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pro vyhledávání: '"Sharma, Navodita"'
In many applications, especially due to lack of supervision or privacy concerns, the training data is grouped into bags of instances (feature-vectors) and for each bag we have only an aggregate label derived from the instance-labels in the bag. In le
Externí odkaz:
http://arxiv.org/abs/2411.12334
Autor:
Sharma, Navodita, Vinod, Vishnu, Thakurta, Abhradeep, Agarwal, Alekh, Balle, Borja, Dann, Christoph, Raghuveer, Aravindan
The offline reinforcement learning (RL) problem aims to learn an optimal policy from historical data collected by one or more behavioural policies (experts) by interacting with an environment. However, the individual experts may be privacy-sensitive
Externí odkaz:
http://arxiv.org/abs/2411.13598
Autor:
Havaldar, Shreyas, Sharma, Navodita, Sareen, Shubhi, Shanmugam, Karthikeyan, Raghuveer, Aravindan
Learning from Label Proportions (LLP) is a learning problem where only aggregate level labels are available for groups of instances, called bags, during training, and the aim is to get the best performance at the instance-level on the test data. This
Externí odkaz:
http://arxiv.org/abs/2310.08056
Time series prediction is an important problem in machine learning. Previous methods for time series prediction did not involve additional information. With a lot of dynamic knowledge graphs available, we can use this additional information to predic
Externí odkaz:
http://arxiv.org/abs/2003.03919