10 papers
Decision-Calibrated Conformal Uncertainty for Pacing Decisions in Streaming Advertising
Prashant Shekhar, Caroline Howard
We develop a decision-calibrated conformal framework for pacing decisions in streaming advertising. Pacing depends on uncertain future inventory, demand pressure, incremental respo…
Privacy-Robust Incrementality Measurement for Advertising Systems under Signal Loss
Prashant Shekhar, Caroline Howard
Advertising platforms use randomized lift tests to measure incrementality, but privacy-preserving reporting systems degrade the observed signal through match-rate loss, linkability…
PILOT: A Data-Free Continual Learning Approach for Real-Time Semantic Segmentation via Boundary Guidance
Yujing Zhou, Prashant Shekhar, Thomas Yang +1
Real-time semantic segmentation models offer an excellent balance between accuracy and inference speed. However, deploying these models in dynamic real world environments often req…
Choosing Online Experiment Designs under Interference in Ads, Recommendations, and Member-Experience Systems
Prashant Shekhar, Caroline Howard
Online experiments in ads, recommendation, and member-experience systems are often planned before the dominant interference mechanism is known. A treatment may propagate through bu…
Support-aware offline policy selection for advertising marketplaces
Prashant Shekhar, Caroline Howard
Logged advertising auctions make offline reserve-price evaluation attractive but risky. Replay tables can identify policies with large apparent yield gains, yet they can also hide…
Decision Support for Marketplace Policies under Incomplete Evidence: From Replay to Launch Readiness
Prashant Shekhar, Caroline Howard
Marketplace platforms routinely evaluate pricing and allocation policies using logged observational data, yet strong offline performance does not imply that a policy is safe to dep…