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Result diversification is extensively studied in the context of search, recommendation, and data exploration. There are numerous algorithms that return top-k results that are both diverse and relevant. These algorithms typically have computational loops that compare the pairwise diversity of records to decide which ones to retain. We propose an access primitive DivGetBatch() that replaces repeated pairwise comparisons of diversity scores of records by pairwise comparisons of “aggregate” diversity scores of a group of records, thereby improving the running time of these algorithms while preserving the same results. We integrate the access primitive inside three representative diversity algorithms and prove that the augmented algorithms leveraging the access primitive preserve original results. We analyze the worst and expected case running times of these algorithms. We propose a computational framework to design this access primitive that has a pre-computed index structure I-tree that is agnostic to the specific details of diversity algorithms. We develop principled solutions to construct and maintain I-tree. Our experiments on multiple large real-world datasets corroborate our theoretical findings, while ensuring up to a 24× speedup.
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Quality-Weighted Vendi Scores And Their Application To Diverse Experimental Design
Experimental design techniques such as active search and Bayesian optimization are widely used in the natural sciences for data collection and discovery. However, existing techniques tend to favor exploitation over exploration of the search space, which causes them to get stuck in local optima. This collapse problem prevents experimental design algorithms from yielding diverse high-quality data. In this paper, we extend the Vendi scores—a family of interpretable similarity-based diversity metrics—to account for quality. We then leverage these quality-weighted Vendi scores to tackle experimental design problems across various applications, including drug discovery, materials discovery, and reinforcement learning. We found that quality-weighted Vendi scores allow us to construct policies for experimental design that flexibly balance quality and diversity, and ultimately assemble rich and diverse sets of high-performing data points. Our algorithms led to a 70%–170% increase in the number of effective discoveries compared to baselines.
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- Award ID(s):
- 2118201
- PAR ID:
- 10535334
- Publisher / Repository:
- MLR
- Date Published:
- ISSN:
- 2640-3498
- Format(s):
- Medium: X
- Location:
- International Conference on Machine Learning
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
