Zobrazeno 1 - 10
of 100
pro vyhledávání: '"Van Hoof, Herke"'
Exposure bias is a well-known issue in recommender systems where items and suppliers are not equally represented in the recommendation results. This bias becomes particularly problematic over time as a few items are repeatedly over-represented in rec
Externí odkaz:
http://arxiv.org/abs/2408.04332
Two typical forms of bias in user interaction data with recommender systems (RSs) are popularity bias and positivity bias, which manifest themselves as the over-representation of interactions with popular items or items that users prefer, respectivel
Externí odkaz:
http://arxiv.org/abs/2404.18640
Conventional reinforcement learning (RL) methods can successfully solve a wide range of sequential decision problems. However, learning policies that can generalize predictably across multiple tasks in a setting with non-Markovian reward specificatio
Externí odkaz:
http://arxiv.org/abs/2403.15301
Learning in high-dimensional action spaces is a key challenge in applying reinforcement learning (RL) to real-world systems. In this paper, we study the possibility of controlling power networks using RL methods. Power networks are critical infrastru
Externí odkaz:
http://arxiv.org/abs/2311.02129
Pool-based active learning (AL) is a promising technology for increasing data-efficiency of machine learning models. However, surveys show that performance of recent AL methods is very sensitive to the choice of dataset and training setting, making t
Externí odkaz:
http://arxiv.org/abs/2309.05477
In multi-agent problems requiring a high degree of cooperation, success often depends on the ability of the agents to adapt to each other's behavior. A natural solution concept in such settings is the Stackelberg equilibrium, in which the ``leader''
Externí odkaz:
http://arxiv.org/abs/2302.03438
Autor:
Kuric, David, van Hoof, Herke
Hierarchical methods in reinforcement learning have the potential to reduce the amount of decisions that the agent needs to perform when learning new tasks. However, finding reusable useful temporal abstractions that facilitate fast learning remains
Externí odkaz:
http://arxiv.org/abs/2212.11726
Exposure bias is a well-known issue in recommender systems where items and suppliers are not equally represented in the recommendation results. This is especially problematic when bias is amplified over time as a few items (e.g., popular ones) are re
Externí odkaz:
http://arxiv.org/abs/2209.01665
In reinforcement learning, different reward functions can be equivalent in terms of the optimal policies they induce. A particularly well-known and important example is potential shaping, a class of functions that can be added to any reward function
Externí odkaz:
http://arxiv.org/abs/2208.09570
Autor:
Gagrani, Mukul, Rainone, Corrado, Yang, Yang, Teague, Harris, Jeon, Wonseok, Van Hoof, Herke, Zeng, Weiliang Will, Zappi, Piero, Lott, Christopher, Bondesan, Roberto
Recent works on machine learning for combinatorial optimization have shown that learning based approaches can outperform heuristic methods in terms of speed and performance. In this paper, we consider the problem of finding an optimal topological ord
Externí odkaz:
http://arxiv.org/abs/2207.05899