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Found 14 papers, 1 papers with code
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising
Third, to deploy MAAB in the large-scale advertising system with millions of advertisers, we propose a mean-field approach.
Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals
Particularly, the proposed algorithm ensured that the BCI classification and the drowsiness estimation had little degradation even when data were compressed by 80%, making it very suitable for continuous wireless telemonitoring of multichannel signals.
Extension of SBL Algorithms for the Recovery of Block Sparse Signals with Intra-Block Correlation
We examine the recovery of block sparse signals and extend the framework in two important directions; one by exploiting signals' intra-block correlation and the other by generalizing signals' block structure.
Compressed Sensing of EEG for Wireless Telemonitoring with Low Energy Consumption and Inexpensive Hardware
Compressed sensing (CS), as an emerging data compression methodology, is promising in catering to these constraints.
Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG via Block Sparse Bayesian Learning
The design of a telemonitoring system via a wireless body-area network with low energy consumption for ambulatory use is highly desirable.
Compressed Sensing for Energy-Efficient Wireless Telemonitoring: Challenges and Opportunities
As a lossy compression framework, compressed sensing has drawn much attention in wireless telemonitoring of biosignals due to its ability to reduce energy consumption and make possible the design of low-power devices.
Fast Marginalized Block Sparse Bayesian Learning Algorithm
One typical correlation structure is the intra-block correlation in block sparse signals.
Robust Face Recognition via Block Sparse Bayesian Learning
Recently, it has been found that algorithms based on a block sparse model can achieve better recognition rates.
$α$-Approximation Density-based Clustering of Multi-valued Objects
In this paper, a concept of $\alpha$-approximation distance is introduced to measure the connectivity between multi-valued objects by taking account of the distribution of the instances.
Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising
In e-commerce advertising, the ad platform usually relies on auction mechanisms to optimize different performance metrics, such as user experience, advertiser utility, and platform revenue.
Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising
In e-commerce advertising, it is crucial to jointly consider various performance metrics, e. g., user experience, advertiser utility, and platform revenue.
Scalable Virtual Valuations Combinatorial Auction Design by Combining Zeroth-Order and First-Order Optimization Method
Subsequently, we propose a novel optimization method that combines both zeroth-order and first-order techniques to optimize the VVCA parameters.
Trajectory-wise Iterative Reinforcement Learning Framework for Auto-bidding
The trained policy can subsequently be deployed for further data collection, resulting in an iterative training framework, which we refer to as iterative offline RL.
MEBS: Multi-task End-to-end Bid Shading for Multi-slot Display Advertising
Through extensive offline and online experiments, we demonstrate the effectiveness and efficiency of our method, and we obtain a 7. 01% lift in Gross Merchandise Volume, a 7. 42% lift in Return on Investment, and a 3. 26% lift in ad buy count.
