Elo ratings learn model parameters quickly using Markov chains.
problem Ranking players in online settings.
method Bradley--Terry--Luce model and Markov chain theory.
result Elo learns model parameters at a competitive rate.
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17 results for “Elo”
Study shows Elo models fail to accurately measure transitive strength in competitive games.
problem Elo models fail to correctly identify the transitive component in real-world competitive games.
method Investigated the challenge of identifying the transitive component in games, proposed an extension of the Elo score.
result Disc ranking system assigns two scores: skill and consistency.
Elo rating outperforms complex models in skill estimation despite model misspecification.
problem Elo rating's reliability in skill estimation is questioned due to model misspecification.
method Interpreted Elo as online gradient descent and conducted synthetic experiments on non-BT models.
result Elo's superior performance in win rate prediction compared to complex models is explained through its sparsity and ranking effectiveness.
MOVDA improves skill ratings by considering margin of victory deviations.
problem Traditional rating systems discard valuable performance data.
method Margin of Victory Differential Analysis (MOVDA) learns a non-linear function to predict expected MOV and uses the difference between true and expected MOV for rating updates.
result MOVDA significantly outperforms standard ELO and Bayesian baselines in NBA basketball data.
Prediction and modelling of competitive sports outcomes has received much recent attention, especially from the Bayesian statistics and machine learning communities. In the real world setting of outcome prediction, the seminal Élő update still remains, after more than 50 years, a valuable baseline which is difficult to…
Dynamic paired comparison models, such as Elo and Glicko, are frequently used for sports prediction and ranking players or teams. We present an alternative dynamic paired comparison model which uses a Gaussian Process (GP) as a prior for the time dynamics rather than the Markovian dynamics usually assumed. In addition,…
New method for evaluating LLMs reduces bias in open-ended evaluations.
problem Bias in Elo-based ratings of LLMs due to data redundancies.
method Proposes evaluation as a 3-player game and introduces novel solution concepts.
result Novel method leads to more robust and intuitive ratings.
Research builds an index measuring analysts' perception of informational asymmetry.
problem Measuring the level of informational asymmetry among companies.
method Developed an algorithm based on Elo rating to capture analysts' perception.
result The model shows good fit with significant variables: coverage, volatility, Tobin q, and size.
A simplified Bayesian approach for online sports rating.
problem Rating players/teams based on game outcomes.
method Bayesian approach using Kalman filter principles.
result Known algorithms like Elo and TrueSkill are special cases.
New meta-score EPP interprets model performance differences.
problem Lack of interpretable benchmarks for model performance.
method Elo-based Predictive Power (EPP) meta-score, logistic regression.
result EPP scores have probabilistic interpretation and can be compared between data sets.
TabPFN-3 scales tabular prediction models to large datasets and improves performance and speed.
problem Scaling tabular prediction models to large datasets with high performance and efficiency.
method Pretrained on synthetic data, introduces test-time compute scaling, and uses row-chunking.
result Significantly outperforms other models on TabArena and diverse datasets.
Variational Proximal Policy Optimization improves reinforcement learning from human feedback.
problem Policy mode collapse and brittle exploration loops in reinforcement learning.
method Particle-based variational inference framework with Mixture-of-Experts architecture.
result Significant improvements in complex reasoning benchmarks.
Bayesian rating system for large competitions improves prediction and efficiency.
problem Rating systems for large, competitive events like online programming contests.
method Developed a Bayesian rating system for many participants, proving robustness and runtime.
result The system outperforms existing systems in accuracy and computation speed.
Gradient equilibrium improves online learning performance without requiring sublinear regret.
problem Achieving sublinear regret in online learning.
method Gradient equilibrium: average of gradients converges to zero.
result Gradient equilibrium can be achieved by standard online learning methods.
Transformers learn to predict chess moves with surprising accuracy and strength.
problem Training transformers on chess to predict moves accurately.
method Large-scale chess dataset (10M games), supervised learning with up to 270M parameters.
result Transformers can predict action-values for novel boards with high accuracy.
Paper introduces metrics for evaluating multi-agent policies using best response dynamics.
problem Evaluation and ranking of multi-agent policies in reinforcement learning.
method Adopting strict best response dynamics (SBRD) to model selfish behaviors, proposing perturbed SBRD for dynamic and non-stationary settings.
result Proposed perturbed SBRD can observe policies with maximum metrics and differ from optimal by any given tolerance.
Adaptive Nucleus Truncation Improves Long-Form Reasoning
problem Improving long-form reasoning in language models
method Adaptive Nucleus Truncation Sampling (ANTS)
result Significant performance gains across various benchmarks