Weekend of a Data Scientist is series of articles with some cool stuff I care about. The idea is to spend a weekend by learning something new, reading and coding.
This week was pretty busy, so I read little bit below my average. The plan for this week is to catch up!
ICML 2018 has finished and papers became available, they are pure gold for Data Scientists out there! Many different topics cover State of the Art approaches.
I highly recommend to go thought it, at least to be familiar with papers. The full list can be found here: https://icml.cc/Conferences/2018/Schedule?type=Poster
I just wanna highlight several papers I’ve read already.
- Markov Modulated Gaussian Cox Processes for Semi-Stationary Intensity Modeling of Events Data http://proceedings.mlr.press/v80/kim18a.html
In short studies proposed Markov Modulated Poisson Process model incorporated Gaussian Cox Process model, which allowed to cover two important features GP-based smooth intensity changes and major regime switches through a hidden Markov process. Paper showed two practical examples applied to Kaggle’s football data and Italy’s Earthquakes Data. Results showed superior performance on such combined model on semi-stationary data, which is always challenging for modelling.
2. Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling http://proceedings.mlr.press/v80/lee18b.html
I’m gathering experience on reinforcement learning, so I found this paper very interesting. Researchers used supervised learning combined with reinforcement learning and to learn game strategy with kernel-based Monte Carlo tree search within a continuous space. As a result, the model they made won an international digital curling competition (Yes, this thing exists).
3. Visualizing and Understanding Atari Agents http://proceedings.mlr.press/v80/greydanus18a.html
When I read the original paper on AI playing Atari games back in 2013, I was blown away! But Q-learning made huge steps since then: Deep Mind made AI that won Go championship, OpenAI made AI that can play Dota (and it probably will win against the current champions). Q-learning was kind of black-box for me.
I found this paper very interesting because it is breaking down the decision-process used by AI, authors made very explicit visualizations. As a result, papers bring solid explanations and visualizations that can make Atari agent more understandable even for non-experts.