4 citations · 4 across the 5 of their papers we have counts for
5 papers
Off-Policy Evaluation and Learning for Survival Outcomes under Censoring
Kohsuke Kubota, Mitsuhiro Takahashi, Yuta Saito
Optimizing survival outcomes, such as patient survival or customer retention, is a critical objective in data-driven decision-making. Off-Policy Evaluation~(OPE) provides a powerfu…
MultiScale Contextual Bandits for Long Term Objectives
Richa Rastogi, Yuta Saito, Thorsten Joachims
The feedback that AI systems (e.g., recommender systems, chatbots) collect from user interactions is a crucial source of training data. While short-term feedback (e.g., clicks, eng…
Long-term Off-Policy Evaluation and Learning
Yuta Saito, Himan Abdollahpouri, Jesse Anderton +2
Short- and long-term outcomes of an algorithm often differ, with damaging downstream effects. A known example is a click-bait algorithm, which may increase short-term clicks but da…
Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems
Riku Togashi, Kenshi Abe, Yuta Saito
Typical recommendation and ranking methods aim to optimize the satisfaction of users, but they are often oblivious to their impact on the items (e.g., products, jobs, news, video)…
POTEC: Off-Policy Learning for Large Action Spaces via Two-Stage Policy Decomposition
Yuta Saito, Jihan Yao, Thorsten Joachims
We study off-policy learning (OPL) of contextual bandit policies in large discrete action spaces where existing methods -- most of which rely crucially on reward-regression models…