activity
20172020
most citedEstimating Position Bias without Intrusive Interventions

103 citations · 201 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CY2020

Functionally Effective Conscious AI Without Suffering

Aman Agarwal, Shimon Edelman

Insofar as consciousness has a functional role in facilitating learning and behavioral control, the builders of autonomous AI systems are likely to attempt to incorporate it into t…

cs.IR2018★ 103 cited

Estimating Position Bias without Intrusive Interventions

Aman Agarwal, Ivan Zaitsev, Xuanhui Wang +3

Presentation bias is one of the key challenges when learning from implicit feedback in search engines, as it confounds the relevance signal. While it was recently shown how counter…

cs.IR2018★ 53 cited

Intervention Harvesting for Context-Dependent Examination-Bias Estimation

Zhichong Fang, Aman Agarwal, Thorsten Joachims

Accurate estimates of examination bias are crucial for unbiased learning-to-rank from implicit feedback in search engines and recommender systems, since they enable the use of Inve…

cs.IR2018★ 3 cited

Offline Comparison of Ranking Functions using Randomized Data

Aman Agarwal, Xuanhui Wang, Cheng Li +2

Ranking functions return ranked lists of items, and users often interact with these items. How to evaluate ranking functions using historical interaction logs, also known as off-po…

cs.LG2018★ 5 cited

Consistent Position Bias Estimation without Online Interventions for Learning-to-Rank

Aman Agarwal, Ivan Zaitsev, Thorsten Joachims

Presentation bias is one of the key challenges when learning from implicit feedback in search engines, as it confounds the relevance signal with uninformative signals due to positi…

cs.IR2018★ 2 cited

A General Framework for Counterfactual Learning-to-Rank

Aman Agarwal, Kenta Takatsu, Ivan Zaitsev +1

Implicit feedback (e.g., click, dwell time) is an attractive source of training data for Learning-to-Rank, but its naive use leads to learning results that are distorted by present…

cs.LG2017★ 35 cited

Effective Evaluation using Logged Bandit Feedback from Multiple Loggers

Aman Agarwal, Soumya Basu, Tobias Schnabel +1

Accurately evaluating new policies (e.g. ad-placement models, ranking functions, recommendation functions) is one of the key prerequisites for improving interactive systems. While…