“‘Statistical Comparison’ Tag”,2019-12-15 (information theory, sorting, active learning, preference learning)⁠:

Bibliography for tag statistics/order/comparison, most recent first: 1 related tag, 46 annotations, & 10 links (parent).

• See Also
• Gwern
  • “Open Questions”, Gwern2018
  • “GPT-2 Preference Learning for Music Generation”, Gwern2019
  • “Resorting Media Ratings”, Gwern2015
• Links
  • “Epistemic Calibration and Searching the Space of Truth”, Lee2024
  • “Predicting the Direction of Phenotypic Difference”, Gokhman et al 2024
  • “Diffusion Model Alignment Using Direct Preference Optimization”, Wallace et al 2023
  • “A General Theoretical Paradigm to Understand Learning from Human Preferences”, Azar et al 2023
  • “On the Optimal Bounds for Noisy Computing”, Zhu et al 2023
  • “Direct Preference Optimization (DPO): Your Language Model Is Secretly a Reward Model”, Rafailov et al 2023
  • “Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-Oriented Dialogue Systems”, Feng et al 2023
  • “Reputation Inflation”, Filippas et al 2022
  • “Bayesian Inference of the Climbing Grade Scale”, Drummond & Popinga2021
  • “PiRank: Learning To Rank via Differentiable Sorting”, Swezey et al 2020
  • “Rank-Smoothed Pairwise Learning In Perceptual Quality Assessment”, Talebi et al 2020
  • “Self-Play Learning Without a Reward Metric”, Schmidt et al 2019
  • “Group Testing: An Information Theory Perspective”, Aldridge et al 2019
  • “Top-K Off-Policy Correction for a REINFORCE Recommender System”, Chen et al 2018
  • “Comparison Based Learning from Weak Oracles”, Kazemi et al 2018
  • “OptionGAN: Learning Joint Reward-Policy Options Using Generative Adversarial Inverse Reinforcement Learning”, Henderson et al 2017
  • “Analogical-Based Bayesian Optimization”, Le et al 2017
  • “Spectral Method and Regularized MLE Are Both Optimal for Top-K Ranking”, Chen et al 2017
  • “The Competitiveness of Games in Professional Sports Leagues”, Wills2017
  • “Deep Reinforcement Learning from Human Preferences”, Christiano et al 2017
  • “PBO: Preferential Bayesian Optimization”, Gonzalez et al 2017
  • “D-TS: Double Thompson Sampling for Dueling Bandits”, Wu & Liu2016
  • “Just Sort It! A Simple and Effective Approach to Active Preference Learning”, Maystre & Grossglauser2015
  • “On the Complexity of Best Arm Identification in Multi-Armed Bandit Models”, Kaufmann et al 2014
  • “Bayesian Active Learning for Classification and Preference Learning”, Houlsby et al 2011
  • “Case Studies in Bayesian Computation Using INLA”, Martino & Rue2010
  • “Sorting from Noisy Information”, Braverman & Mossel2009
  • “Can People Distinguish Pâté From Dog Food? [Preprint]”, Bohannon et al 2009
  • “Aggregating Inconsistent Information: Ranking and Clustering”, Ailon et al 2008
  • “Pure Exploration for Multi-Armed Bandit Problems”, Bubeck et al 2008
  • “Do More Expensive Wines Taste Better? Evidence from a Large Sample of Blind Tastings”, Goldstein et al 2008
  • “Noisy Sorting Without Resampling”, Braverman & Mossel2007
  • “Noisy Binary Search and Its Applications”, Karp & Kleinberg2007
  • “Paired Comparison Models for Ranking National Soccer Teams”, Hallinan2005
  • “Bayesian Adaptive Exploration”, Loredo & Chernoff2003
  • “How Dangerous Are Drinking Drivers?”, Levitt & Porter2001
  • “Sympercents: Symmetric Percentage Differences on the 100 Log_e Scale Simplify the Presentation of Log Transformed Data”, Cole2000
  • “Born Again Group Testing: Multiaccess Communications”, Wolf1985
  • “The Analysis of Sequential Experiments With Feedback to Subjects”, Diaconis & Graham1981
  • “Rating the Ratings: Assessing the Psychometric Quality of Rating Data”, Saal et al 1980
  • The Rating of Chessplayers, Past and Present (Second Edition), Elo1978
  • “Optimal Selection Based On Relative Rank (the ‘Secretary Problem’)”, Chow1964
  • “Inconsistencies in a Schedule of Paired Comparisons”
  • “Metacritic Has A (File-Drawer) Problem”
  • “Valuing Research Works by Eliciting Comparisons from EA Researchers”
  • “Futurama Theorem”
  • Sort By Magic
    • multi-armed-bandits
    • preference-learning
    • rating-comparison
  • Wikipedia
• Miscellaneous
• Bibliography