter-session information when providing new recommendations. The latter has been used to both provide state-of-the-art predictions for when the user will return to the service and also for improving recommendations. In this work we combine these two extensions in a joint model for the tasks of recommendation and return-time prediction. The model consists of a Hierarchical RNN for the inter-session and intra-session items recommendation extended with a Point Process model for the time-gaps between the sessions. The experimental results indicate that the proposed model improves recommendations significantly on two datasets over a strong baseline, while simultaneously improving return-time predictions over a baseline return-time prediction model.

https://arxiv.org/pdf/1812.01276.pdf

Last year I had the opportunity to attend this great summer school in the beautiful and lovely city of Lisbon. It was a great week together with a lot of interesting and intelligent people, all of them interested in the amazing and exciting area of machine learning and NLP. I liked it so much last year that I decided to come back this year to volunteer as an assistant in the summer school. Today was the -1 day, where we organized some of the registration stuff, welcomed some student and had some beers. Looks like it will be, again, a great time here in Lisbon

http://lxmls.it.pt/2017/

Continue reading “Lisbon Machine Learning Summer School (LxMLS) 2017” →