45 citations · 51 across the 7 of their papers we have counts for
7 papers
Unbiased Decisions Reduce Regret: Adversarial Domain Adaptation for the Bank Loan Problem
Elena Gal, Shaun Singh, Aldo Pacchiano +3
In many real world settings binary classification decisions are made based on limited data in near real-time, e.g. when assessing a loan application. We focus on a class of these p…
Practical Policy Optimization with Personalized Experimentation
Mia Garrard, Hanson Wang, Ben Letham +9
Many organizations measure treatment effects via an experimentation platform to evaluate the casual effect of product variations prior to full-scale deployment. However, standard e…
Neural Pseudo-Label Optimism for the Bank Loan Problem
Aldo Pacchiano, Shaun Singh, Edward Chou +2
We study a class of classification problems best exemplified by the \emph{bank loan} problem, where a lender decides whether or not to issue a loan. The lender only observes whethe…
Interpretable Personalized Experimentation
Han Wu, Sarah Tan, Weiwei Li +7
Black-box heterogeneous treatment effect (HTE) models are increasingly being used to create personalized policies that assign individuals to their optimal treatments. However, they…
Looper: An end-to-end ML platform for product decisions
Igor L. Markov, Hanson Wang, Nitya Kasturi +16
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
Hongzi Mao, Shannon Chen, Drew Dimmery +5
Client-side video players employ adaptive bitrate (ABR) algorithms to optimize user quality of experience (QoE). We evaluate recently proposed RL-based ABR methods in Facebook's we…
Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints
Samuel Daulton, Shaun Singh, Vashist Avadhanula +2
Recent advances in contextual bandit optimization and reinforcement learning have garnered interest in applying these methods to real-world sequential decision making problems. Rea…