Abstract: An artificial intelligence (AI) classifier is trained using supervised training and an effect of noise in the training data is reduced. The training data includes observed noisy labels. A posterior transition matrix (PTM) is used to minimize, in a statistical sense, a cross entropy between a noisy label and a function of the classifier output. A loss function using the PTM is provided to use in training the classifier. The classifier provides final output predictions with good performance even with the existence of noisy labels. Also, information fusion is included in the classifier training using the PTM and an estimated noise transition matrix (NTM) to reduce estimation error at the classifier output.

Abstract: A method of operating a demand side platform (DSP) includes determining a current state of the DSP, wherein the current state of the DSP is based on a remaining bid budget and remaining number of opportunities, receiving, at the DSP, a bid request for one or more advertisement impressions, and determining an uncertainty of a predicted user response probability. The method further includes determining a risk tendency value based on the current state of the DSP, determining an adjusted value of the one or more advertisement impressions based on the uncertainty and risk tendency, determining a bid price for each of the one or more advertisement impressions based on the adjusted value of the one or more advertisement impressions, transmitting the bid price to an exchange platform to participate in an auction, receiving an auction result and updating the current state of the DSP based on the auction result.