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Found 17 papers, 3 papers with code
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.
ECLM: Efficient Edge-Cloud Collaborative Learning with Continuous Environment Adaptation
In this paper, we propose ECLM, an edge-cloud collaborative learning framework for rapid model adaptation for dynamic edge environments.
To Store or Not? Online Data Selection for Federated Learning with Limited Storage
We first define a new data valuation metric for data evaluation and selection in FL with theoretical guarantees for speeding up model convergence and enhancing final model accuracy, simultaneously.
Hierarchically Constrained Adaptive Ad Exposure in Feeds
To this end, adaptive ad exposure has become an appealing strategy to boost the overall performance of the feed.
Data-Free Evaluation of User Contributions in Federated Learning
We evaluate the performance of PCA and Fed-PCA using the MNIST dataset and a large industrial product recommendation dataset.
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.
We Know What You Want: An Advertising Strategy Recommender System for Online Advertising
To achieve this goal, the advertising platform needs to identify the advertiser's optimization objectives, and then recommend the corresponding strategies to fulfill the objectives.
Toward Understanding the Influence of Individual Clients in Federated Learning
Federated learning allows mobile clients to jointly train a global model without sending their private data to a central server.
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.
A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction
For e-commerce platforms such as Taobao and Amazon, advertisers play an important role in the entire digital ecosystem: their behaviors explicitly influence users' browsing and shopping experience; more importantly, advertiser's expenditure on advertising constitutes a primary source of platform revenue.
Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising
In E-commerce, advertising is essential for merchants to reach their target users.
Learning to Accelerate Heuristic Searching for Large-Scale Maximum Weighted b-Matching Problems in Online Advertising
Bipartite b-matching is fundamental in algorithm design, and has been widely applied into economic markets, labor markets, etc.
Distributed Optimization over Block-Cyclic Data
We consider practical data characteristics underlying federated learning, where unbalanced and non-i. i. d.
Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability
In this work, we consider a practical and ubiquitous issue when deploying federated learning in mobile environments: intermittent client availability, where the set of eligible clients may change during the training process.
Online Pricing with Reserve Price Constraint for Personal Data Markets
The analysis and evaluation results reveal that our proposed pricing mechanism incurs low practical regret, online latency, and memory overhead, and also demonstrate that the existence of reserve price can mitigate the cold-start problem in a posted price mechanism, and thus can reduce the cumulative regret.
