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Found 21 papers, 3 papers with code
An Adaptable Budget Planner for Enhancing Budget-Constrained Auto-Bidding in Online Advertising
Extensive simulation experiments and real-world A/B testing validate the effectiveness of ABPlanner, demonstrating its capability to enhance the cumulative value achieved by auto-bidders.
AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Games
We believe that AuctionNet is applicable not only to research on bid decision-making in ad auctions but also to the general area of decision-making in large-scale games.
SentiXRL: An advanced large language Model Framework for Multilingual Fine-Grained Emotion Classification in Complex Text Environment
With strong expressive capabilities in Large Language Models(LLMs), generative models effectively capture sentiment structures and deep semantics, however, challenges remain in fine-grained sentiment classification across multi-lingual and complex contexts.
Efficient Bilinear Attention-based Fusion for Medical Visual Question Answering
Medical Visual Question Answering (MedVQA) has attracted growing interest at the intersection of computer vision and natural language processing.
Contrastive Adversarial Training for Unsupervised Domain Adaptation
Domain adversarial training has shown its effective capability for finding domain invariant feature representations and been successfully adopted for various domain adaptation tasks.
AIGB: Generative Auto-bidding via Conditional Diffusion Modeling
Auto-bidding plays a crucial role in facilitating online advertising by automatically providing bids for advertisers.
Enhanced Textual Feature Extraction for Visual Question Answering: A Simple Convolutional Approach
Visual Question Answering (VQA) has emerged as a highly engaging field in recent years, with increasing research focused on enhancing VQA accuracy through advanced models such as Transformers.
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.
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.
Automated Deterministic Auction Design with Objective Decomposition
Identifying high-revenue mechanisms that are both dominant strategy incentive compatible (DSIC) and individually rational (IR) is a fundamental challenge in auction design.
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.
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.
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.
$α$-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.
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.
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.
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.
Fast Marginalized Block Sparse Bayesian Learning Algorithm
One typical correlation structure is the intra-block correlation in block sparse signals.
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.
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.
