Is Elo Rating Reliable? A Study Under Model Misspecification

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Tang, Shange, Wang, Yuanhao, Jin, Chi
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Uplift Modeling Under Limited Supervision
by: Panagopoulos, George, et al.
Published: (2024)

Best of mini-N in-loop Sampling: A Contextual Quality Reward Model for Reliable and Efficient Best-of-N Sampling
by: Rho, Hyung Gyu, et al.
Published: (2025)

Adaptive Prediction-Powered AutoEval with Reliability and Efficiency Guarantees
by: Park, Sangwoo, et al.
Published: (2025)

Rating Multi-Modal Time-Series Forecasting Models (MM-TSFM) for Robustness Through a Causal Lens
by: Lakkaraju, Kausik, et al.
Published: (2024)

Beyond Pairwise: Empowering LLM Alignment With Ranked Choice Modeling
by: Tang, Yuxuan, et al.
Published: (2025)

New Statistical Framework for Extreme Error Probability in High-Stakes Domains for Reliable Machine Learning
by: Michelucci, Umberto, et al.
Published: (2025)

Industrializing Prediction-Powered Inference: The GLIDE Library for Reliable GenAI and Agentic Systems Evaluation
by: Martinon, Grégoire, et al.
Published: (2026)

Conformal Diffusion Models for Individual Treatment Effect Estimation and Inference
by: Cai, Hengrui, et al.
Published: (2024)

Operationalizing Longitudinal Causal Discovery Under Real-World Workflow Constraints
by: Okuda, Tadahisa, et al.
Published: (2026)

Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies
by: Jeong, Hyunchai, et al.
Published: (2024)

GACE: Learning Graph-Based Cross-Page Ads Embedding For Click-Through Rate Prediction
by: Wang, Haowen, et al.
Published: (2024)

DCRMTA: Unbiased Causal Representation for Multi-touch Attribution
by: Tang, Jiaming
Published: (2024)

Integrating Random Forests and Generalized Linear Models for Improved Accuracy and Interpretability
by: Agarwal, Abhineet, et al.
Published: (2023)

First-Order Efficiency for Probabilistic Value Estimation via A Statistical Viewpoint
by: Liu, Ziqi, et al.
Published: (2026)

Optimized Conformal Selection: Powerful Selective Inference After Conformity Score Optimization
by: Bai, Tian, et al.
Published: (2024)

Transformer-Based Spatial-Temporal Counterfactual Outcomes Estimation
by: Li, He, et al.
Published: (2025)

Confounder Detection via Treatment Intent: A New Observational Study Design
by: Plecko, Drago, et al.
Published: (2026)

Model-Free Assessment of Simulator Fidelity via Quantile Curves
by: Iyengar, Garud, et al.
Published: (2025)

New insights into Elo algorithm for practitioners and statisticians
by: Szczecinski, Leszek
Published: (2026)

Interpreting Time Series Forecasts with LIME and SHAP: A Case Study on the Air Passengers Dataset
by: Shukla, Manish
Published: (2025)

How Many Human Survey Respondents is a Large Language Model Worth? An Uncertainty Quantification Perspective
by: Huang, Chengpiao, et al.
Published: (2025)

RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Model
by: Li, Peiwen, et al.
Published: (2024)

Data-Augmented Few-Shot Neural Emulator for Computer-Model System Identification
by: Jantre, Sanket, et al.
Published: (2025)

Modeling and Discovering Direct Causes for Predictive Models
by: Chen, Yizuo, et al.
Published: (2024)

Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach
by: Takayama, Masayuki, et al.
Published: (2024)

Statistical Tests for Replacing Human Decision Makers with Algorithms
by: Feng, Kai, et al.
Published: (2023)

Causal Discovery Under Local Privacy
by: Binkytė, Rūta, et al.
Published: (2023)

Teleporter Theory: A General and Simple Approach for Modeling Cross-World Counterfactual Causality
by: Li, Jiangmeng, et al.
Published: (2024)

A Data-Driven Two-Phase Multi-Split Causal Ensemble Model for Time Series
by: Ma, Zhipeng, et al.
Published: (2024)

TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models
by: González, Javier, et al.
Published: (2023)

Identifying Causal Effects Under Functional Dependencies
by: Chen, Yizuo, et al.
Published: (2024)

Counterfactual Probabilistic Diffusion with Expert Models
by: Mu, Wenhao, et al.
Published: (2025)

Flexible Counterfactual Explanations with Generative Models
by: Hellemans, Stig, et al.
Published: (2025)

Causal Identification in Time Series Models
by: Jahn, Erik, et al.
Published: (2025)

Cardinality-Regularized Hawkes-Granger Model
by: Idé, Tsuyoshi, et al.
Published: (2022)

Partially Observed Structural Causal Models
by: Orujlu, Turan, et al.
Published: (2026)

Compositional Models for Estimating Causal Effects
by: Pruthi, Purva, et al.
Published: (2024)

A Recipe for Causal Graph Regression: Confounding Effects Revisited
by: Yin, Yujia, et al.
Published: (2025)

Comparing Foundation Models using Data Kernels
by: Duderstadt, Brandon, et al.
Published: (2023)

Multi-Domain Causal Discovery in Bijective Causal Models
by: Jalaldoust, Kasra, et al.
Published: (2025)