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Found 5 papers, 1 papers with code
SIGIR 2021 E-Commerce Workshop Data Challenge
The 2021 SIGIR workshop on eCommerce is hosting the Coveo Data Challenge for "In-session prediction for purchase intent and recommendations".
Efficient Learning of Ensembles with QuadBoost
We first present a general risk bound for ensembles that depends on the Lp norm of the weighted combination of voters which can be selected from a continuous set.
Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm
We propose an extensive analysis of the behavior of majority votes in binary classification.
On Generalizing the C-Bound to the Multiclass and Multi-label Settings
The C-bound, introduced in Lacasse et al., gives a tight upper bound on the risk of a binary majority vote classifier.
On the Generalization of the C-Bound to Structured Output Ensemble Methods
This paper generalizes an important result from the PAC-Bayesian literature for binary classification to the case of ensemble methods for structured outputs.
