From Optimizing Engagement to Measuring Value

21 August 2020

Papers citing "From Optimizing Engagement to Measuring Value"

9 / 9 papers shown

Title
System-2 Recommenders: Disentangling Utility and Engagement in Recommendation Systems via Temporal Point-Processes Arpit Agarwal Nicolas Usunier A. Lazaric Maximilian Nickel 30 3 0 29 May 2024
From Model Performance to Claim: How a Change of Focus in Machine Learning Replicability Can Help Bridge the Responsibility Gap Tianqi Kou 44 0 0 19 Apr 2024
Optimization's Neglected Normative Commitments Benjamin Laufer T. Gilbert Helen Nissenbaum OffRL 21 4 0 27 May 2023
Choosing the Right Weights: Balancing Value, Strategy, and Noise in Recommender Systems S. Milli Emma Pierson Nikhil Garg 9 9 0 27 May 2023
Trustworthy Social Bias Measurement Rishi Bommasani Percy Liang 27 10 0 20 Dec 2022
Measuring Disparate Outcomes of Content Recommendation Algorithms with Distributional Inequality Metrics Tomo Lazovich Luca Belli Aaron Gonzales Amanda Bower U. Tantipongpipat K. Lum Ferenc Huszár Rumman Chowdhury 12 17 0 03 Feb 2022
The MineRL BASALT Competition on Learning from Human Feedback Rohin Shah Cody Wild Steven H. Wang Neel Alex Brandon Houghton ... Stephanie Milani Nicholay Topin Pieter Abbeel Stuart J. Russell Anca Dragan 28 31 0 05 Jul 2021
On component interactions in two-stage recommender systems Jiri Hron K. Krauth Michael I. Jordan Niki Kilbertus CML LRM 40 31 0 28 Jun 2021
Avoiding Tampering Incentives in Deep RL via Decoupled Approval J. Uesato Ramana Kumar Victoria Krakovna Tom Everitt Richard Ngo Shane Legg 26 14 0 17 Nov 2020