Hsu-Chao Lai
  • Bio
  • Research
  • Papers
  • Experience
  • Teaching
  • Courses
    • Data Mining
    • Probability and Statistics
    • Graph Mining and Learning
  • Publications
    • SPAIT: A Novel Movement Forecasting Model with Shot-Based Position-Adaptive Inference Transformer in Badminton
    • A Market-Aware Real-Time Bidding Strategy Using Censored Data with Reinforcement Learning in Online Advertising
    • Towards Hierarchical Multi-Agent Decision-Making for Uncertainty-Aware EV Charging
    • DeCo: Defect-Aware Modeling with Contrasting Matching for Optimizing Task Assignment in Online IC Testing
    • NEST: A Novel Ensemble Method for Estimating Spatio-Temporal Gait Parameters Using Inertial Measurement Units
    • Player Movement Predictions Using Team and Opponent Dynamics for Doubles Badminton
    • A Confidence-Based Power-Efficient Framework for Sleep Stage Classification on Consumer Wearables
    • MoCVAE: Movement Prediction by A Conditional Variational Autoencoder for Doubles Badminton
    • A Robust Real Time Bidding Strategy Against Inaccurate CTR Predictions by Using Cluster Expected Win Rate
    • Learning the Co-evolution Process on Live Stream Platforms with Dual Self-attention for Next-topic Recommendations
    • Pairs Trading Strategy Optimization Using Proximal Policy Optimization Algorithms
    • Spread Movement Prediction for Pairs Trading with High-Frequency Limit Order Data
    • Structural break-aware pairs trading strategy using deep reinforcement learning
    • SPENT(^mbox+): A Category- and Region-aware Successive POI Recommendation Model
    • The Design and Implementation of a Blockchain-Based Logistics Platform for International Trade
    • Live Multi-Streaming and Donation Recommendations via Coupled Donation-Response Tensor Factorization
    • Optimizing Item and Subgroup Configurations for Social-Aware VR Shopping
    • Social Attentive Network for Live Stream Recommendation
    • On VR Spatial Query for Dual Entangled Worlds
    • Social-Aware VR Configuration Recommendation via Multi-Feedback Coupled Tensor Factorization
    • Predicting traffic of online advertising in real-time bidding systems from perspective of demand-side platforms
  • Experience
  • Research
    • Group Recommendation
    • Social Influence in Networks
    • Team Sports Intelligence
    • Intelligent Decision Support
    • Learning in Dynamic Markets
  • Teaching

A Robust Real Time Bidding Strategy Against Inaccurate CTR Predictions by Using Cluster Expected Win Rate

Jan 1, 2023·
Wen-Yueh Shih
,
Hsu-Chao Lai
,
Jiun-Long Huang
· 0 min read
URL DOI
Type
Journal article
Publication
IEEE Access
Last updated on Jan 1, 2023
← MoCVAE: Movement Prediction by A Conditional Variational Autoencoder for Doubles Badminton Jan 1, 2024
Learning the Co-evolution Process on Live Stream Platforms with Dual Self-attention for Next-topic Recommendations Jan 1, 2023 →

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