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Found 5 papers, 2 papers with code
Ranking-Based Physics-Informed Line Failure Detection in Power Grids
Climate change increases the number of extreme weather events (wind and snowstorms, heavy rains, wildfires) that compromise power system reliability and lead to multiple equipment failures.
Large-Scale Sequential Learning for Recommender and Engineering Systems
In this thesis, we focus on the design of an automatic algorithms that provide personalized ranking by adapting to the current conditions.
Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation (Extended Abstract)
This paper is an extended version of [Burashnikova et al., 2021, arXiv: 2012. 06910], where we proposed a theoretically supported sequential strategy for training a large-scale Recommender System (RS) over implicit feedback, mainly in the form of clicks.
Recommender systems: when memory matters
In this paper, we study the effect of long memory in the learnability of a sequential recommender system including users' implicit feedback.
Learning over no-Preferred and Preferred Sequence of items for Robust Recommendation
In this paper, we propose a theoretically founded sequential strategy for training large-scale Recommender Systems (RS) over implicit feedback, mainly in the form of clicks.
