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Found 5 papers, 0 papers with code
Federated XGBoost on Sample-Wise Non-IID Data
Federated Learning (FL) is a paradigm for jointly training machine learning algorithms in a decentralized manner which allows for parties to communicate with an aggregator to create and train a model, without exposing the underlying raw data distribution of the local parties involved in the training process.
SimPO: Simultaneous Prediction and Optimization
Concretely, predictive models are often employed in estimating the parameters for the input values that are utilized for optimization models as isolated processes.
Predicting Loss Risks for B2B Tendering Processes
There are many predictive models that offer likelihood insights and win prediction modeling for these opportunities.
Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning
This approach makes the use of gradient boosted trees practical in enterprise federated learning.
Temporal Tensor Transformation Network for Multivariate Time Series Prediction
In this work, we present a novel deep learning architecture, known as Temporal Tensor Transformation Network, which transforms the original multivariate time series into a higher order of tensor through the proposed Temporal-Slicing Stack Transformation.
