DiffOR reformulates ordinal regression as continuous generative modeling using diffusion models with dual-decoupling to capture soft semantic transitions.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
GBS replaces two-stage bid landscape modeling with an autoregressive generative model plus reward-aligned policy optimization to improve short- and long-term advertiser surplus in real-time bidding.
FlowTime introduces continuous generative regression using a one-step VAE and normalizing flows for personalized priors to predict watch time while addressing mean-collapse, quantization, and latency issues in prior paradigms.
citing papers explorer
-
DiffoR: A Unified Continuous Generative Framework for Universal Ordinal Regression
DiffOR reformulates ordinal regression as continuous generative modeling using diffusion models with dual-decoupling to capture soft semantic transitions.
-
Generative Bid Shading in Real-Time Bidding Advertising
GBS replaces two-stage bid landscape modeling with an autoregressive generative model plus reward-aligned policy optimization to improve short- and long-term advertiser surplus in real-time bidding.
-
FlowTime: Towards Continuous Generative Watch Time Prediction via Flow-based Personalized Priors
FlowTime introduces continuous generative regression using a one-step VAE and normalizing flows for personalized priors to predict watch time while addressing mean-collapse, quantization, and latency issues in prior paradigms.