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Found 7 papers, 0 papers with code
Unbiased Decisions Reduce Regret: Adversarial Domain Adaptation for the Bank Loan Problem
We introduce adversarial optimism (AdOpt) to directly address bias in the training set using adversarial domain adaptation.
Practical Policy Optimization with Personalized Experimentation
Many organizations measure treatment effects via an experimentation platform to evaluate the casual effect of product variations prior to full-scale deployment.
Neural Pseudo-Label Optimism for the Bank Loan Problem
The lender only observes whether a customer will repay a loan if the loan is issued to begin with, and thus modeled decisions affect what data is available to the lender for future decisions.
Interpretable Personalized Experimentation
Black-box heterogeneous treatment effect (HTE) models are increasingly being used to create personalized policies that assign individuals to their optimal treatments.
Looper: An end-to-end ML platform for product decisions
Modern software systems and products increasingly rely on machine learning models to make data-driven decisions based on interactions with users, infrastructure and other systems.
Real-world Video Adaptation with Reinforcement Learning
Client-side video players employ adaptive bitrate (ABR) algorithms to optimize user quality of experience (QoE).
Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints
Real-world applications frequently have constraints with respect to a currently deployed policy.
