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Markdowns in E-Commerce Fresh Retail: A Counterfactual Prediction and Multi-Period Optimization Approach
In this paper, by leveraging abundant observational transaction data, we propose a novel data-driven and interpretable pricing approach for markdowns, consisting of counterfactual prediction and multi-period price optimization.
Distributed Variational Bayesian Algorithms Over Sensor Networks
Distributed inference/estimation in Bayesian framework in the context of sensor networks has recently received much attention due to its broad applicability.
Distributed Variational Bayesian Algorithms for Extended Object Tracking
This paper is concerned with the problem of distributed extended object tracking, which aims to collaboratively estimate the state and extension of an object by a network of nodes.
A Unified Framework for Marketing Budget Allocation
In our approach, a semi-black-box model is built to forecast the dynamic market response and an efficient optimization method is proposed to solve the complex allocation task.
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