Projekteja vuodessa
Abstrakti
Active learning is usually applied to acquire labels of informative data points in supervised learning, to maximize accuracy in a sample-efficient way. However, maximizing the accuracy is not the end goal when the results are used for decision-making, for example in personalized medicine or economics. We argue that when acquiring samples sequentially, separating learning and decision-making is sub-optimal, and we introduce a novel active learning strategy which takes the down-the-line decision problem into account. Specifically, we introduce a novel active learning criterion which maximizes the expected information gain on the posterior distribution of the optimal decision. We compare our decision-making-aware active learning strategy to existing alternatives on both simulated and real data, and show improved performance in decision-making accuracy.
Alkuperäiskieli | Englanti |
---|---|
Sivumäärä | 20 |
Julkaisu | Transactions on Machine Learning Research |
Tila | Julkaistu - 12 kesäk. 2024 |
OKM-julkaisutyyppi | A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä |
Sormenjälki
Sukella tutkimusaiheisiin 'Targeted Active Learning for Bayesian Decision-Making'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.Projektit
- 4 Päättynyt
-
Interaktiivinen koneoppiminen useista biodatalähteistä
Kaski, S. (Vastuullinen tutkija)
01/01/2019 → 31/08/2021
Projekti: Academy of Finland: Other research funding
-
-: Finnish Center for Artificial Intelligence
Kaski, S. (Vastuullinen tutkija)
01/01/2019 → 31/12/2022
Projekti: Academy of Finland: Other research funding
-
Interaktiivinen koneoppiminen useista biodatalähteistä
Kaski, S. (Vastuullinen tutkija)
01/01/2016 → 31/08/2021
Projekti: Academy of Finland: Other research funding