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[2502.03576] Clone-Robust Weights in Metric Spaces: Handling Redundancy Bias for Benchmark Aggregation
Summary
This article presents a theoretical framework for clone-robust weighting functions in metric spaces, addressing redundancy bias in benchmark aggregation and other applications.
Why It Matters
The research is significant as it tackles the challenge of ensuring fair representation in data aggregation, particularly in adversarial contexts. By introducing clone-proof weighting functions, it offers a solution that could enhance the robustness of various machine learning applications, including domain adaptation and voting systems.
Key Takeaways
- Introduces clone-proof weighting functions to mitigate redundancy bias.
- Extends the maximum uncertainty principle to general metric spaces.
- Establishes axioms for constructing robust weighting functions.
- Addresses the existence of these functions in Euclidean spaces.
- Proposes a general method for constructing clone-proof weights.
Computer Science > Machine Learning arXiv:2502.03576 (cs) [Submitted on 5 Feb 2025 (v1), last revised 16 Feb 2026 (this version, v3)] Title:Clone-Robust Weights in Metric Spaces: Handling Redundancy Bias for Benchmark Aggregation Authors:Damien Berriaud, Roger Wattenhofer View a PDF of the paper titled Clone-Robust Weights in Metric Spaces: Handling Redundancy Bias for Benchmark Aggregation, by Damien Berriaud and 1 other authors View PDF Abstract:We are given a set of elements in a metric space. The distribution of the elements is arbitrary, possibly adversarial. Can we weigh the elements in a way that is resistant to such (adversarial) manipulations? This problem arises in various contexts. For instance, the elements could represent data points, requiring robust domain adaptation. Alternatively, they might represent tasks to be aggregated into a benchmark; or questions about personal political opinions in voting advice applications. This article introduces a theoretical framework for dealing with such problems. We propose clone-proof weighting functions as a solution concept. These functions distribute importance across elements of a set such that similar objects (``clones'') share (some of) their weights, thus avoiding a potential bias introduced by their multiplicity. Our framework extends the maximum uncertainty principle to accommodate general metric spaces and includes a set of axioms -- symmetry, continuity, and clone-proofness -- that guide the construction of wei...
