# Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making

arXiv: https://arxiv.org/abs/2305.01063 · HTML: https://mlsanity.com/paper/2305.01063/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 2 May 2023.

## Builds on (most cited first, 7 of 7)

- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles](https://mlsanity.com/paper/2002.04926/) (2020)
- [Finite-Time Analysis of Kernelised Contextual Bandits](https://mlsanity.com/paper/1309.6869/) (2013)
- [Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling](https://mlsanity.com/paper/1802.09127/) (2018)
- [Explore no more: Improved high-probability regret bounds for non-stochastic bandits](https://mlsanity.com/paper/1506.03271/) (2015)
- [Towards Unbiased and Accurate Deferral to Multiple Experts](https://mlsanity.com/paper/2102.13004/) (2021)
- [Dealing with Expert Bias in Collective Decision-Making](https://mlsanity.com/paper/2106.13539/) (2021)

## Built on by (most cited first, 3 of 3)

- [Wisdom from Diversity: Bias Mitigation Through Hybrid Human-LLM Crowds](https://mlsanity.com/paper/2505.12349/) (2025)
- [Mitigating Biases in Collective Decision-Making: Enhancing Performance in the Face of Fake News](https://mlsanity.com/paper/2403.08829/) (2024)
- [Reliable Fusion of Conflicting Experts](https://mlsanity.com/paper/2609.27913/) (2026)

## Abstract (arXiv)

Experts advising decision-makers are likely to display expertise which varies as a function of the problem instance. In practice, this may lead to sub-optimal or discriminatory decisions against minority cases. In this work we model such changes in depth and breadth of knowledge as a partitioning of the problem space into regions of differing expertise. We provide here new algorithms that explicitly consider and adapt to the relationship between problem instances and experts' knowledge. We first propose and highlight the drawbacks of a naive approach based on nearest neighbor queries. To address these drawbacks we then introduce a novel algorithm - expertise trees - that constructs decision trees enabling the learner to select appropriate models. We provide theoretical insights and empirically validate the improved performance of our novel approach on a range of problems for which existing methods proved to be inadequate.